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WHO 2016 subtyping and automated
segmentation of glioma using multi-task
deep learning
Sebastian R van der Voort1dagger Fatih Incekara23dagger Maarten MJWijnenga4 Georgios Kapsas2 Renske Gahrmann2 Joost W
Schouten3 Rishi Nandoe Tewarie5 Geert J Lycklama6 Philip CDe Witt Hamer7 Roelant S Eijgelaar7 Pim J French4 HendrikusJ Dubbink8 Arnaud JPE Vincent3 Wiro J Niessen19 Martin J
van den Bent4 Marion Smits2daggerdagger and Stefan Klein1daggerdagger
1Biomedical Imaging Group Rotterdam Department of Radiologyand Nuclear Medicine Erasmus MC University Medical Centre
Rotterdam Rotterdam the Netherlands2Department of Radiology and Nuclear Medicine Erasmus MC
University Medical Centre Rotterdam Rotterdam the Netherlands3Department of Neurosurgery Brain Tumor Center Erasmus MCUniversity Medical Centre Rotterdam Rotterdam the Netherlands
4Department of Neurology Brain Tumor Center Erasmus MCCancer Institute Rotterdam the Netherlands
5Department of Neurosurgery Haaglanden Medical Center theHague the Netherlands
6Department of Radiology Haaglanden Medical Center theHague the Netherlands
8Department of Pathology Brain Tumor Center at Erasmus MCCancer Institute Rotterdam the Netherlands
9Imaging Physics Faculty of Applied Sciences Delft University ofTechnology Delft the Netherlands
7Department of Neurosurgery Cancer Center Amsterdam BrainTumor Center Amsterdam UMC Amsterdam Netherlands
daggerThese authors contributed equallydaggerdaggerThese authors contributed equally
Corresponding author skleinerasmusmcnl
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Abstract
Accurate characterization of glioma is crucial for clinical decision mak-ing A delineation of the tumor is also desirable in the initial decisionstages but is a time-consuming task Leveraging the latest GPU capabil-ities we developed a single multi-task convolutional neural network thatuses the full 3D structural pre-operative MRI scans to can predict theIDH mutation status the 1p19q co-deletion status and the grade of a tu-mor while simultaneously segmenting the tumor We trained our methodusing the largest most diverse patient cohort to date containing 1508glioma patients from 16 institutes We tested our method on an indepen-dent dataset of 240 patients from 13 different institutes and achieved anIDH-AUC of 090 1p19q-AUC of 085 grade-AUC of 081 and a meanwhole tumor DICE score of 084 Thus our method non-invasively pre-dicts multiple clinically relevant parameters and generalizes well to thebroader clinical population
1 Introduction
Glioma is the most common primary brain tumor and is one of the deadliestforms of cancer [1] Differences in survival and treatment response of glioma areattributed to their genetic and histological features specifically the isocitratedehydrogenase (IDH) mutation status the 1p19q co-deletion status and thetumor grade [2 3] Therefore in 2016 the World Health Organization (WHO)updated its brain tumor classification categorizing glioma based on these ge-netic and histological features [4] In current clinical practice these features aredetermined from tumor tissue While this is not an issue in patients in whomthe tumor can be resected this is problematic when resection can not safelybe performed In these instances surgical biopsy is performed with the solepurpose of obtaining tissue for diagnosis which although relatively safe is notwithout risk [5 6] Therefore there has been an increasing interest in comple-mentary non-invasive alternatives that can provide the genetic and histologicalinformation used in the WHO 2016 categorization [7 8]
Magnetic resonance imaging (MRI) has been proposed as a possible candi-date because of its non-invasive nature and its current place in routine clinicalcare [9] Research has shown that certain MRI features such as the tumor het-erogeneity correlate with the genetic and histological features of glioma [10 11]This notion has popularized in addition to already popular applications suchas tumor segmentation the use of machine learning methods for the predictionof genetic and histological features known as radiomics [12 13 14] Althougha plethora of such methods now exist they have found little translation to theclinic [12]
An often discussed challenge for the adoption of machine learning methodsin clinical practice is the lack of standardization resulting in heterogeneity ofpatient populations imaging protocols and scan quality [15 16] Since machinelearning methods are prone to overfitting this heterogeneity questions the va-lidity of such methods in a broader patient population [16] Furthermore it has
2
been noted that most current research concerns narrow task-specific methodsthat lack the context between different related tasks which might restrict theperformance of these methods [17]
An important technical limitation when using deep learning methods is thelimited GPU memory which restricts the size of models that can be trained[18] This is a problem especially for clinical data which is often 3D requiringeven more memory than the commonly used 2D networks This further limitsthe size of these models resulting in shallower models and the use of patches ofa scan instead of using the full 3D scan as an input which limits the amount ofcontext these methods can extract from the scans
Here we present a new method that addresses the above problems Ourmethod consists of a single multi-task convolutional neural network (CNN)that can predict the IDH mutation status the 1p19q co-deletion status andthe grade (grade IIIIIIV) of a tumor while also simultaneously segmenting thetumor see Figure 1 To the best of our knowledge this is the first method thatprovides all of this information at the same time allowing clinical experts to de-rive the WHO category from the individually predicted genetic and histologicalfeatures while also allowing them to consider or disregard specific predictionsas they deem fit Exploiting the capabilities of the latest GPUs optimizing ourimplementation to reduce the memory footprint and using distributed multi-GPU training we were able to train a model that uses the full 3D scan as aninput We trained our method using the largest most diverse patient cohortto date with 1508 patients included from 16 different institutes To ensurethe broad applicability of our method we used minimal inclusion criteria onlyrequiring the four most commonly used MRI sequences pre- and post-contrastT1-weighted (T1w) T2-weighted (T2w) and T2-weighted fluid attenuated in-version recovery (T2w-FLAIR) [19 20] No constraints were placed on thepatientsrsquo clinical characteristics such as the tumor grade or the radiologicalcharacteristics of scans such as the scan quality In this way our method couldcapture the heterogeneity that is naturally present in clinical data We testedour method on an independent dataset of 240 patients from 13 different insti-tutes to evaluate the true generalizability of our method Our results show thatwe can predict multiple clinical features of glioma from MRI scans in a diversepatient population
3
Convolutionalneural network
IDH status
Wildtype Mutated
1p19q status
Intact Co-deleted
Grade
II III IV
WHO 2016categorization
MRI scansPreprocessed
scansSegmentation
Figure 1 Overview of our method Pre- and post-contrast T1w T2w and T2w-FLAIR scans are used as an input The scans are registered to an atlas biasfield corrected skull stripped and normalized before being passed through ourconvolutional neural network One branch of the network segments the tumorwhile at the same time the features are combined to predict the IDH status1p19q status and grade of the tumor
4
2 Results
21 Patient characteristics
We included a total of 1748 patients in our study 1508 as a train set and240 as an independent test set The patients in the train set originated fromnine different data collections and 16 different institutes and the test set wascollected from two different data collections and 13 different institutes Table 1provides a full overview of the patient characteristics in the train and test setand Figure 2 shows the inclusion flowchart and the distribution of the patientsover the different data collections in the train set and test set
Table 1 Patient characteristics for the train set and test set
Train set Test setN N
Patients 1508 240IDH status
Mutated 226 150 88 367Wildtype 440 292 129 537Unknown 842 558 23 96
1p19q co-deletion statusCo-deleted 103 68 26 108Intact 337 224 207 863Unknown 1068 708 7 29
GradeII 230 153 47 196III 114 76 59 246IV 830 550 132 550Unknown 334 221 2 08
WHO 2016 categorizationOligodendroglioma 96 64 26 108Astrocytoma IDH wildtype 31 21 22 92Astrocytoma IDH mutated 98 64 57 237GBM IDH wildtype 331 219 106 442GBM IDH mutated 16 11 5 21Unknown 936 621 24 100
SegmentationManual 716 475 240 100Automatic 792 525 0 0
IDH isocitrate dehydrogenase WHO World Health Organization GBMglioblastoma
5
Patient screening
Train set2181 Glioma patients
1241 Erasmus MC491 Haaglanden Medical Center168 BraTS130 REMBRANDT66 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set461 Glioma patients
199 TCGA-LGG262 TCGA-GBM
Patient inclusion
Train set1508 Patients in train set
816 Erasmus MC279 Haaglanden Medical Center168 BraTS109 REMBRANDT51 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set240 Patients in test set
107 TCGA-LGG133 TCGA-GBM
Patient exclusion
Train set673 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
425 Erasmus MC212 Haaglanden Medical Center
0 BraTS21 REMBRANDT15 CPTAC-GBM0 Ivy GAP0 Amsterdam UMC0 Brain-Tumor-Progression0 University Medical Center Utrecht
Test set221 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
92 TCGA-LGG129 TCGA-GBM
Figure 2 Inclusion flowchart of the train set and test set
6
22 Algorithm performance
We used 15 of the train set as a validation set and selected the model pa-rameters that achieved the best performance on this validation set where themodel achieved an area under receiver operating characteristic curve (AUC) of088 for the IDH mutation status prediction an AUC of 076 for the 1p19qco-deletion prediction an AUC of 075 for the grade prediction and a meansegmentation DICE score of 081 The selected model parameters are shown inAppendix F We then trained a model using these parameters and the full trainset and evaluated it on the independent test set
For the genetic and histological feature predictions we achieved an AUC of090 for the IDH mutation status prediction an AUC of 085 for the 1p19qco-deletion prediction and an AUC of 081 for the grade prediction in thetest set The full results are shown in Table 2 with the corresponding receiveroperating characteristic (ROC)-curves in Figure 3 Table 2 also shows the resultsin (clinically relevant) subgroups of patients This shows that we achieved anIDH-AUC of 081 in low grade glioma (LGG) (grade IIIII) an IDH-AUC of064 in high grade glioma (HGG) (grade IV) and a 1p19q-AUC of 073 in LGGWhen only predicting LGG vs HGG instead of predicting the individual gradeswe achieved an AUC of 091 In Appendix A we provide confusion matrices forthe IDH 1p19q and grade predictions as well as a confusion matrix for thefinal WHO 2016 subtype which shows that only one patient was predicted asa non-existing WHO 2016 subtype In Appendix C we provide the individualpredictions and ground truth labels for all patients in the test set to allow forthe calculation of additional metrics
For the automatic segmentation we achieved a mean DICE score of 084 amean Hausdorff distance of 189 mm and a mean volumetric similarity coeffi-cient of 090 Figure 4 shows boxplots of the DICE scores Hausdorff distancesand volumetric similarity coefficients for the different patients in the test set InAppendix B we show five patients that were randomly selected from both theTCGA-LGG and TCGA-GBM data collections to demonstrate the automaticsegmentations made by our method
23 Model interpretability
To provide insight into the behavior of our model we created saliency mapswhich show which parts of the scans contributed the most to the predictionThese saliency maps are shown in Figure 5 for two example patients from thetest set It can be seen that for the LGG the network focused on a bright rim inthe T2w-FLAIR scan whereas for the HGG it focused on the enhancement in thepost-contrast T1w scan To aid further interpretation we provide visualizationsof selected filter outputs in the network in Appendix D which also show thatthe network focuses on the tumor and these filters seem to recognize specificimaging features such as the contrast enhancement and T2w-FLAIR brightness
7
Table 2 Evaluation results of the final model on the test set
Patientgroup
Task AUC Accuracy Sensitivity Specificity
All IDH 090 084 072 0931p19q 085 089 039 095Grade (IIIIIIV) 081 071 NA NAGrade II 091 086 075 089Grade III 069 075 017 094Grade IV 091 082 095 066LGG vs HGG 091 084 072 093
LGG IDH 081 074 073 0771p19q 073 076 039 089
HGG IDH 064 094 040 096
Abbreviations AUC area under receiver operating characteristic curve IDHisocitrate dehydrogenase LGG low grade glioma HGG high grade glioma
Figure 3 Receiver operating characteristic (ROC)-curves of the genetic andhistological features evaluated on the test set The crosses indicate the locationof the decision threshold for the reported accuracy sensitivity and specificity
8
Figure 4 DICE scores Hausdorff distances and volumetric similarity coeffi-cients for all patients in the test set The DICE score is a measure of theoverlap between the ground truth and predicted segmentation (where 1 indi-cates perfect overlap) The Hausdorff distance is a measure of the agreementbetween the boundaries of the ground truth and predicted segmentation (loweris better) The volumetric similarity coefficient is a measure of the agreementin volume (where 1 indicates perfect agreement)
9
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Saliency maps of a low grade glioma patient (TCGA-DU-6400) This is an IDH mu-tated 1p19q co-deleted grade II tumor The network focuses on a rim of brightnessin the T2w-FLAIR scan
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) Saliency maps of a high grade glioma patient (TCGA-06-0238) This is an IDHwildtype grade IV tumor The network focuses on enhancing spots around the necrosison the post-contrast T1w scan
Figure 5 Saliency maps of two patients from the test set showing areas thatare relevant for the prediction
10
24 Model robustness
By not excluding scans from our train set based on radiological characteristicswe were able to make our model robust to low scan quality as can be seen in anexample from the test set in Figure 6 Even though this example scan containedimaging artifacts our method was able to properly segment the tumor (DICEscore of 087) and correctly predict the tumor as an IDH wildtype grade IVtumor
Figure 6 Example of a T2w-FLAIR scan containing imaging artifacts and theautomatic segmentation (red overlay) made by our method It was correctlypredicted as an IDH wildtype grade IV glioma This is patient TCGA-06-5408from the TCGA-GBM collection
Finally we considered two examples of scans that were incorrectly predictedby our method see Figure 7 These two examples were chosen because ournetwork assigned high prediction scores to the wrong classes for these casesFigure 7a shows an example of a grade II IDH mutated 1p19q co-deletedglioma that was predicted as grade IV IDH wildtype by our method Ourmethodrsquos prediction was most likely caused by the hyperintensities in the post-contrast T1w scan being interpreted as contrast enhancement Since these hy-perintensities are also present in the pre-contrast T1w scan they are most likelycalcifications and the radiological appearance of this tumor is indicative of anoligodendroglioma Figure 7b shows an example of a grade IV IDH wildtypeglioma that was predicted as a grade III IDH mutated glioma by our method
11
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) TCGA-DU-6410 from the TCGA-LGG collection The ground truth histopatho-logical analysis indicated this glioma was grade II IDH mutated 1p19q co-deletedbut our method predicted it as a grade IV IDH wildtype
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) TCGA-76-7664 from the TCGA-HGG collection Histopathologically this gliomawas grade IV IDH wildtype but our method predicted it as grade III IDH mutated
Figure 7 Examples of scans that were incorrectly predicted by our method
12
3 Discussion
We have developed a method that can predict the IDH mutation status 1p19qco-deletion status and grade of glioma while simultaneously providing the tu-mor segmentation based on pre-operative MRI scans For the genetic andhistological feature predictions we achieved an AUC of 090 for the IDH mu-tation status prediction an AUC of 085 for the 1p19q co-deletion predictionand an AUC of 081 for the grade prediction in the test set
In an independent test set which contained data from 13 different instituteswe demonstrated that our method predicts these features with good overall per-formance we achieved an AUC of 090 for the IDH mutation status predictionan AUC of 085 for the 1p19q co-deletion prediction and an AUC of 081 forthe grade prediction and a mean whole tumor DICE score of 084 This per-formance on unseen data that was only used during the final evaluation of thealgorithm and that was purposefully not used to guide any decisions regardingthe method design shows the true generalizability of our method Using thelatest GPU capabilities we were able to train a large model which uses thefull 3D scan as input Furthermore by using the largest most diverse patientcohort to date we were able to make our method robust to the heterogeneitythat is naturally present in clinical imaging data such that it generalizes forbroad application in clinical practice
By using a multi-task network our method could learn the context betweendifferent features For example IDH wildtype and 1p19q co-deletion are mu-tually exclusive [21] If two separate methods had been used one to predict theIDH status and one to predict the 1p19q co-deletion status an IDH wildtypeglioma might be predicted to be 1p19q co-deleted which does not stroke withthe clinical reality Since our method learns both of these genetic features si-multaneously it correctly learned not to predict 1p19q co-deletion in tumorsthat were IDH wildtype there was only one patient in which our algorithm pre-dicted a tumor to be both IDH wildtype and 1p19q co-deleted Furthermoreby predicting the genetic and histological features individually instead of onlypredicting the WHO 2016 category it is possible to adopt updated guidelinessuch as cIMPACT-NOW future-proofing our method [22]
Some previous studies also used multi-task networks to predict the geneticand histological features of glioma [23 24 25] Tang et al [23] used a multi-tasknetwork that predicts multiple genetic features as well as the overall survivalof glioblastoma Since their method only works for glioblastoma patients thetumor grade must be known in advance complicating the use of their methodin the pre-operative setting when tumor grade is not yet known Furthermoretheir method requires a tumor segmentation prior to application of their methodwhich is a time-consuming expert task In a study by Xue et al [24] a multi-task network was used with a structure similar to the one proposed in thispaper to segment the tumor and predict the grade (LGG or HGG) and IDHmutation status However they do not predict the 1p19q co-deletion statusneeded for the WHO 2016 categorization Lastly Decuyper et al [25] useda multi-task network that predicts the IDH mutation and 1p19q co-deletion
13
status and the tumor grade (LGG or HGG) Their method requires a tumorsegmentation as input which they obtain from a U-Net that is applied earlierin their pipeline thus their method requires two networks instead of the singlenetwork we use in our method These differences aside the most importantlimitation of each of these studies is the lack of an independent test set forevaluating their results It is now considered essential that an independent testset is used to prevent an overly optimistic estimate of a methodrsquos performance[15 26 27 28] Thus our study improves on this previous work by providinga single network that combines the different tasks being trained on a moreextensive and diverse dataset not requiring a tumor segmentation as an in-put providing all information needed for the WHO 2016 categorization andcrucially by being evaluated in an independent test set
An important genetic feature that is not predicted by our method is theO6-methylguanine-methyltransferase (MGMT) methylation status Althoughthe MGMT methylation status is not part of the WHO 2016 categorizationit is part of clinical management guidelines and is an important prognosticmarker in glioblastoma [4] In the initial stages of this study we attemptedto predict the MGMT methylation status however the performance of thisprediction was poor Furthermore the methylation cutoff level which is usedto determine whether a tumor is MGMT methylated shows a wide varietybetween institutes leading to inconsistent results [29] We therefore opted not toinclude the MGMT prediction at all rather than to provide a poor prediction ofan unsharply defined parameter Although some methods attempted to predictthe MGMT status with varying degrees of success there is still an ongoingdiscussion on the validity of MR imaging features of the MGMT status [23 3031 32 33]
Our method shows good overall performance but there are noticeable per-formance differences between tumor categories For example when our methodpredicts a tumor as an IDH wildtype glioblastoma it is correct almost all of thetime On the other hand it has some difficulty differentiating IDH mutated1p19q co-deleted low-grade glioma from other low-grade glioma The sensitiv-ity for the prediction of grade III glioma was low which might be caused bythe lack of a central pathology review Because of this there were differences inmolecular testing and histological analysis and it is known that distinguishingbetween grade II and grade III has a poor observer reliability [34] Althoughour method can be relevant for certain subgroups our methodrsquos performancestill needs to be improved to ensure relevancy for the full patient population
In future work we aim to increase the performance of our method by includ-ing perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI)since there has been an increasing amount of evidence that these physiologi-cal imaging modalities contain additional information that correlates with thetumorrsquos genetic status and aggressiveness [35 36] They were not included inthis study since PWI and to a lesser extent DWI are not as ingrained in theclinical imaging routine as the structural scans used in this work [19 20] Thusincluding these modalities would limit our methodrsquos clinical applicability andsubstantially reduce the number of patients in the train and test set However
14
PWI and DWI are increasingly becoming more commonplace which will allowincluding these in future research and which might improve performance
In conclusion we have developed a non-invasive method that can predict theIDH mutation status 1p19q co-deletion status and grade of glioma while atthe same time segmenting the tumor based on pre-operative MRI scans withhigh overall performance Although the performance of our method might needto be improved before it will find widespread clinical acceptance we believe thatthis research is an important step forward in the field of radiomics Predict-ing multiple clinical features simultaneously steps away from the conventionalsingle-task methods and is more in line with the clinical practice where multipleclinical features are considered simultaneously and may even be related Fur-thermore by not limiting the patient population used to develop our method toa selection based on clinical or radiological characteristics we alleviate the needfor a priori (expert) knowledge which may not always be available Althoughsteps still have to be taken before radiomics will find its way into the clinicespecially in terms of performance our work provides a crucial step forward byresolving some of the hurdles of clinical implementation now and paving theway for a full transition in the future
4 Methods
41 Patient population
The train set was collected from four in-house datasets and five publicly avail-able datasets In-house datasets were collected from four different institutesErasmus MC (EMC) Haaglanden Medical Center (HMC) Amsterdam UMC(AUMC) [37] and University Medical Center Utrecht (UMCU) Four of the fivepublic datasets were collected from The Cancer Imaging Archive (TCIA) [38]the Repository of Molecular Brain Neoplasia Data (REMBRANDT) collection[39] the Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multi-forme (CPTAC-GBM) collection [40] the Ivy Glioblastoma Atlas Project (IvyGAP) collection [41 42] and the Brain-Tumor-Progression collection [43] Thefifth dataset was the 2019 Brain Tumor Segmentation challenge (BraTS) chal-lenge dataset [44 45 46] from which we excluded the patients that were alsoavailable in the TCGA-LGG and TCGA-GBM collections [47 48]
For the internal datasets from the EMC and the HMC manual segmentationswere available which were made by four different clinical experts For patientswhere segmentations from more than one observer were available we randomlypicked one of the segmentations to use in the train set The segmentationsfrom the AUMC data were made by a single observer of the study by Visseret al [37] From the public datasets only the BraTS dataset and the Brain-Tumor-Progression dataset provided manual segmentations Segmentations ofthe BraTS dataset as provided in the 2019 training and validation set wereused For the Brain-Tumor-Progression dataset the segmentations as providedin the TCIA data collection were used
15
Patients were included if pre-operative pre- and post-contrast T1w T2wand T2w-FLAIR scans were available no further inclusion criteria were setFor example patients were not excluded based on the radiological characteristicsof the scan such as low imaging quality or imaging artifacts or the gliomarsquosclinical characteristics such as the grade If multiple scans of the same contrasttype were available in a single scan session (eg multiple T2w scans) the scanupon which the segmentation was made was selected If no segmentation wasavailable or the segmentation was not made based on that scan contrast thescan with the highest axial resolution was used where a 3D acquisition waspreferred over a 2D acquisition
For the in-house data genetic and histological data were available for theEMC HMC and UMCU dataset which were obtained from analysis of tumortissue after biopsy or resection Genetic and histological data of the publicdatasets were also available for the REMBRANDT CPTAC-GBM and IvyGAP collections Data for the REMBRANDT and CPTAC-GBM collectionswas collected from the clinical data available at the TCIA [40 39] For theIvy GAP collection the genetic and histological data were obtained from theSwedish Institute at httpsivygapswedishorghome
As a test set we used the TCGA-LGG and TCGA-GBM collections from theTCIA [47 48] Genetic and histological labels were obtained from the clinicaldata available at the TCIA Segmentations were used as available from theTCIA based on the 2018 BraTS challenge [45 49 50] The inclusion criteriafor the patients included in the BraTS challenge were the same as our inclusioncriteria the presence of a pre-operative pre- and post-contrast T1w T2w andT2w-FLAIR scan Thus patients from the TCGA-LGG and TCGA-GBM wereincluded if a segmentation from the BraTS challenge was available Howeverfor three patients we found that although they did have manual segmentationsthey did not meet our inclusion requirements TCGA-08-0509 and TCGA-08-0510 from TCGA-GBM because they did not have a pre-contrast T1w scan andTCGA-FG-7634 from TCGA-LGG because there was no post-contrast T1wscan
42 Automatic segmentation in the train set
To present our method with a large diversity in scans we wanted to include asmany patients in the train set as possible from the different datasets Thereforewe performed automatic segmentation in patients that did not have manualsegmentations To this end we used an initial version of our network (presentedin Section 44) without the additional layers that were needed for the predictionof the genetic and histological features This network was initially trained usingall patients in the train set for whom a manual segmentation was availableand this trained network was then applied to all patients for which a manualsegmentation was not available The resulting automatic segmentations wereinspected and if their quality was acceptable they were added to the trainset The network was then trained again using this increased dataset and wasapplied to scans that did not yet have a segmentation of acceptable quality
16
This process was repeated until an acceptable segmentation was available forall patients which constituted our final complete train set
43 Pre-processing
For all datasets except for the BraTS dataset for which the scans were alreadyprovided in NIfTI format the scans were converted from DICOM format toNIfTI format using dcm2niix version v1020190410 [51] We then registeredall scans to the MNI152 T1w and T2w atlases version ICBM 2009a whichhad a resolution of 1x1x1 mm3 and a size of 197x233x189 voxels [52 53] Thescans were affinely registered using Elastix 50 [54 55] The pre- and post-contrast T1w scans were registered to the T1w atlas the T2w and T2w-FLAIRscans were registered to the T2w atlas When a manual segmentation wasavailable for patients from the in-house datasets the registration parametersthat resulted from registering the scan used during the segmentation were usedto transform the segmentation to the atlas In the case of the public datasetswe used the registration parameters of the T2w-FLAIR scans to transform thesegmentations
After the registration all scans were N4 bias field corrected using SimpleITKversion 124 [56] A brain mask was made for the atlas using HD-BET both forthe T1w atlas and the T2w atlas [57] This brain mask was used to skull stripall registered scans and crop them to a bounding box around the brain maskreducing the amount of background present in the scans resulting in a scan sizeof 152x182x145 voxels Subsequently the scans were normalized such that foreach scan the average image intensity was 0 and the standard deviation of theimage intensity was 1 within the brain mask Finally the background outsidethe brain mask was set to the minimum intensity value within the brain mask
Since the segmentation could sometimes be rugged at the edges after regis-tration especially when the segmentations were initially made on low-resolutionscans we smoothed the segmentation using a 3x3x3 median filter (this was onlydone in the train set) For segmentations that contained more than one la-bel eg when the tumor necrosis and enhancement were separately segmentedall labels were collapsed into a single label to obtain a single segmentation ofthe whole tumor The genetic and histological labels and the segmentations ofeach patient were one-hot encoded The four scans ground truth labels andsegmentation of each patient were then used as the input to the network
44 Model
We based the architecture of our model on the U-Net architecture with someadaptations made to allow for a full 3D input and the auxiliary tasks [58] Ournetwork architecture which we have named PrognosAIs Structure-Net or PS-Net for short can be seen in Figure 8
To use the full 3D scan as an input to the network we replaced the firstpooling layer that is usually present in the U-Net with a strided convolutionwith a kernel size of 9x9x9 and a stride of 3x3x3 In the upsampling branch of
17
32
32 64
128
256
512 256
7x8x7256 128
128 64
64 32
32 2
Segmentation
145x182x152
49x61x51
25x31x26
13x16x13
1472
512 2IDH
512 2
1p19q
512 3Grade
Batch normalization Concatenation Convolution amp ReLU3x3x3
Convolution amp Softmax1x1x1
(De)convolution amp ReLU9x9x9
stride 3x3x3
Dense amp ReLU Dense amp Softmax Dropout
Max pooling2x2x2
Up-convolution amp ReLU2x2x2
Global maxpooling
Figure 8 Overview of the PrognosAIs Structure-Net (PS-Net) architecture usedfor our model The numbers below the different layers indicate the number offilters dense units or features at that layer We have also indicated the featuremap size at the different depths of the network
the network the last up-convolution is replaced by a deconvolution with thesame kernel size and stride
At each depth of the network we have added global max-pooling layersdirectly after the dropout layer to obtain imaging features that can be used topredict the genetic and histological features We chose global pooling layers asthey do not introduce any additional parameters that need to be trained thuskeeping the memory required by our model manageable The features from thedifferent depths of the network were concatenated and fed into three differentdense layers one for each of the genetic and histological outputs
l2 kernel regularization was used in all convolutional layers except for thelast convolutional layer used for the output of the segmentation In total thismodel contained 27042473 trainable an 2944 non-trainable parameters
18
45 Model training
Training of the model was done on eight NVidia RTX2080Tirsquos with 11GB ofmemory using TensorFlow 220 [59] To be able to use the full 3D scan asinput to the network without running into memory issues we had to optimizethe memory efficiency of the model training Most importantly we used mixed-precision training which means that most of the variables of the model (such asthe weights) were stored in float16 which requires half the memory of float32which is typically used to store these variables [60] Only the last softmaxactivation layers of each output were stored as float32 We also stored ourpre-processed scans as float16 to further reduce memory usage
However even with these settings we could not use a batch size larger than1 It is known that a larger batch size is preferable as it increases the stabilityof the gradient updates and allows for a better estimation of the normalizationparameters in batch normalization layers [61] Therefore we distributed thetraining over the eight GPUs using the NCCL AllReduce algorithm whichcombines the gradients calculated on each GPU before calculating the updateto the model parameters [62] We also used synchronized batch normalizationlayers which synchronize the updates of their parameters over the distributedmodels In this way our model had a virtual batch size of eight for the gradientupdates and the batch normalization layers parameters
To provide more samples to the algorithm and prevent potential overtrain-ing we applied four types of data augmentation during training croppingrotation brightness shifts and contrast shifts Each augmentation was appliedwith a certain augmentation probability which determined the probability ofthat augmentation type being applied to a specific sample When an image wascropped a random number of voxels between 0 and 20 was cropped from eachdimension and filled with zeros For the random rotation an angle betweenminus30 and 30 degrees was selected from a uniform distribution for each dimen-sion The brightness shift was applied with a delta uniformly drawn between0 and 02 and the contrast shift factor was randomly drawn between 085 and115 We also introduced an augmentation factor which determines how ofteneach sample was parsed as an input sample during a single epoch where eachtime it could be augmented differently
For the IDH 1p19q and grade output we used a masked categorical cross-entropy loss and for the segmentation we used a DICE loss see Appendix E fordetails We used AdamW as an optimizer which has shown improved general-ization performance over Adam by introducing the weight decay parameter as aseparate parameter from the learning rate [63] The learning rate was automat-ically reduced by a factor of 025 if the loss did not improve during the last fiveepochs with a minimum learning rate of 1 middot 10minus11 The model could train for amaximum of 150 epochs and training was stopped early if the average loss overthe last five epochs did not improve Once the model was finished training theweights from the epoch with the lowest loss were restored
19
46 Hyperparameter tuning
Hyperparameters involved in the training of the model needed to be tuned toachieve the best performance We tuned a total of six hyper parameters the l2-norm the dropout rate the augmentation factor the augmentation probabilitythe optimizerrsquos initial learning rate and the optimizerrsquos weight decay A fulloverview of the trained parameters and the values tested for the different settingsis presented in Appendix F
To tune these hyperparameters we split the train set into a hyperparametertraining set (851282 patients of the full train data) and a hyperparametervalidation set (15226 patients of the full train data) Models were trained fordifferent hyperparameter settings via an exhaustive search using the hyperpa-rameter train set and then evaluated on the hyperparameter validation set Nodata augmentation was applied to the hyperparameter validation to ensure thatresults between trained models were comparable The hyperparameters that ledto the lowest overall loss in the hyperparameter validation set were chosen asthe optimal hyperparameters We trained the final model using these optimalhyperparameters and the full train set
47 Post-processing
The predictions of the network were post-processed to obtain the final predictedlabels and segmentations for the samples Since softmax activations were usedfor the genetic and histological outputs a prediction between 0 and 1 was out-putted for each class where the individual predictions summed to 1 The finalpredicted label was then considered as the class with the highest prediction scoreFor the prediction of LGG (grade IIIII) vs HGG (grade IV) the predictionscores of grade II and grade III were combined to obtain the prediction scorefor LGG the prediction score of grade IV was used as the prediction score forHGG If a segmentation contained multiple unconnected components we onlyretained the largest component to obtain a single whole tumor segmentation
48 Model evaluation
The performance of the final trained model was evaluated on the independenttest set comparing the predicted labels with the ground truth labels For thegenetic and histological features we evaluated the AUC the accuracy the sen-sitivity and the specificity using scikit-learn version 0231 for details see Ap-pendix G [64] We evaluated these metrics on the full test set and in subcate-gories relevant to the WHO 2016 guidelines We evaluated the IDH performanceseparately in the LGG (grade IIIII) and HGG (grade IV) subgroups the 1p19qperformance in LGG and we also evaluated the performance of distinguishingbetween LGG and HGG instead of predicting the individual grades
To evaluate the performance of the segmentation we calculated the DICEscores Hausdorff distances and volumetric similarity coefficient comparing theautomatic segmentation of our method and the manual ground truth segmen-
20
tations for all patients in the test set These metrics were calculated usingthe EvaluateSegmentation toolbox version 20170425 [65] for details see Ap-pendix G
To prevent an overly optimistic estimation of our modelrsquos predictive valuewe only evaluated our model on the test set once all hyperparameters werechosen and the final model was trained In this way the performance in thetest set did not influence decisions made during the development of the modelpreventing possible overfitting by fine-tuning to the test set
To gain insight into the model we made saliency maps that show whichparts of the scan contribute the most to the prediction of the CNN [66] Saliencymaps were made using tf-keras-vis 052 changing the activation function of alloutput layers from softmax to linear activations using SmoothGrad to reducethe noisiness of the saliency maps [66]
Another way to gain insight into the networkrsquos behavior is to visualize thefilter outputs of the convolutional layers as they can give some idea as to whatoperations the network applies to the scans We visualized the filter outputs ofthe last convolutional layers in the downsample and upsample path at the firstdepth (at an image size of 49x61x51) of our network These filter outputs werevisualized by passing a sample through the network and showing the convolu-tional layersrsquo outputs replacing the ReLU activation with linear activations
49 Data availability
An overview of the patients included from the public datasets used in the train-ing and testing of the algorithm and their ground truth label is available inAppendix H The data from the public datasets are available in TCIA un-der DOIs 107937K9TCIA2015588OZUZB 107937k9tcia20183rje41q1107937K9TCIA2016XLwaN6nL and 107937K9TCIA201815quzvnb Datafrom the BraTS are available at httpbraintumorsegmentationorg Datafrom the in-house datasets are not publicly available due to participant privacyand consent
410 Code availability
The code used in this paper is available on GitHub under an Apache 2 license athttpsgithubcomSvdvoortPrognosAIs_glioma This code includes thefull pipeline from registration of the patients to the final post-processing of thepredictions The trained model is also available on GitHub along with code toapply it to new patients
21
Appendices
A Confusion matrices
Tables 3 4 and 5 show the confusion matrices for the IDH 1p19q and gradepredictions and Table 6 shows the confusion matrix for the WHO 2016 subtypes
Table 4 shows that the algorithm mainly has difficulty recognizing 1p19qco-deleted tumors which are mostly predicted as 1p19q intact Table 5 showsthat most of the incorrectly predicted grade III tumors are predicted as gradeIV tumors
Table 6 shows that our algorithm often incorrectly predicts IDH-wildtypeastrocytoma as IDH-wildtype glioblastoma The latest cIMPACT-NOW guide-lines propose a new categorization in which IDH-wildtype astrocytoma thatshow either TERT promoter methylation or EFGR gene amplification or chro-mosome 7 gainchromsome 10 loss are classified as IDH-wildtype glioblastoma[22] This new categorization is proposed since the survival of patients withthose IDH-wildtype astrocytoma is similar to the survival of patients with IDH-wildtype glioblastoma [22] From the 13 IDH-wildtype astrocytoma that werewrongly predicted as IDH-wildtype glioblastoma 12 would actually be cate-gorized as IDH-wildtype glioblastoma under this new categorization Thusalthough our method wrongly predicted the WHO 2016 subtype it might ac-tually have picked up on imaging features related to the aggressiveness of thetumor which might lead to a better categorization
Table 3 Confusion matrix of the IDH predictions
Predicted
Wildtype Mutated
Actu
al
Wildtype 120 9
Mutated 25 63
Table 4 Confusion matrix of the 1p19q predictions
Predicted
Intact Co-deleted
Actu
al
Intact 197 10
Co-deleted 16 10
22
Table 5 Confusion matrix of the grade predictions
Predicted
Grade II Grade III Grade IV
Actu
al Grade II 35 6 6
Grade III 19 10 30
Grade IV 2 5 125
Table 6 Confusion matrix of the WHO 2016 predictions The rsquootherrsquo categoryindicates patients that were predicted as a non-existing WHO 2016 subtype forexample IDH wildtype 1p19q co-deleted tumors Only one patient (TCGA-HT-A5RC) was predicted as a non-existing category It was predicted as anIDH wildtype 1p19q co-deleted grade IV tumor
Predicted
Oligodendrogliom
a
IDH-m
utated
astrocytoma
IDH-w
ildtype
astrocytoma
IDH-m
utated
glioblastoma
IDH-w
ildtype
glioblastoma
Other
Actu
al
Oligodendroglioma 10 8 1 0 7 0
IDH-mutatedastrocytoma 6 34 4 3 10 0
IDH-wildtypeastrocytoma 1 2 3 2 13 1
IDH-mutatedglioblastoma 0 1 0 0 3 0
IDH-wildtypeglioblastoma 0 3 3 1 96 0
Oligodendroglioma are IDH-mutated 1p19q co-deleted grade IIIII giomaIDH-mutated astrocytoma are IDH-mutated 1p19q intact grade IIIII gliomaIDH-wildtype astrocytoma are IDH-wildtype 1p19q intact grade IIIII gliomaIDH-mutated glioblastoma are IDH-mutated grade IV gliomaIDH-wildtype glioblastoma are IDH-wildtype grade IV glioma
23
B Segmentation examples
To demonstrate the automatic segmentations made by our method we randomlyselected five patients from both the TCGA-LGG and the TCGA-GBM datasetThe scans and segmentations of the five patients from the TCGA-LGG datasetand the TCGA-GBM dataset are shown in Figures 9 and 10 respectively TheDICE score Hausdorff distance and volumetric similarity coefficient for thesepatients are given in Table 7 The method seems to mostly focus on the hyper-intensities of the T2w-FLAIR scan Despite the registrations issues that can beseen for the T2w scan in Figure 10d the tumor was still properly segmenteddemonstrating the robustness of our method
Patient DICE HD (mm) VSC
TCGA-LGG
TCGA-DU-7301 089 103 095TCGA-FG-5964 080 58 082TCGA-FG-A713 073 78 088TCGA-HT-7475 087 149 090TCGA-HT-8106 088 112 099
TCGA-GBM
TCGA-02-0037 082 226 099TCGA-08-0353 091 130 098TCGA-12-1094 090 73 093TCGA-14-3477 090 165 099TCGA-19-5951 073 197 073
Table 7 The DICE score Hausdorff distance (HD) and volumetric similaritycoefficient (VSC) for the randomly selected patients from the TCGA-LGG andTCGA-GBM data collections
24
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-DU-7301 from the TCGA-LGG data collection
(b) Patient TCGA-FG-5964 from the TCGA-LGG data collection
(c) Patient TCGA-FG-A713 from the TCGA-LGG data collection
(d) Patient TCGA-HT-7475 from the TCGA-LGG data collection
(e) Patient TCGA-HT-8106 from the TCGA-LGG data collection
Figure 9 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-LGG data collection
25
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-02-0037 from the TCGA-GBM data collection
(b) Patient TCGA-08-0353 from the TCGA-GBM data collection
(c) Patient TCGA-12-1094 from the TCGA-GBM data collection
(d) Patient TCGA-14-3477 from the TCGA-GBM data collection
(e) Patient TCGA-19-5951 from the TCGA-GBM data collection
Figure 10 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-GBM data collection
26
C Prediction results in the test set
27
D Filter output visualizations
Figures 11 and 12 show the output of the convolution filters for the same LGGpatient as shown in Figure 5a and Figures 13 and 14 show the output of theconvolution filters for the same HGG patient as shown in Figure 5b Figures 11and 13 show the outputs of the last convolution layer in the downsample pathat the feature size of 49x61x51 (the fourth convolutional layer in the network)Figures 12 and 14 show the outputs of the last convolution layer in the upsamplepath at the feature size of 49x61x51 (the nineteenth convolutional layer in thenetwork)
Comparing Figure 11 to Figure 12 and Figure 13 to Figure 14 we can seethat the convolutional layers in the upsample path do not keep a lot of detailfor the healthy part of the brain as this region seems blurred However withinthe tumor different regions can still be distinguished The different parts ofthe tumor from the scans can also be seen such as the contrast-enhancing partand the high signal intensity on the T2w-FLAIR For the grade IV glioma inFigure 14 some filters such as filter 26 also seem to focus on the necrotic partof the tumor
28
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 11 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
29
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 12 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
30
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 13 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-06-0238 This is an IDH wildtype grade IV glioma
31
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 14 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-06-0238 This is an IDH wildtype grade IV glioma
32
E Training losses
During the training of the network we used masked categorical cross-entropy lossfor the IDH 1p19q and grade outputs The normal categorical cross-entropyloss is defined as
LCEbatch = minus 1
Nbatch
sumj
sumiisinC
yij log (yij) (1)
where LCEbatch is the total cross-entropy loss over a batch yij is the ground truth
label of sample j for class i yij is the prediction score for sample j for class iC is the set of classes and Nbatch is the number of samples in the batch Here itis assumed that the ground truth labels are one-hot-encoded thus yij is either0 or 1 for each class In our case the ground truth is not known for all sampleswhich can be incorporated in Equation (1) by setting yij to 0 for all classesfor a sample for which the ground truth is not known That sample wouldthen not contribute to the overall loss and would not contribute to the gradientupdate However this can skew the total loss over a batch since the loss is stillaveraged over the total number of samples in a batch regardless of whether theground truth is known resulting in a lower loss for batches that contained moresamples with unknown ground truth Therefore we used a masked categoricalcross-entropy loss
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
yij log (yij) (2)
where
microbatchj =
Nbatchsumij yij
sumi
yij (3)
is the batch weight for sample j In this way the total batch loss is only averagedover the samples that actually have a ground truth
Since there was an imbalance between the number of ground truth samplesfor each class we used class weights to compensate for this imbalance Thusthe loss becomes
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
microclassi yij log (yij) (4)
where
microclassi =
N
Ni |C|(5)
is the class weight for class i N is the total number of samples with knownground truth Ni is the number of samples of class i and |C| is the number ofclasses By determining the class weight in this way we ensured that
microclassi Ni =
N
|C|= constant (6)
33
Thus each class would have the same contribution to the overall loss Theseclass weights were (individually) determined for the IDH output the 1p19qoutput and the grade output
For the segmentation output we used the DICE loss
LDICEbatch =
sumj
1minus 2 middotsumvoxels
k yjk middot yjksumvoxelsk yjk + yjk
(7)
where yjk is the ground truth label in voxel k of sample j and yjk is theprediction score outputted for voxel k of sample j
The total loss that was optimized for the model was a weighted sum of thefour individual losses
Ltotal =summ
micromLm (8)
with
microm =1
Xm (9)
where Lm is the loss for output m microm is the loss weight for loss m (either theIDH 1p19q grade or segmentation loss) and Xm is the number of sampleswith known ground truth for output m In this way we could counteract theeffect of certain outputs having more known labels than other outputs
34
F Parameter tuning
Table 8 Hyperparameters that were tuned and the values that were testedValues in bold show the selected values used in the final model
Tuning parameter Tested values
Dropout rate 015 02 025 030 035 040l2-norm 00001 000001 0000001Learning rate 001 0001 00001 000001 00000001Weight decay 0001 00001 000001Augmentation factor 1 2 3Augmentation probability 025 030 035 040 045
35
G Evaluation metrics
We calculated the AUC accuracy sensitivity and specificity metrics for thegenetic and histological features for the definitions of these metrics see [67]
For the IDH and 1p19q co-deletion outputs the IDH mutated and the1p19q co-deleted samples were regarded as the positive class respectively Sincethe grade was a multi-class problem no single positive class could be determinedFor the prediction of the individual grades that grade was seen as the positiveclass and all other grades as the negative class (eg in the case of the gradeIII prediction grade III was regarded as the positive class and grade II and IVwere regarded as the negative class) For the LGG vs HGG prediction LGGwas considered as the positive class and HGG as the negative class For theevaluation of these metrics for the genetic and histological features only thesubjects with known ground truth were taken into account
The overall AUC for the grade was a multi-class AUC determined in a one-vs-one approach comparing each class against the others in this way thismetric was insensitive to class imbalance [68] A multi-class accuracy was usedto determine the overall accuracy for the grade predictions [67]
To evaluate the performance of the automated segmentation we evaluatedthe DICE score the Hausdorff distance and the volumetric similarity coefficientThe DICE score is a measure of overlap between two segmentations where avalue of 1 indicates perfect overlap and the Hausdorff distance is a measureof the closeness of the borders of the segmentations The volumetric similaritycoefficient is a measure of the agreement between the volumes of two segmen-tations without taking account the actual location of the tumor where a valueof 1 indicates perfect agreement See [65] for the definitions of these metrics
36
H Ground truth labels of patients included frompublic datasets
Acknowledgments
Sebastian van der Voort and Fatih Incekara acknowledge funding by the DutchCancer Society (KWF project number EMCR 2015-7859)
Data used in this publication were generated by the National Cancer Insti-tute Clinical Proteomic Tumor Analysis Consortium (CPTAC)
The results published here are in whole or part based upon data generatedby the TCGA Research Network httpcancergenomenihgov
Author contributions
SRvdV FI WJN MS and SK contrived the study and designed theexperiments FI MMJW JWS RNT GJL PCDWH RSEAJPEV MJvdB and MS included patients in the different studiesSRvdV FI MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV MJvdB and MS collected the dataSRvdV carried out the experiments SRvdV FI MS and SK inter-preted the results SRvdV FI MJvdB MS and SK created the initialdraft of the paper MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV WJN and MJvdB revised the paper
References
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[2] Hendrikus J Dubbink Peggy N Atmodimedjo Johan M Kros Pim JFrench Marc Sanson Ahmed Idbaih Pieter Wesseling Roelien EntingWim Spliet Cees Tijssen Winand N M Dinjens Thierry Gorlia andMartin J van den Bent Molecular classification of anaplastic oligoden-droglioma using next-generation sequencing a report of the prospectiverandomized EORTC brain tumor group 26951 phase III trial Neuro-Oncology 18(3)388ndash400 March 2016 ISSN 1522-8517 URL https
doiorg101093neuoncnov182
[3] Jeanette E Eckel-Passow Daniel H Lachance Annette M Molinaro Kyle MWalsh Paul A Decker Hugues Sicotte Melike Pekmezci Terri Rice Matt LKosel Ivan V Smirnov Gobinda Sarkar Alissa A Caron Thomas M
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Kollmeyer Corinne E Praska Anisha R Chada Chandralekha Halder He-len M Hansen Lucie S McCoy Paige M Bracci Roxanne Marshall ShichunZheng Gerald F Reis Alexander R Pico Brian P OrsquoNeill Jan C BucknerCaterina Giannini Jason T Huse Arie Perry Tarik Tihan Mitchell SBerger Susan M Chang Michael D Prados Joseph Wiemels John KWiencke Margaret R Wrensch and Robert B Jenkins Glioma groupsbased on 1p19q IDH and TERT promoter mutations in tumors NewEngland Journal of Medicine 372(26)2499ndash2508 June 2015 ISSN 0028-4793 URL httpsdoiorg101056NEJMoa1407279
[4] David N Louis Arie Perry Guido Reifenberger Andreas von Deimling Do-minique Figarella-Branger Webster K Cavenee Hiroko Ohgaki Otmar DWiestler Paul Kleihues and David W Ellison The 2016 world health orga-nization classification of tumors of the central nervous system a summaryActa Neuropathologica 131(6)803ndash820 June 2016 ISSN 0001-6322 URLhttpsdoiorg101007s00401-016-1545-1
[5] Ching-Chang Chen Peng-Wei Hsu Tai-Wei Erich Wu Shih-Tseng LeeChen-Nen Chang Kuo-chen Wei Chih-Cheng Chuang Chieh-Tsai WuTai-Ngar Lui Yung-Hsin Hsu Tzu-Kang Lin Sai-Cheung Lee and Yin-Cheng Huang Stereotactic brain biopsy Single center retrospective anal-ysis of complications Clinical Neurology and Neurosurgery 111(10)835ndash839 December 2009 ISSN 0303-8467 URL httpsdoiorg101016
jclineuro200908013
[6] Robert J Jackson Gregory N Fuller Dima Abi-Said Frederick F LangZiya L Gokaslan Wei Ming Shi David M Wildrick and Raymond SawayaLimitations of stereotactic biopsy in the initial management of gliomasNeuro-Oncology 3(3)193ndash200 July 2001 ISSN 1522-8517 URL https
doiorg101093neuonc33193
[7] M Zhou J Scott B Chaudhury L Hall D Goldgof KW Yeom M IvY Ou J Kalpathy-Cramer S Napel R Gillies O Gevaert and R GatenbyRadiomics in brain tumor Image assessment quantitative feature de-scriptors and machine-learning approaches American Journal of Neu-roradiology 39(2)208ndash216 February 2018 ISSN 0195-6108 URL https
doiorg103174ajnrA5391
[8] Wenya Linda Bi Ahmed Hosny Matthew B Schabath Maryellen LGiger Nicolai J Birkbak Alireza Mehrtash Tavis Allison Omar ArnaoutChristopher Abbosh Ian F Dunn Raymond H Mak Rulla M TamimiClare M Tempany Charles Swanton Udo Hoffmann Lawrence H SchwartzRobert J Gillies Raymond Y Huang and Hugo J W L Aerts Artificialintelligence in cancer imaging Clinical challenges and applications CAA Cancer Journal for Clinicians 69(2)caac21552 February 2019 ISSN0007-9235 URL httpsdoiorg103322caac21552
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[9] Ahmad Chaddad Michael Jonathan Kucharczyk Paul Daniel SihamSabri Bertrand J Jean-Claude Tamim Niazi and Bassam AbdulkarimRadiomics in glioblastoma Current status and challenges facing clinicalimplementation Frontiers in Oncology 9374ndash374 May 2019 ISSN 2234-943X URL httpsdoiorg103389fonc201900374
[10] Marion Smits Imaging of oligodendroglioma The British Journal of Ra-diology 89(1060)20150857 April 2016 ISSN 0007-1285 URL https
doiorg101259bjr20150857
[11] Rachel L Delfanti David E Piccioni Jason Handwerker Naeim BahramiAnithaPriya Krishnan Roshan Karunamuni Jona A Hattangadi-GluthTyler M Seibert Ashwin Srikant Karra A Jones Vivian S Snyder An-ders M Dale Nathan S White Carrie R McDonald and Nikdokht FaridImaging correlates for the 2016 update on WHO classification of gradeIIIII gliomas implications for IDH 1p19q and ATRX status Journal ofNeuro-Oncology 135(3)601ndash609 December 2017 ISSN 0167-594X URLhttpsdoiorg101007s11060-017-2613-7
[12] Sonal Gore Tanay Chougule Jayant Jagtap Jitender Saini and MadhuraIngalhalikar A review of radiomics and deep predictive modeling in gliomacharacterization Academic Radiology July 2020 ISSN 1076-6332 URLhttpsdoiorg101016jacra202006016
[13] Hugo J W L Aerts Emmanuel Rios Velazquez Ralph T H LeijenaarChintan Parmar Patrick Grossmann Sara Carvalho Johan BussinkRene Monshouwer Benjamin Haibe-Kains Derek Rietveld Frank HoebersMichelle M Rietbergen C Rene Leemans Andre Dekker John Quacken-bush Robert J Gillies and Philippe Lambin Decoding tumour pheno-type by noninvasive imaging using a quantitative radiomics approach Na-ture Communications 5(1)4006 September 2014 ISSN 2041-1723 URLhttpsdoiorg101038ncomms5006
[14] Sarah Chihati and Djamel Gaceb A review of recent progress in deeplearning-based methods for MRI brain tumor segmentation In 202011th International Conference on Information and Communication Sys-tems (ICICS) pages 149ndash154 Institute of Electrical and Electronics Engi-neers (IEEE) April 2020 ISBN 9781728162270 URL httpsdoiorg
101109icics494692020239550
[15] Robert J Gillies Paul E Kinahan and Hedvig Hricak Radiomics Imagesare more than pictures they are data Radiology 278(2)563ndash577 Febru-ary 2016 ISSN 0033-8419 URL httpsdoiorg101148radiol
2015151169
[16] James H Thrall Xiang Li Quanzheng Li Cinthia Cruz Synho Do KeithDreyer and James Brink Artificial intelligence and machine learning in ra-diology Opportunities challenges pitfalls and criteria for success Journal
39
of the American College of Radiology 15(3 Part B)504ndash508 March 2018ISSN 1546-1440 URL httpsdoiorg101016jjacr201712026
[17] Ahmed Hosny Chintan Parmar John Quackenbush Lawrence H Schwartzand Hugo J W L Aerts Artificial intelligence in radiology Nature ReviewsCancer 18(8)500ndash510 August 2018 ISSN 1474-175X URL https
doiorg101038s41568-018-0016-5
[18] Okan Kopuklu Neslihan Kose Ahmet Gunduz and Gerhard Rigoll Re-source efficient 3D convolutional neural networks In 2019 IEEECVFInternational Conference on Computer Vision Workshop (ICCVW) Insti-tute of Electrical and Electronics Engineers (IEEE) October 2019 ISBN9781728150239 URL httpsdoiorg101109iccvw201900240
[19] S C Thust S Heiland A Falini H R Jager A D Waldman P C SundgrenC Godi V K Katsaros A Ramos N Bargallo M W Vernooij T YousryM Bendszus and M Smits Glioma imaging in europe A survey of 220centres and recommendations for best clinical practice European Radiology28(8)3306ndash3317 August 2018 ISSN 0938-7994 URL httpsdoiorg
101007s00330-018-5314-5
[20] Christian F Freyschlag Sandro M Krieg Johannes Kerschbaumer DanielPinggera Marie-Therese Forster Dominik Cordier Marco Rossi GabrieleMiceli Alexandre Roux Andres Reyes Silvio Sarubbo Anja Smits JoannaSierpowska Pierre A Robe Geert-Jan Rutten Thomas Santarius TomaszMatys Marc Zanello Fabien Almairac Lydiane Mondot Asgeir S JakolaMaria Zetterling Adria Rofes Gord von Campe Remy Guillevin DanieleBagatto Vincent Lubrano Marion Rapp John Goodden Philip C De WittHamer Johan Pallud Lorenzo Bello Claudius Thome Hugues Duffauand Emmanuel Mandonnet Imaging practice in low-grade gliomas amongeuropean specialized centers and proposal for a minimum core of imagingJournal of Neuro-Oncology 139(3)699ndash711 September 2018 ISSN 0167-594X URL httpsdoiorg101007s11060-018-2916-3
[21] M Labussiere A Idbaih X-W Wang Y Marie B Boisselier C FaletS Paris J Laffaire C Carpentier E Criniere F Ducray S El HallaniK Mokhtari K Hoang-Xuan J-Y Delattre and M Sanson All the 1p19qcodeleted gliomas are mutated on IDH1 or IDH2 Neurology 74(23)1886ndash1890 June 2010 ISSN 0028-3878 URL httpsdoiorg101212WNL
0b013e3181e1cf3a
[22] David N Louis Pieter Wesseling Kenneth Aldape Daniel J Brat DavidCapper Ian A Cree Charles Eberhart Dominique Figarella-BrangerMaryam Fouladi Gregory N Fuller Caterina Giannini Christine Haber-ler Cynthia Hawkins Takashi Komori Johan M Kros HK Ng Brent AOrr Sung-Hye Park Werner Paulus Arie Perry Torsten Pietsch GuidoReifenberger Marc Rosenblum Brian Rous Felix Sahm Chitra SarkarDavid A Solomon Uri Tabori Martin J Bent Andreas Deimling Michael
40
Weller Valerie A White and David W Ellison cIMPACT-NOW up-date 6 new entity and diagnostic principle recommendations of thecIMPACT-Utrecht meeting on future CNS tumor classification and grad-ing Brain Pathology 30(4)844ndash856 July 2020 ISSN 1015-6305 URLhttpsdoiorg101111bpa12832
[23] Zhenyu Tang Yuyun Xu Zhicheng Jiao Junfeng Lu Lei Jin Abudumi-jiti Aibaidula Jinsong Wu Qian Wang Han Zhang and Dinggang ShenPre-operative overall survival time prediction for glioblastoma patients us-ing deep learning on both imaging phenotype and genotype In Ding-gang Shen Tianming Liu Terry M Peters Lawrence H Staib CarolineEssert Sean Zhou Pew-Thian Yap and Ali Khan editors Lecture Notesin Computer Science pages 415ndash422 Springer Science and Business Me-dia LLC 2019 ISBN 9783030322380 URL httpsdoiorg101007
978-3-030-32239-7_46
[24] Zhiyuan Xue Bowen Xin Dingqian Wang and Xiuying Wang Radiomics-enhanced multi-task neural network for non-invasive glioma subtypingand segmentation In Hassan Mohy-ud Din and Saima Rathore editorsRadiomics and Radiogenomics in Neuro-oncology pages 81ndash90 SpringerScience and Business Media LLC 2020 ISBN 9783030401238 URLhttpsdoiorg101007978-3-030-40124-5_9
[25] Milan Decuyper Stijn Bonte Karel Deblaere and Roel Van Holen Au-tomated MRI based pipeline for glioma segmentation and prediction ofgrade IDH mutation and 1p19q co-deletion 2020 Preprint at https
arxivorgabs200511965
[26] Stefania Rizzo Francesca Botta Sara Raimondi Daniela Origgi Cris-tiana Fanciullo Alessio Giuseppe Morganti and Massimo Bellomi Ra-diomics the facts and the challenges of image analysis European Ra-diology Experimental 2(1)36 December 2018 ISSN 2509-9280 URLhttpsdoiorg101186s41747-018-0068-z
[27] Philipp Lohmann Norbert Galldiks Martin Kocher Alexander HeinzelChristian P Filss Carina Stegmayr Felix M Mottaghy Gereon R FinkN Jon Shah and Karl-Josef Langen Radiomics in neuro-oncology Basicsworkflow and applications Methods June 2020 ISSN 1046-2023 URLhttpsdoiorg101016jymeth202006003
[28] Stephen S F Yip and Hugo J W L Aerts Applications and limitationsof radiomics Physics in Medicine and Biology 61(13)R150ndashR166 July2016 ISSN 0031-9155 URL httpsdoiorg1010880031-915561
13r150
[29] Annika Malmstrom Ma lgorzata Lysiak Bjarne Winther Kristensen Eliz-abeth Hovey Roger Henriksson and Peter Soderkvist Do we really knowwho has an MGMT methylated glioma Results of an international survey
41
regarding use of MGMT analyses for glioma Neuro-Oncology Practice 7(1)68ndash76 09 2019 ISSN 2054-2577 URL httpsdoiorg101093
nopnpz039
[30] Takahiro Sasaki Manabu Kinoshita Koji Fujita Junya Fukai NobuhideHayashi Yuji Uematsu Yoshiko Okita Masahiro Nonaka Shusuke Mo-riuchi Takehiro Uda Naohiro Tsuyuguchi Hideyuki Arita Kanji MoriKenichi Ishibashi Koji Takano Namiko Nishida Tomoko Shofuda EmaYoshioka Daisuke Kanematsu Yoshinori Kodama Masayuki ManoNaoyuki Nakao and Yonehiro Kanemura Radiomics and MGMT pro-moter methylation for prognostication of newly diagnosed glioblastomaScientific Reports 9(1)14435 December 2019 ISSN 2045-2322 URLhttpsdoiorg101038s41598-019-50849-y
[31] A Gupta A Prager RJ Young W Shi AMP Omuro and JJ GraberDiffusion-weighted MR imaging and MGMT methylation status in glioblas-toma A reappraisal of the role of preoperative quantitative ADC measure-ments American Journal of Neuroradiology 34(1)E10ndashE11 January 2013ISSN 0195-6108 URL httpsdoiorg103174ajnrA3467
[32] JA Carrillo A Lai PL Nghiemphu HJ Kim HS Phillips S KharbandaP Moftakhar S Lalaezari W Yong BM Ellingson TF Cloughesy andWB Pope Relationship between tumor enhancement edema IDH1 muta-tional status MGMT promoter methylation and survival in glioblastomaAmerican Journal of Neuroradiology 33(7)1349ndash1355 August 2012 ISSN0195-6108 URL httpsdoiorg103174ajnrA2950
[33] Vilde Elisabeth Mikkelsen Hong Yan Dai Anne Line Stensjoslashen Erik Mag-nus Berntsen Oslashyvind Salvesen Ole Solheim and Sverre Helge TorpMGMT promoter methylation status is not related to histological or radio-logical features in IDH wild-type glioblastomas Journal of Neuropathologyamp Experimental Neurology 79(8)855ndash862 August 2020 ISSN 0022-3069URL httpsdoiorg101093jnennlaa060
[34] Martin J van den Bent Interobserver variation of the histopathologicaldiagnosis in clinical trials on glioma a clinicianrsquos perspective Acta Neu-ropathologica 120(3)297ndash304 September 2010 ISSN 1432-0533 URLhttpsdoiorg101007s00401-010-0725-7
[35] Ji Eun Park Ho Sung Kim Youngheun Jo Roh-Eul Yoo Seung Hong ChoiSoo Jung Nam and Jeong Hoon Kim Radiomics prognostication modelin glioblastoma using diffusion- and perfusion-weighted MRI ScientificReports 10(1)4250 December 2020 ISSN 2045-2322 URL httpsdoi
org101038s41598-020-61178-w
[36] Minjae Kim So Yeong Jung Ji Eun Park Yeongheun Jo Seo YoungPark Soo Jung Nam Jeong Hoon Kim and Ho Sung Kim Diffusion-and perfusion-weighted MRI radiomics model may predict isocitrate dehy-drogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade
42
glioma European Radiology 30(4)2142ndash2151 April 2020 ISSN 0938-7994URL httpsdoiorg101007s00330-019-06548-3
[37] M Visser DMJ Muller RJM van Duijn M Smits N Verburg EJ HendriksRJA Nabuurs JCJ Bot RS Eijgelaar M Witte MB van Herk F BarkhofPC de WittHamer and JC de Munck Inter-rater agreement in glioma seg-mentations on longitudinal MRI NeuroImage Clinical 22101727 2019ISSN 2213-1582 URL httpsdoiorg101016jnicl2019101727
[38] Kenneth Clark Bruce Vendt Kirk Smith John Freymann Justin KirbyPaul Koppel Stephen Moore Stanley Phillips David Maffitt MichaelPringle Lawrence Tarbox and Fred Prior The cancer imaging archive(TCIA) Maintaining and operating a public information repository Jour-nal of Digital Imaging 26(6)1045ndash1057 December 2013 ISSN 0897-1889URL httpsdoiorg101007s10278-013-9622-7
[39] Lisa Scarpace Adam E Flanders Rajan Jain Tom Mikkelsen and David WAndrews Data from REMBRANDT The Cancer Imaging Archive 2015URL httpsdoiorg107937K9TCIA2015588OZUZB
[40] National Cancer Institute Clinical Proteomic Tumor Analysis Consortium(CPTAC) Radiology data from the clinical proteomic tumor analysisconsortium glioblastoma multiforme CPTAC-GBM collection The CancerImaging Archive 2018 URL httpsdoiorg107937k9tcia2018
3rje41q1
[41] Nameeta Shah Xu Feng Michael Lankerovich Ralph B Puchalski andBart Keogh Data from Ivy GAP The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016XLwaN6nL
[42] Ralph B Puchalski Nameeta Shah Jeremy Miller Rachel Dalley Steve RNomura Jae-Guen Yoon Kimberly A Smith Michael Lankerovich DarrenBertagnolli Kris Bickley Andrew F Boe Krissy Brouner Stephanie But-ler Shiella Caldejon Mike Chapin Suvro Datta Nick Dee Tsega DestaTim Dolbeare Nadezhda Dotson Amanda Ebbert David Feng Xu FengMichael Fisher Garrett Gee Jeff Goldy Lindsey Gourley Benjamin WGregor Guangyu Gu Nika Hejazinia John Hohmann Parvinder HothiRobert Howard Kevin Joines Ali Kriedberg Leonard Kuan Chris LauFelix Lee Hwahyung Lee Tracy Lemon Fuhui Long Naveed Mastan ErikaMott Chantal Murthy Kiet Ngo Eric Olson Melissa Reding Zack RileyDavid Rosen David Sandman Nadiya Shapovalova Clifford R Slaughter-beck Andrew Sodt Graham Stockdale Aaron Szafer Wayne WakemanPaul E Wohnoutka Steven J White Don Marsh Robert C RostomilyLydia Ng Chinh Dang Allan Jones Bart Keogh Haley R GittlemanJill S Barnholtz-Sloan Patrick J Cimino Megha S Uppin C Dirk KeeneFarrokh R Farrokhi Justin D Lathia Michael E Berens Antonio IavaroneAmy Bernard Ed Lein John W Phillips Steven W Rostad Charles Cobbs
43
Michael J Hawrylycz and Greg D Foltz An anatomic transcriptional at-las of human glioblastoma Science 360(6389)660ndash663 May 2018 ISSN0036-8075 URL httpsdoiorg101126scienceaaf2666
[43] Kathleen Schmainda and Melissa Prah Data from Brain-Tumor-Progression The Cancer Imaging Archive 2018 URL httpsdoiorg
107937K9TCIA201815quzvnb
[44] B H Menze A Jakab S Bauer J Kalpathy-Cramer K FarahaniJ Kirby Y Burren N Porz J Slotboom R Wiest L Lanczi E GerstnerM Weber T Arbel B B Avants N Ayache P Buendia D L CollinsN Cordier J J Corso A Criminisi T Das H Delingette C Demi-ralp C R Durst M Dojat S Doyle J Festa F Forbes E GeremiaB Glocker P Golland X Guo A Hamamci K M IftekharuddinR Jena N M John E Konukoglu D Lashkari J A Mariz R MeierS Pereira D Precup S J Price T R Raviv S M S Reza M RyanD Sarikaya L Schwartz H Shin J Shotton C A Silva N SousaN K Subbanna G Szekely T J Taylor O M Thomas N J Tusti-son G Unal F Vasseur M Wintermark D H Ye L Zhao B ZhaoD Zikic M Prastawa M Reyes and K Van Leemput The mul-timodal brain tumor image segmentation benchmark (BRATS) IEEETransactions on Medical Imaging 34(10)1993ndash2024 2015 URL https
doiorg101109TMI20142377694
[45] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin S Kirby John B Freymann Keyvan Farahani and ChristosDavatzikos Advancing the cancer genome atlas glioma MRI collectionswith expert segmentation labels and radiomic features Scientific Data 4(1)170117 December 2017 ISSN 2052-4463 URL httpsdoiorg10
1038sdata2017117
[46] Spyridon Bakas Mauricio Reyes Andras Jakab Stefan Bauer MarkusRempfler Alessandro Crimi Russell Takeshi Shinohara Christoph BergerSung Min Ha Martin Rozycki Marcel Prastawa Esther Alberts JanaLipkova John Freymann Justin Kirby Michel Bilello Hassan Fathallah-Shaykh Roland Wiest Jan Kirschke Benedikt Wiestler Rivka ColenAikaterini Kotrotsou Pamela Lamontagne Daniel Marcus MikhailMilchenko Arash Nazeri Marc-Andre Weber Abhishek Mahajan UjjwalBaid Elizabeth Gerstner Dongjin Kwon Gagan Acharya Manu Agar-wal Mahbubul Alam Alberto Albiol Antonio Albiol Francisco J AlbiolVarghese Alex Nigel Allinson Pedro H A Amorim Abhijit AmrutkarGanesh Anand Simon Andermatt Tal Arbel Pablo Arbelaez Aaron Av-ery Muneeza Azmat Pranjal B W Bai Subhashis Banerjee Bill BarthThomas Batchelder Kayhan Batmanghelich Enzo Battistella AndrewBeers Mikhail Belyaev Martin Bendszus Eze Benson Jose Bernal Ha-landur Nagaraja Bharath George Biros Sotirios Bisdas James BrownMariano Cabezas Shilei Cao Jorge M Cardoso Eric N Carver Adria
44
Casamitjana Laura Silvana Castillo Marcel Cata Philippe Cattin Al-bert Cerigues Vinicius S Chagas Siddhartha Chandra Yi-Ju ChangShiyu Chang Ken Chang Joseph Chazalon Shengcong Chen Wei ChenJefferson W Chen Zhaolin Chen Kun Cheng Ahana Roy ChoudhuryRoger Chylla Albert Clerigues Steven Colleman Ramiro German Ro-driguez Colmeiro Marc Combalia Anthony Costa Xiaomeng Cui Zhen-zhen Dai Lutao Dai Laura Alexandra Daza Eric Deutsch ChangxingDing Chao Dong Shidu Dong Wojciech Dudzik Zach Eaton-Rosen GaryEgan Guilherme Escudero Theo Estienne Richard Everson JonathanFabrizio Yong Fan Longwei Fang Xue Feng Enzo Ferrante Lucas Fi-don Martin Fischer Andrew P French Naomi Fridman Huan Fu DavidFuentes Yaozong Gao Evan Gates David Gering Amir Gholami WilliGierke Ben Glocker Mingming Gong Sandra Gonzalez-Villa T Gros-ges Yuanfang Guan Sheng Guo Sudeep Gupta Woo-Sup Han Il SongHan Konstantin Harmuth Huiguang He Aura Hernandez-Sabate EvelynHerrmann Naveen Himthani Winston Hsu Cheyu Hsu Xiaojun Hu Xi-aobin Hu Yan Hu Yifan Hu Rui Hua Teng-Yi Huang Weilin HuangSabine Van Huffel Quan Huo Vivek HV Khan M Iftekharuddin FabianIsensee Mobarakol Islam Aaron S Jackson Sachin R Jambawalikar An-drew Jesson Weijian Jian Peter Jin V Jeya Maria Jose Alain JungoB Kainz Konstantinos Kamnitsas Po-Yu Kao Ayush Karnawat ThomasKellermeier Adel Kermi Kurt Keutzer Mohamed Tarek Khadir Mahen-dra Khened Philipp Kickingereder Geena Kim Nik King Haley KnappUrspeter Knecht Lisa Kohli Deren Kong Xiangmao Kong Simon Kop-pers Avinash Kori Ganapathy Krishnamurthi Egor Krivov Piyush Ku-mar Kaisar Kushibar Dmitrii Lachinov Tryphon Lambrou Joon LeeChengen Lee Yuehchou Lee M Lee Szidonia Lefkovits Laszlo LefkovitsJames Levitt Tengfei Li Hongwei Li Wenqi Li Hongyang Li XiaochuanLi Yuexiang Li Heng Li Zhenye Li Xiaoyu Li Zeju Li XiaoGang LiWenqi Li Zheng-Shen Lin Fengming Lin Pietro Lio Chang Liu Bo-qiang Liu Xiang Liu Mingyuan Liu Ju Liu Luyan Liu Xavier LladoMarc Moreno Lopez Pablo Ribalta Lorenzo Zhentai Lu Lin Luo ZhigangLuo Jun Ma Kai Ma Thomas Mackie Anant Madabushi Issam Mah-moudi Klaus H Maier-Hein Pradipta Maji CP Mammen Andreas MangB S Manjunath Michal Marcinkiewicz S McDonagh Stephen McKennaRichard McKinley Miriam Mehl Sachin Mehta Raghav Mehta RaphaelMeier Christoph Meinel Dorit Merhof Craig Meyer Robert Miller Sush-mita Mitra Aliasgar Moiyadi David Molina-Garcia Miguel A B Mon-teiro Grzegorz Mrukwa Andriy Myronenko Jakub Nalepa Thuyen NgoDong Nie Holly Ning Chen Niu Nicholas K Nuechterlein Eric Oer-mann Arlindo Oliveira Diego D C Oliveira Arnau Oliver AlexanderF I Osman Yu-Nian Ou Sebastien Ourselin Nikos Paragios Moo SungPark Brad Paschke J Gregory Pauloski Kamlesh Pawar Nick PawlowskiLinmin Pei Suting Peng Silvio M Pereira Julian Perez-Beteta Vic-tor M Perez-Garcia Simon Pezold Bao Pham Ashish Phophalia GemmaPiella G N Pillai Marie Piraud Maxim Pisov Anmol Popli Michael P
45
Pound Reza Pourreza Prateek Prasanna Vesna Prkovska Tony P Prid-more Santi Puch Elodie Puybareau Buyue Qian Xu Qiao MartinRajchl Swapnil Rane Michael Rebsamen Hongliang Ren Xuhua RenKarthik Revanuru Mina Rezaei Oliver Rippel Luis Carlos Rivera Char-lotte Robert Bruce Rosen Daniel Rueckert Mohammed Safwan MostafaSalem Joaquim Salvi Irina Sanchez Irina Sanchez Heitor M Santos Em-mett Sartor Dawid Schellingerhout Klaudius Scheufele Matthew R ScottArtur A Scussel Sara Sedlar Juan Pablo Serrano-Rubio N Jon ShahNameetha Shah Mazhar Shaikh B Uma Shankar Zeina Shboul HaipengShen Dinggang Shen Linlin Shen Haocheng Shen Varun Shenoy FengShi Hyung Eun Shin Hai Shu Diana Sima M Sinclair Orjan SmedbyJames M Snyder Mohammadreza Soltaninejad Guidong Song MehulSoni Jean Stawiaski Shashank Subramanian Li Sun Roger Sun JiaweiSun Kay Sun Yu Sun Guoxia Sun Shuang Sun Yannick R Suter Las-zlo Szilagyi Sanjay Talbar Dacheng Tao Dacheng Tao Zhongzhao TengSiddhesh Thakur Meenakshi H Thakur Sameer Tharakan Pallavi Ti-wari Guillaume Tochon Tuan Tran Yuhsiang M Tsai Kuan-Lun TsengTran Anh Tuan Vadim Turlapov Nicholas Tustison Maria VakalopoulouSergi Valverde Rami Vanguri Evgeny Vasiliev Jonathan Ventura LuisVera Tom Vercauteren C A Verrastro Lasitha Vidyaratne Veronica Vi-laplana Ajeet Vivekanandan Guotai Wang Qian Wang Chiatse J WangWeichung Wang Duo Wang Ruixuan Wang Yuanyuan Wang ChunliangWang Guotai Wang Ning Wen Xin Wen Leon Weninger Wolfgang WickShaocheng Wu Qiang Wu Yihong Wu Yong Xia Yanwu Xu XiaowenXu Peiyuan Xu Tsai-Ling Yang Xiaoping Yang Hao-Yu Yang JunlinYang Haojin Yang Guang Yang Hongdou Yao Xujiong Ye ChangchangYin Brett Young-Moxon Jinhua Yu Xiangyu Yue Songtao Zhang AngelaZhang Kun Zhang Xuejie Zhang Lichi Zhang Xiaoyue Zhang YazhuoZhang Lei Zhang Jianguo Zhang Xiang Zhang Tianhao Zhang SichengZhao Yu Zhao Xiaomei Zhao Liang Zhao Yefeng Zheng Liming ZhongChenhong Zhou Xiaobing Zhou Fan Zhou Hongtu Zhu Jin Zhu YingZhuge Weiwei Zong Jayashree Kalpathy-Cramer Keyvan Farahani Chris-tos Davatzikos Koen van Leemput and Bjoern Menze Identifying the bestmachine learning algorithms for brain tumor segmentation progression as-sessment and overall survival prediction in the BRATS challenge 2018Preprint at httpsarxivorgabs181102629
[47] Nancy Pedano Adam E Flanders Lisa Scarpace Tom Mikkelsen Jen-nifer M Eschbacher Beth Hermes Victor Sisneros Jill Barnholtz-Sloanand Quinn Ostrom Radiology data from the cancer genome atlas lowgrade glioma [TCGA-LGG] collection The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016L4LTD3TK
[48] Lisa Scarpace Tom Mikkelsen Soonmee Cha Sujaya Rao SangeetaTekchandani David Gutman Joel H Saltz Bradley J Erickson NancyPedano Adam E Flanders Jill Barnholtz-Sloan Quinn Ostrom DanielBarboriak and Laura J Pierce Radiology data from the cancer genome
46
atlas glioblastoma multiforme [TCGA-GBM] collection The Cancer Imag-ing Archive 2016 URL httpsdoiorg107937K9TCIA2016
RNYFUYE9
[49] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin Kirby John Freymann Keyvan Farahani and ChristosDavatzikos Segmentation labels and radiomic features for the pre-operativescans of the TCGA-LGG collection [data set] The Cancer Imaging Archive2017 URL httpsdoiorg107937K9TCIA2017GJQ7R0EF
[50] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello Mar-tin Rozycki Justin Kirby John Freymann Keyvan Farahani and Chris-tos Davatzikos Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [data set] The CancerImaging Archive 2017 URL httpsdoiorg107937K9TCIA2017
KLXWJJ1Q
[51] Xiangrui Li Paul S Morgan John Ashburner Jolinda Smith and Christo-pher Rorden The first step for neuroimaging data analysis DICOM toNIfTI conversion Journal of Neuroscience Methods 26447ndash56 May 2016ISSN 0165-0270 URL httpsdoiorg101016jjneumeth201603
001
[52] Vladimir Fonov Alan C Evans Kelly Botteron C Robert Almli Robert CMcKinstry and D Louis Collins Unbiased average age-appropriate at-lases for pediatric studies NeuroImage 54(1)313ndash327 January 2011ISSN 1053-8119 URL httpsdoiorg101016jneuroimage2010
07033
[53] VS Fonov AC Evans RC McKinstry CR Almli and DL Collins Unbiasednonlinear average age-appropriate brain templates from birth to adulthoodNeuroImage 47S102 July 2009 ISSN 1053-8119 URL httpsdoi
org101016S1053-8119(09)70884-5 Organization for Human BrainMapping 2009 Annual Meeting
[54] S Klein M Staring K Murphy MA Viergever and J Pluim elastix Atoolbox for intensity-based medical image registration IEEE Transactionson Medical Imaging 29(1)196ndash205 January 2010 ISSN 0278-0062 URLhttpsdoiorg101109TMI20092035616
[55] Denis Shamonin Fast parallel image registration on CPU and GPU fordiagnostic classification of alzheimerrsquos disease Frontiers in Neuroinfor-matics 750 2013 ISSN 1662-5196 URL httpsdoiorg103389
fninf201300050
[56] Bradley C Lowekamp David T Chen Luis Ibanez and Daniel Blezek Thedesign of SimpleITK Frontiers in Neuroinformatics 745 2013 ISSN1662-5196 URL httpsdoiorg103389fninf201300045
47
[57] Fabian Isensee Marianne Schell Irada Pflueger Gianluca Brugnara DavidBonekamp Ulf Neuberger Antje Wick Heinz-Peter Schlemmer SabineHeiland Wolfgang Wick Martin Bendszus Klaus H Maier-Hein andPhilipp Kickingereder Automated brain extraction of multisequence MRIusing artificial neural networks Human Brain Mapping 40(17)4952ndash4964December 2019 ISSN 1065-9471 URL httpsdoiorg101002hbm
24750
[58] Olaf Ronneberger Invited talk U-net convolutional networks for biomed-ical image segmentation In geb Fritzsche K Maier-Hein geb Lehmann TDeserno Heinz Handels and Thomas Tolxdorff editors Bildverarbeitungfur die Medizin 2017 Informatik aktuell pages 3ndash3 Springer Scienceand Business Media LLC 2017 ISBN 9783662543443 URL https
doiorg101007978-3-662-54345-0_3
[59] Martın Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy DavisJeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving MichaelIsard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry MooreDerek G Murray Benoit Steiner Paul Tucker Vijay Vasudevan PeteWarden Martin Wicke Yuan Yu and Xiaoqiang Zheng Tensor-Flow A system for large-scale machine learning In 12th USENIXSymposium on Operating Systems Design and Implementation (OSDI16) pages 265ndash283 USENIX Association November 2016 ISBN978-1-931971-33-1 URL httpswwwusenixorgconferenceosdi16
technical-sessionspresentationabadi
[60] Dipankar Das Naveen Mellempudi Dheevatsa Mudigere Dhiraj KalamkarSasikanth Avancha Kunal Banerjee Srinivas Sridharan KarthikVaidyanathan Bharat Kaul Evangelos Georganas Alexander HeineckePradeep Dubey Jesus Corbal Nikita Shustrov Roma Dubtsov EvaristFomenko and Vadim Pirogov Mixed precision training of convolutionalneural networks using integer operations In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=H135uzZ0-
[61] Samuel L Smith Pieter-Jan Kindermans and Quoc V Le Donrsquot decaythe learning rate increase the batch size In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=B1Yy1BxCZ
[62] Cliff Woolley NCCL accelarated multi-GPU collective communicationsURL httpsimagesnvidiacomeventssc15pdfsNCCL-Woolley
pdf Accessed on 2020-09-30
[63] Ilya Loshchilov and Frank Hutter Decoupled weight decay regularization2017 Preprint at httpsarxivorgabs171105101
48
[64] F Pedregosa G Varoquaux A Gramfort V Michel B Thirion O GriselM Blondel P Prettenhofer R Weiss V Dubourg J Vanderplas A Pas-sos D Cournapeau M Brucher M Perrot and E Duchesnay Scikit-learn Machine learning in python Journal of Machine Learning Re-search 122825ndash2830 2011 URL httpswwwjmlrorgpapersv12
pedregosa11ahtml
[65] Abdel Aziz Taha and Allan Hanbury Metrics for evaluating 3d medicalimage segmentation analysis selection and tool BMC Medical Imaging15(1)29 August 2015 ISSN 1471-2342 URL httpsdoiorg101186
s12880-015-0068-x
[66] Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viegas and MartinWattenberg SmoothGrad removing noise by adding noise 2017 Preprintat httpsarxivorgabs170603825
[67] Alaa Tharwat Classification assessment methods Applied Computing andInformatics 2018 ISSN 2210-8327 URL httpsdoiorg101016j
aci201808003
[68] David J Hand and Robert J Till A simple generalisation of the areaunder the ROC curve for multiple class classification problems MachineLearning 45(2)171ndash186 November 2001 ISSN 1573-0565 URL https
doiorg101023A1010920819831
49
Data Collection | Patient | IDH_mutated | 1p19q_codeleted | Grade | |||||
BTumorP | PGBM-001 | -1 | -1 | -1 | |||||
BTumorP | PGBM-002 | -1 | -1 | -1 | |||||
BTumorP | PGBM-003 | -1 | -1 | -1 | |||||
BTumorP | PGBM-004 | -1 | -1 | -1 | |||||
BTumorP | PGBM-005 | -1 | -1 | -1 | |||||
BTumorP | PGBM-006 | -1 | -1 | -1 | |||||
BTumorP | PGBM-007 | -1 | -1 | -1 | |||||
BTumorP | PGBM-008 | -1 | -1 | -1 | |||||
BTumorP | PGBM-009 | -1 | -1 | -1 | |||||
BTumorP | PGBM-010 | -1 | -1 | -1 | |||||
BTumorP | PGBM-011 | -1 | -1 | -1 | |||||
BTumorP | PGBM-012 | -1 | -1 | -1 | |||||
BTumorP | PGBM-013 | -1 | -1 | -1 | |||||
BTumorP | PGBM-014 | -1 | -1 | -1 | |||||
BTumorP | PGBM-015 | -1 | -1 | -1 | |||||
BTumorP | PGBM-016 | -1 | -1 | -1 | |||||
BTumorP | PGBM-017 | -1 | -1 | -1 | |||||
BTumorP | PGBM-018 | -1 | -1 | -1 | |||||
BTumorP | PGBM-019 | -1 | -1 | -1 | |||||
BTumorP | PGBM-020 | -1 | -1 | -1 | |||||
BraTS | 2013_0 | -1 | -1 | -1 | |||||
BraTS | 2013_10 | -1 | -1 | -1 | |||||
BraTS | 2013_11 | -1 | -1 | -1 | |||||
BraTS | 2013_12 | -1 | -1 | -1 | |||||
BraTS | 2013_13 | -1 | -1 | -1 | |||||
BraTS | 2013_14 | -1 | -1 | -1 | |||||
BraTS | 2013_15 | -1 | -1 | -1 | |||||
BraTS | 2013_16 | -1 | -1 | -1 | |||||
BraTS | 2013_17 | -1 | -1 | -1 | |||||
BraTS | 2013_18 | -1 | -1 | -1 | |||||
BraTS | 2013_19 | -1 | -1 | -1 | |||||
BraTS | 2013_1 | -1 | -1 | -1 | |||||
BraTS | 2013_20 | -1 | -1 | -1 | |||||
BraTS | 2013_21 | -1 | -1 | -1 | |||||
BraTS | 2013_22 | -1 | -1 | -1 | |||||
BraTS | 2013_23 | -1 | -1 | -1 | |||||
BraTS | 2013_24 | -1 | -1 | -1 | |||||
BraTS | 2013_25 | -1 | -1 | -1 | |||||
BraTS | 2013_26 | -1 | -1 | -1 | |||||
BraTS | 2013_27 | -1 | -1 | -1 | |||||
BraTS | 2013_28 | -1 | -1 | -1 | |||||
BraTS | 2013_29 | -1 | -1 | -1 | |||||
BraTS | 2013_2 | -1 | -1 | -1 | |||||
BraTS | 2013_3 | -1 | -1 | -1 | |||||
BraTS | 2013_4 | -1 | -1 | -1 | |||||
BraTS | 2013_5 | -1 | -1 | -1 | |||||
BraTS | 2013_6 | -1 | -1 | -1 | |||||
BraTS | 2013_7 | -1 | -1 | -1 | |||||
BraTS | 2013_8 | -1 | -1 | -1 | |||||
BraTS | 2013_9 | -1 | -1 | -1 | |||||
BraTS | CBICA_AAB | -1 | -1 | -1 | |||||
BraTS | CBICA_AAG | -1 | -1 | -1 | |||||
BraTS | CBICA_AAL | -1 | -1 | -1 | |||||
BraTS | CBICA_AAP | -1 | -1 | -1 | |||||
BraTS | CBICA_ABB | -1 | -1 | -1 | |||||
BraTS | CBICA_ABE | -1 | -1 | -1 | |||||
BraTS | CBICA_ABM | -1 | -1 | -1 | |||||
BraTS | CBICA_ABN | -1 | -1 | -1 | |||||
BraTS | CBICA_ABO | -1 | -1 | -1 | |||||
BraTS | CBICA_ABY | -1 | -1 | -1 | |||||
BraTS | CBICA_ALN | -1 | -1 | -1 | |||||
BraTS | CBICA_ALU | -1 | -1 | -1 | |||||
BraTS | CBICA_ALX | -1 | -1 | -1 | |||||
BraTS | CBICA_AME | -1 | -1 | -1 | |||||
BraTS | CBICA_AMH | -1 | -1 | -1 | |||||
BraTS | CBICA_ANG | -1 | -1 | -1 | |||||
BraTS | CBICA_ANI | -1 | -1 | -1 | |||||
BraTS | CBICA_ANP | -1 | -1 | -1 | |||||
BraTS | CBICA_ANV | -1 | -1 | -1 | |||||
BraTS | CBICA_ANZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AOC | -1 | -1 | -1 | |||||
BraTS | CBICA_AOD | -1 | -1 | -1 | |||||
BraTS | CBICA_AOH | -1 | -1 | -1 | |||||
BraTS | CBICA_AOO | -1 | -1 | -1 | |||||
BraTS | CBICA_AOP | -1 | -1 | -1 | |||||
BraTS | CBICA_AOS | -1 | -1 | -1 | |||||
BraTS | CBICA_AOZ | -1 | -1 | -1 | |||||
BraTS | CBICA_APK | -1 | -1 | -1 | |||||
BraTS | CBICA_APR | -1 | -1 | -1 | |||||
BraTS | CBICA_APY | -1 | -1 | -1 | |||||
BraTS | CBICA_APZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQA | -1 | -1 | -1 | |||||
BraTS | CBICA_AQD | -1 | -1 | -1 | |||||
BraTS | CBICA_AQG | -1 | -1 | -1 | |||||
BraTS | CBICA_AQJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQN | -1 | -1 | -1 | |||||
BraTS | CBICA_AQO | -1 | -1 | -1 | |||||
BraTS | CBICA_AQP | -1 | -1 | -1 | |||||
BraTS | CBICA_AQQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQR | -1 | -1 | -1 | |||||
BraTS | CBICA_AQT | -1 | -1 | -1 | |||||
BraTS | CBICA_AQU | -1 | -1 | -1 | |||||
BraTS | CBICA_AQV | -1 | -1 | -1 | |||||
BraTS | CBICA_AQY | -1 | -1 | -1 | |||||
BraTS | CBICA_AQZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ARF | -1 | -1 | -1 | |||||
BraTS | CBICA_ARW | -1 | -1 | -1 | |||||
BraTS | CBICA_ARZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ASA | -1 | -1 | -1 | |||||
BraTS | CBICA_ASE | -1 | -1 | -1 | |||||
BraTS | CBICA_ASF | -1 | -1 | -1 | |||||
BraTS | CBICA_ASG | -1 | -1 | -1 | |||||
BraTS | CBICA_ASH | -1 | -1 | -1 | |||||
BraTS | CBICA_ASK | -1 | -1 | -1 | |||||
BraTS | CBICA_ASN | -1 | -1 | -1 | |||||
BraTS | CBICA_ASO | -1 | -1 | -1 | |||||
BraTS | CBICA_ASR | -1 | -1 | -1 | |||||
BraTS | CBICA_ASU | -1 | -1 | -1 | |||||
BraTS | CBICA_ASV | -1 | -1 | -1 | |||||
BraTS | CBICA_ASW | -1 | -1 | -1 | |||||
BraTS | CBICA_ASY | -1 | -1 | -1 | |||||
BraTS | CBICA_ATB | -1 | -1 | -1 | |||||
BraTS | CBICA_ATD | -1 | -1 | -1 | |||||
BraTS | CBICA_ATF | -1 | -1 | -1 | |||||
BraTS | CBICA_ATN | -1 | -1 | -1 | |||||
BraTS | CBICA_ATP | -1 | -1 | -1 | |||||
BraTS | CBICA_ATV | -1 | -1 | -1 | |||||
BraTS | CBICA_ATX | -1 | -1 | -1 | |||||
BraTS | CBICA_AUA | -1 | -1 | -1 | |||||
BraTS | CBICA_AUN | -1 | -1 | -1 | |||||
BraTS | CBICA_AUQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AUR | -1 | -1 | -1 | |||||
BraTS | CBICA_AUW | -1 | -1 | -1 | |||||
BraTS | CBICA_AUX | -1 | -1 | -1 | |||||
BraTS | CBICA_AVB | -1 | -1 | -1 | |||||
BraTS | CBICA_AVF | -1 | -1 | -1 | |||||
BraTS | CBICA_AVG | -1 | -1 | -1 | |||||
BraTS | CBICA_AVJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AVT | -1 | -1 | -1 | |||||
BraTS | CBICA_AVV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWG | -1 | -1 | -1 | |||||
BraTS | CBICA_AWH | -1 | -1 | -1 | |||||
BraTS | CBICA_AWI | -1 | -1 | -1 | |||||
BraTS | CBICA_AWV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWX | -1 | -1 | -1 | |||||
BraTS | CBICA_AXJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXL | -1 | -1 | -1 | |||||
BraTS | CBICA_AXM | -1 | -1 | -1 | |||||
BraTS | CBICA_AXN | -1 | -1 | -1 | |||||
BraTS | CBICA_AXO | -1 | -1 | -1 | |||||
BraTS | CBICA_AXQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXW | -1 | -1 | -1 | |||||
BraTS | CBICA_AYA | -1 | -1 | -1 | |||||
BraTS | CBICA_AYC | -1 | -1 | -1 | |||||
BraTS | CBICA_AYG | -1 | -1 | -1 | |||||
BraTS | CBICA_AYI | -1 | -1 | -1 | |||||
BraTS | CBICA_AYU | -1 | -1 | -1 | |||||
BraTS | CBICA_AYW | -1 | -1 | -1 | |||||
BraTS | CBICA_AZD | -1 | -1 | -1 | |||||
BraTS | CBICA_AZH | -1 | -1 | -1 | |||||
BraTS | CBICA_BAN | -1 | -1 | -1 | |||||
BraTS | CBICA_BAP | -1 | -1 | -1 | |||||
BraTS | CBICA_BAX | -1 | -1 | -1 | |||||
BraTS | CBICA_BBG | -1 | -1 | -1 | |||||
BraTS | CBICA_BCF | -1 | -1 | -1 | |||||
BraTS | CBICA_BCL | -1 | -1 | -1 | |||||
BraTS | CBICA_BDK | -1 | -1 | -1 | |||||
BraTS | CBICA_BEM | -1 | -1 | -1 | |||||
BraTS | CBICA_BFB | -1 | -1 | -1 | |||||
BraTS | CBICA_BFP | -1 | -1 | -1 | |||||
BraTS | CBICA_BGE | -1 | -1 | -1 | |||||
BraTS | CBICA_BGG | -1 | -1 | -1 | |||||
BraTS | CBICA_BGN | -1 | -1 | -1 | |||||
BraTS | CBICA_BGO | -1 | -1 | -1 | |||||
BraTS | CBICA_BGR | -1 | -1 | -1 | |||||
BraTS | CBICA_BGT | -1 | -1 | -1 | |||||
BraTS | CBICA_BGW | -1 | -1 | -1 | |||||
BraTS | CBICA_BGX | -1 | -1 | -1 | |||||
BraTS | CBICA_BHB | -1 | -1 | -1 | |||||
BraTS | CBICA_BHK | -1 | -1 | -1 | |||||
BraTS | CBICA_BHM | -1 | -1 | -1 | |||||
BraTS | CBICA_BHQ | -1 | -1 | -1 | |||||
BraTS | CBICA_BHV | -1 | -1 | -1 | |||||
BraTS | CBICA_BHZ | -1 | -1 | -1 | |||||
BraTS | CBICA_BIC | -1 | -1 | -1 | |||||
BraTS | CBICA_BJY | -1 | -1 | -1 | |||||
BraTS | CBICA_BKV | -1 | -1 | -1 | |||||
BraTS | CBICA_BLJ | -1 | -1 | -1 | |||||
BraTS | CBICA_BNR | -1 | -1 | -1 | |||||
BraTS | TMC_6290 | -1 | -1 | -1 | |||||
BraTS | TMC_6643 | -1 | -1 | -1 | |||||
BraTS | TMC_9043 | -1 | -1 | -1 | |||||
BraTS | TMC_11964 | -1 | -1 | -1 | |||||
BraTS | TMC_12866 | -1 | -1 | -1 | |||||
BraTS | TMC_15477 | -1 | -1 | -1 | |||||
BraTS | TMC_21360 | -1 | -1 | -1 | |||||
BraTS | TMC_27374 | -1 | -1 | -1 | |||||
BraTS | TMC_30014 | -1 | -1 | -1 | |||||
CPTAC-GBM | C3L-00016 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00019 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00265 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00278 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00349 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00424 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00429 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00506 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00528 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00591 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00631 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00636 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00671 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00674 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00677 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01045 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01046 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01142 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01156 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01327 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02041 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02465 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02504 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02704 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02706 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02707 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02708 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03260 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03266 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03727 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03728 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03747 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03748 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-04084 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00661 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00662 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00663 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00665 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01192 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01196 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01505 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01849 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01851 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01852 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02255 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02256 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02286 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03001 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03003 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03755 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-04686 | -1 | -1 | 4 | |||||
IvyGAP | W10 | 1 | 1 | 4 | |||||
IvyGAP | W11 | 0 | 0 | 4 | |||||
IvyGAP | W12 | 0 | 0 | 4 | |||||
IvyGAP | W13 | 0 | 0 | 4 | |||||
IvyGAP | W16 | 0 | 0 | 4 | |||||
IvyGAP | W18 | 0 | 0 | 4 | |||||
IvyGAP | W19 | 0 | 0 | 4 | |||||
IvyGAP | W1 | 0 | 0 | 4 | |||||
IvyGAP | W20 | 0 | 0 | 4 | |||||
IvyGAP | W21 | 0 | 0 | 4 | |||||
IvyGAP | W22 | 0 | 0 | -1 | |||||
IvyGAP | W26 | 0 | -1 | 4 | |||||
IvyGAP | W29 | 0 | 0 | 4 | |||||
IvyGAP | W2 | 0 | 1 | 4 | |||||
IvyGAP | W30 | 0 | 0 | 4 | |||||
IvyGAP | W31 | 1 | 1 | 4 | |||||
IvyGAP | W32 | 0 | 0 | 4 | |||||
IvyGAP | W33 | 0 | 0 | 4 | |||||
IvyGAP | W34 | 0 | 0 | 4 | |||||
IvyGAP | W35 | 1 | 0 | 3 | |||||
IvyGAP | W36 | 0 | 0 | 4 | |||||
IvyGAP | W38 | 0 | 0 | 4 | |||||
IvyGAP | W39 | 0 | 0 | 4 | |||||
IvyGAP | W3 | 1 | 0 | 4 | |||||
IvyGAP | W40 | 0 | 0 | 4 | |||||
IvyGAP | W42 | 0 | -1 | 4 | |||||
IvyGAP | W43 | 0 | -1 | 4 | |||||
IvyGAP | W45 | -1 | -1 | 4 | |||||
IvyGAP | W48 | 0 | -1 | 4 | |||||
IvyGAP | W4 | 1 | 0 | 4 | |||||
IvyGAP | W50 | 0 | -1 | 3 | |||||
IvyGAP | W53 | 1 | -1 | 4 | |||||
IvyGAP | W54 | 0 | -1 | 4 | |||||
IvyGAP | W55 | 0 | -1 | 4 | |||||
IvyGAP | W5 | 0 | 0 | 4 | |||||
IvyGAP | W6 | 0 | 0 | 4 | |||||
IvyGAP | W7 | 0 | 0 | 4 | |||||
IvyGAP | W8 | 0 | 0 | 4 | |||||
IvyGAP | W9 | 0 | 0 | 4 | |||||
REMBRANDT | 900-00-5299 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5303 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5308 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5316 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5317 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5332 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5339 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5341 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5342 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5346 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5380 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5381 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5382 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5385 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5396 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5404 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5412 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5414 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5458 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5459 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5462 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5468 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5476 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5477 | -1 | -1 | 2 | |||||
REMBRANDT | HF0763 | -1 | -1 | -1 | |||||
REMBRANDT | HF0828 | -1 | -1 | 3 | |||||
REMBRANDT | HF0835 | -1 | -1 | 2 | |||||
REMBRANDT | HF0855 | -1 | -1 | 2 | |||||
REMBRANDT | HF0868 | -1 | -1 | -1 | |||||
REMBRANDT | HF0883 | -1 | -1 | -1 | |||||
REMBRANDT | HF0899 | -1 | -1 | 2 | |||||
REMBRANDT | HF0920 | -1 | -1 | 2 | |||||
REMBRANDT | HF0931 | -1 | -1 | 2 | |||||
REMBRANDT | HF0953 | -1 | -1 | 2 | |||||
REMBRANDT | HF0960 | -1 | -1 | 2 | |||||
REMBRANDT | HF0966 | -1 | -1 | 3 | |||||
REMBRANDT | HF0986 | -1 | -1 | 4 | |||||
REMBRANDT | HF0990 | -1 | -1 | 4 | |||||
REMBRANDT | HF1000 | -1 | -1 | 2 | |||||
REMBRANDT | HF1058 | -1 | -1 | 4 | |||||
REMBRANDT | HF1059 | -1 | -1 | 3 | |||||
REMBRANDT | HF1071 | -1 | -1 | 4 | |||||
REMBRANDT | HF1077 | -1 | -1 | 4 | |||||
REMBRANDT | HF1078 | -1 | -1 | 4 | |||||
REMBRANDT | HF1097 | -1 | -1 | 4 | |||||
REMBRANDT | HF1113 | -1 | -1 | -1 | |||||
REMBRANDT | HF1122 | -1 | -1 | 4 | |||||
REMBRANDT | HF1136 | -1 | -1 | 3 | |||||
REMBRANDT | HF1139 | -1 | -1 | 4 | |||||
REMBRANDT | HF1150 | -1 | -1 | 3 | |||||
REMBRANDT | HF1156 | -1 | -1 | 2 | |||||
REMBRANDT | HF1167 | -1 | -1 | 2 | |||||
REMBRANDT | HF1185 | -1 | -1 | 3 | |||||
REMBRANDT | HF1191 | -1 | -1 | 4 | |||||
REMBRANDT | HF1199 | -1 | -1 | -1 | |||||
REMBRANDT | HF1219 | -1 | -1 | 3 | |||||
REMBRANDT | HF1227 | -1 | -1 | 2 | |||||
REMBRANDT | HF1232 | -1 | -1 | 3 | |||||
REMBRANDT | HF1235 | -1 | -1 | 2 | |||||
REMBRANDT | HF1242 | -1 | -1 | 3 | |||||
REMBRANDT | HF1246 | -1 | -1 | 2 | |||||
REMBRANDT | HF1264 | -1 | -1 | 2 | |||||
REMBRANDT | HF1269 | -1 | -1 | 4 | |||||
REMBRANDT | HF1280 | -1 | -1 | 3 | |||||
REMBRANDT | HF1292 | -1 | -1 | 4 | |||||
REMBRANDT | HF1293 | -1 | -1 | -1 | |||||
REMBRANDT | HF1297 | -1 | -1 | 4 | |||||
REMBRANDT | HF1300 | -1 | -1 | -1 | |||||
REMBRANDT | HF1307 | -1 | -1 | -1 | |||||
REMBRANDT | HF1316 | -1 | -1 | 2 | |||||
REMBRANDT | HF1318 | -1 | -1 | -1 | |||||
REMBRANDT | HF1325 | -1 | -1 | 2 | |||||
REMBRANDT | HF1331 | -1 | -1 | -1 | |||||
REMBRANDT | HF1334 | -1 | -1 | 2 | |||||
REMBRANDT | HF1344 | -1 | -1 | 2 | |||||
REMBRANDT | HF1345 | -1 | -1 | 2 | |||||
REMBRANDT | HF1357 | -1 | -1 | 3 | |||||
REMBRANDT | HF1381 | -1 | -1 | 2 | |||||
REMBRANDT | HF1397 | -1 | -1 | 4 | |||||
REMBRANDT | HF1398 | -1 | -1 | 3 | |||||
REMBRANDT | HF1407 | -1 | -1 | 2 | |||||
REMBRANDT | HF1409 | -1 | -1 | 3 | |||||
REMBRANDT | HF1420 | -1 | -1 | -1 | |||||
REMBRANDT | HF1429 | -1 | -1 | -1 | |||||
REMBRANDT | HF1433 | -1 | -1 | 2 | |||||
REMBRANDT | HF1437 | -1 | -1 | -1 | |||||
REMBRANDT | HF1442 | -1 | -1 | 2 | |||||
REMBRANDT | HF1458 | -1 | -1 | 3 | |||||
REMBRANDT | HF1463 | -1 | -1 | 2 | |||||
REMBRANDT | HF1489 | -1 | -1 | 2 | |||||
REMBRANDT | HF1490 | -1 | -1 | 3 | |||||
REMBRANDT | HF1493 | -1 | -1 | -1 | |||||
REMBRANDT | HF1510 | -1 | -1 | -1 | |||||
REMBRANDT | HF1511 | -1 | -1 | 2 | |||||
REMBRANDT | HF1517 | -1 | -1 | 4 | |||||
REMBRANDT | HF1538 | -1 | -1 | 4 | |||||
REMBRANDT | HF1551 | -1 | -1 | 2 | |||||
REMBRANDT | HF1553 | -1 | -1 | 2 | |||||
REMBRANDT | HF1560 | -1 | -1 | 4 | |||||
REMBRANDT | HF1568 | -1 | -1 | 2 | |||||
REMBRANDT | HF1587 | -1 | -1 | 3 | |||||
REMBRANDT | HF1588 | -1 | -1 | 2 | |||||
REMBRANDT | HF1606 | -1 | -1 | 2 | |||||
REMBRANDT | HF1613 | -1 | -1 | 3 | |||||
REMBRANDT | HF1628 | -1 | -1 | 4 | |||||
REMBRANDT | HF1652 | -1 | -1 | -1 | |||||
REMBRANDT | HF1677 | -1 | -1 | 2 | |||||
REMBRANDT | HF1702 | -1 | -1 | 3 | |||||
REMBRANDT | HF1708 | -1 | -1 | 2 | |||||
TCGA-GBM | TCGA-02-0003 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0006 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0009 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0011 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0027 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0033 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0034 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0037 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0046 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0047 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0048 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0054 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0059 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0060 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0064 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0068 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0069 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0070 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0075 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0085 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0086 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0087 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0102 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0106 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0116 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0119 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0122 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0128 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0130 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0132 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0133 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0137 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0138 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0139 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0142 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0145 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0149 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0154 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0158 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0162 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0164 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0166 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0168 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0175 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0176 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0177 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0179 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0182 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0184 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0185 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0187 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0188 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0189 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0190 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0192 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0213 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0238 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0240 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0241 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0644 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0646 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0648 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0649 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1084 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1802 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-2570 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5408 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5412 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5413 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5417 | 1 | -1 | 4 | |||||
TCGA-GBM | TCGA-06-6389 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0350 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0352 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0353 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0354 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0355 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0356 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0357 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0358 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0359 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0360 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0385 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0389 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0392 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0512 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0520 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0521 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0522 | -1 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0524 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0529 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0616 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0776 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1093 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1094 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1098 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1598 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1601 | 0 | -1 | -1 | |||||
TCGA-GBM | TCGA-12-1602 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-3650 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-0789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1456 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1794 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1825 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-3477 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-0963 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2624 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2631 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5951 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5954 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5958 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5960 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1834 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1838 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-2526 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4932 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-76-4934 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4935 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6191 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6193 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6280 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6282 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6285 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6656 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6657 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6661 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6662 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6663 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6664 | 0 | 0 | 4 | |||||
TCGA-LGG | TCGA-CS-4941 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4942 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4944 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5393 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-5395 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5396 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-CS-5397 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6186 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6188 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6290 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6665 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6666 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6667 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-6668 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-CS-6669 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5849 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-5851 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5852 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5854 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5871 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5874 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6397 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6399 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6400 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6404 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6405 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6407 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6410 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6542 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7008 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7010 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7014 | -1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7015 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7018 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7019 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7294 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-7298 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7300 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7302 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7309 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8162 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8164 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-8165 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8167 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8168 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-EZ-7265A | -1 | -1 | -1 | |||||
TCGA-LGG | TCGA-FG-5964 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-6688 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6691 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6692 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-7643 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-A713 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7473 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7616 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7680 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7684 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7686 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7692 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7693 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7694 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7856 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7860 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7874 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7879 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7882 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8105 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-8106 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8107 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8111 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8114 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8563 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A614 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0 | 2 |
Data_collection | Patient | IDH_mutated | Prediction_score_IDH_wildtype | Prediction_score_IDH_mutated | 1p19q_codeleted | Prediction_score_1p19q_codeleted | Prediction_score_1p19q_intact | Grade | Prediction_score_grade_2 | Prediction_score_grade_3 | Prediction_score_grade_4 | ||||||||||||
TCGA-GBM | TCGA-02-0003 | 0 | 099998915 | 10867886E-05 | 0 | 099996686 | 3308471E-05 | 4 | 7377526E-05 | 000074111245 | 099918514 | ||||||||||||
TCGA-GBM | TCGA-02-0006 | 0 | 042321962 | 05767803 | 0 | 068791837 | 031208166 | 4 | 060229343 | 026596427 | 013174225 | ||||||||||||
TCGA-GBM | TCGA-02-0009 | 0 | 099306935 | 0006930672 | 0 | 09906961 | 0009303949 | 4 | 0056565534 | 010282235 | 08406121 | ||||||||||||
TCGA-GBM | TCGA-02-0011 | 0 | 013531776 | 08646823 | 0 | 085318035 | 01468197 | 4 | 0015055533 | 092510724 | 005983725 | ||||||||||||
TCGA-GBM | TCGA-02-0027 | 0 | 09997279 | 000027212297 | 0 | 09986827 | 00013172914 | 4 | 00016104137 | 00038575265 | 0994532 | ||||||||||||
TCGA-GBM | TCGA-02-0033 | 0 | 099974436 | 000025564007 | 0 | 099940693 | 0000593021 | 4 | 00020670628 | 0003761288 | 09941717 | ||||||||||||
TCGA-GBM | TCGA-02-0034 | 0 | 091404164 | 008595832 | 0 | 089209336 | 01079066 | 4 | 00116944825 | 0061110377 | 092719513 | ||||||||||||
TCGA-GBM | TCGA-02-0037 | 0 | 09999577 | 42315594E-05 | 0 | 099992716 | 72827526E-05 | 4 | 82080274E-05 | 0009249337 | 09906686 | ||||||||||||
TCGA-GBM | TCGA-02-0046 | 0 | 0999129 | 00008710656 | 0 | 09989637 | 00010362669 | 4 | 0004290756 | 0022799779 | 097290945 | ||||||||||||
TCGA-GBM | TCGA-02-0047 | 0 | 099991703 | 83008505E-05 | 0 | 09999292 | 70863265E-05 | 4 | 000016252015 | 0040118434 | 095971906 | ||||||||||||
TCGA-GBM | TCGA-02-0048 | 0 | 09998785 | 000012148175 | 0 | 099959475 | 000040527192 | 4 | 00002215901 | 000039696065 | 09993814 | ||||||||||||
TCGA-GBM | TCGA-02-0054 | 0 | 09999831 | 1689829E-05 | 0 | 09999442 | 5583975E-05 | 4 | 00010063206 | 0060579527 | 093841416 | ||||||||||||
TCGA-GBM | TCGA-02-0059 | -1 | 09993749 | 000062511285 | 0 | 09996424 | 00003576683 | 4 | 00007046657 | 0010920537 | 09883748 | ||||||||||||
TCGA-GBM | TCGA-02-0060 | 0 | 07197039 | 028029615 | 0 | 09016612 | 009833879 | 4 | 017739706 | 03728545 | 04497484 | ||||||||||||
TCGA-GBM | TCGA-02-0064 | 0 | 09999083 | 9170197E-05 | 0 | 09995073 | 000049264234 | 4 | 000043781495 | 00028024286 | 099675983 | ||||||||||||
TCGA-GBM | TCGA-02-0068 | 0 | 099187535 | 0008124709 | 0 | 099528164 | 00047183693 | 4 | 00030539853 | 059695286 | 039999318 | ||||||||||||
TCGA-GBM | TCGA-02-0069 | 0 | 09890871 | 0010912909 | 0 | 099704784 | 0002952148 | 4 | 00057067247 | 0061368063 | 09329252 | ||||||||||||
TCGA-GBM | TCGA-02-0070 | 0 | 09940659 | 00059340666 | 0 | 0957794 | 0042206 | 4 | 0008216515 | 003556913 | 09562143 | ||||||||||||
TCGA-GBM | TCGA-02-0075 | 0 | 099933076 | 00006693099 | 0 | 099735296 | 00026470982 | 4 | 000044697264 | 00035736929 | 09959793 | ||||||||||||
TCGA-GBM | TCGA-02-0085 | 0 | 099114406 | 0008855922 | 0 | 09756698 | 002433019 | 4 | 00065203947 | 0035171553 | 095830804 | ||||||||||||
TCGA-GBM | TCGA-02-0086 | 0 | 099965334 | 000034666777 | 0 | 0998698 | 00013019645 | 4 | 000032699382 | 00018025768 | 099787045 | ||||||||||||
TCGA-GBM | TCGA-02-0087 | -1 | 09974885 | 00025114634 | 0 | 09990638 | 000093628286 | 4 | 0007505083 | 0008562708 | 098393226 | ||||||||||||
TCGA-GBM | TCGA-02-0102 | 0 | 09797647 | 0020235319 | 0 | 098292196 | 0017078074 | 4 | 003512482 | 03901857 | 05746895 | ||||||||||||
TCGA-GBM | TCGA-02-0106 | -1 | 099993694 | 6302759E-05 | 0 | 099980897 | 000019110431 | 4 | 60797247E-05 | 00008735659 | 09990657 | ||||||||||||
TCGA-GBM | TCGA-02-0116 | 0 | 09999778 | 22125667E-05 | 0 | 09996886 | 00003113695 | 4 | 000015498884 | 000051770627 | 09993273 | ||||||||||||
TCGA-GBM | TCGA-06-0119 | 0 | 09999362 | 63770494E-05 | 0 | 09999355 | 6452215E-05 | 4 | 000013225728 | 00028902534 | 099697745 | ||||||||||||
TCGA-GBM | TCGA-06-0122 | 0 | 09915298 | 0008470196 | 0 | 09859093 | 00140907345 | 4 | 00121390615 | 027333176 | 071452916 | ||||||||||||
TCGA-GBM | TCGA-06-0128 | 1 | 099988174 | 000011820537 | 0 | 099980634 | 000019373452 | 4 | 000016409029 | 0007865882 | 099197 | ||||||||||||
TCGA-GBM | TCGA-06-0130 | 0 | 099998784 | 12123987E-05 | 0 | 09999323 | 6775062E-05 | 4 | 80872844E-05 | 00026260202 | 099729306 | ||||||||||||
TCGA-GBM | TCGA-06-0132 | 0 | 09998566 | 000014341719 | 0 | 099988496 | 000011501736 | 4 | 000072843547 | 0005115947 | 099415565 | ||||||||||||
TCGA-GBM | TCGA-06-0133 | 0 | 097782 | 002218004 | 0 | 0993807 | 00061929906 | 4 | 0026753133 | 004659919 | 09266477 | ||||||||||||
TCGA-GBM | TCGA-06-0137 | 0 | 096448904 | 003551094 | 0 | 099403125 | 0005968731 | 4 | 000649511 | 038909483 | 060441005 | ||||||||||||
TCGA-GBM | TCGA-06-0138 | 0 | 09977743 | 00022256707 | 0 | 099736834 | 0002631674 | 4 | 00032954598 | 0011606657 | 09850979 | ||||||||||||
TCGA-GBM | TCGA-06-0139 | 0 | 09992649 | 00007350447 | 0 | 099898964 | 00010103129 | 4 | 00021781863 | 00069256434 | 099089617 | ||||||||||||
TCGA-GBM | TCGA-06-0142 | 0 | 099909425 | 00009057334 | 0 | 09985896 | 00014103584 | 4 | 0002598974 | 0046451908 | 09509491 | ||||||||||||
TCGA-GBM | TCGA-06-0145 | 0 | 099964654 | 000035350278 | 0 | 0999652 | 000034802416 | 4 | 00009068022 | 0021991275 | 0977102 | ||||||||||||
TCGA-GBM | TCGA-06-0149 | -1 | 09992161 | 00007839425 | 0 | 09981067 | 00018932257 | 4 | 00057726577 | 0013888515 | 09803388 | ||||||||||||
TCGA-GBM | TCGA-06-0154 | 0 | 099968064 | 000031937403 | 0 | 0999729 | 000027106237 | 4 | 000041507537 | 023430935 | 07652756 | ||||||||||||
TCGA-GBM | TCGA-06-0158 | 0 | 09999199 | 8014118E-05 | 0 | 099992514 | 74846226E-05 | 4 | 00026547876 | 020762624 | 0789719 | ||||||||||||
TCGA-GBM | TCGA-06-0162 | -1 | 099964297 | 00003569706 | 0 | 09997459 | 0000254147 | 4 | 000033955855 | 004318936 | 09564711 | ||||||||||||
TCGA-GBM | TCGA-06-0164 | -1 | 09983991 | 00016009645 | 0 | 09873262 | 0012673735 | 4 | 00016517473 | 00048346478 | 09935136 | ||||||||||||
TCGA-GBM | TCGA-06-0166 | 0 | 099991715 | 82846556E-05 | 0 | 0999554 | 00004459562 | 4 | 000013499439 | 0011635037 | 098823 | ||||||||||||
TCGA-GBM | TCGA-06-0168 | 0 | 09975561 | 00024438864 | 0 | 09964825 | 00035174883 | 4 | 0004766434 | 010448053 | 089075303 | ||||||||||||
TCGA-GBM | TCGA-06-0175 | -1 | 09996252 | 000037482675 | 0 | 09988098 | 00011902251 | 4 | 00026097735 | 004992068 | 094746953 | ||||||||||||
TCGA-GBM | TCGA-06-0176 | 0 | 099550986 | 00044901576 | 0 | 09998872 | 000011279297 | 4 | 0032868527 | 036690876 | 06002227 | ||||||||||||
TCGA-GBM | TCGA-06-0177 | -1 | 081774735 | 018225263 | 0 | 09946464 | 00053536464 | 4 | 0026683953 | 013013016 | 08431859 | ||||||||||||
TCGA-GBM | TCGA-06-0179 | -1 | 09997508 | 000024923254 | 0 | 09989778 | 00010222099 | 4 | 0002628482 | 0004127114 | 099324447 | ||||||||||||
TCGA-GBM | TCGA-06-0182 | -1 | 099999547 | 45838406E-06 | 0 | 099998736 | 12656287E-05 | 4 | 00002591103 | 000018499703 | 09995559 | ||||||||||||
TCGA-GBM | TCGA-06-0184 | 0 | 09935369 | 00064631375 | 0 | 099458355 | 00054164114 | 4 | 0023110552 | 0017436244 | 09594532 | ||||||||||||
TCGA-GBM | TCGA-06-0185 | 0 | 09999337 | 66310655E-05 | 0 | 099986255 | 000013738607 | 4 | 7657532E-05 | 0016089642 | 098383385 | ||||||||||||
TCGA-GBM | TCGA-06-0187 | 0 | 09991689 | 00008312097 | 0 | 099700147 | 00029984985 | 4 | 00020616595 | 0033111423 | 096482694 | ||||||||||||
TCGA-GBM | TCGA-06-0188 | 0 | 09883802 | 0011619771 | 0 | 09826743 | 0017325714 | 4 | 0013776424 | 0112841725 | 087338185 | ||||||||||||
TCGA-GBM | TCGA-06-0189 | 0 | 099906737 | 0000932636 | 0 | 09983865 | 00016135005 | 4 | 00022760795 | 00106745735 | 09870494 | ||||||||||||
TCGA-GBM | TCGA-06-0190 | 0 | 099954176 | 000045831292 | 0 | 09967013 | 00032986512 | 4 | 000040555766 | 0001246768 | 099834764 | ||||||||||||
TCGA-GBM | TCGA-06-0192 | 0 | 09997876 | 00002123566 | 0 | 09992735 | 00007264875 | 4 | 00004505576 | 00014473333 | 09981021 | ||||||||||||
TCGA-GBM | TCGA-06-0213 | 0 | 099986935 | 00001305845 | 0 | 099971646 | 000028351307 | 4 | 8755587E-05 | 00013480412 | 09985644 | ||||||||||||
TCGA-GBM | TCGA-06-0238 | 0 | 09999982 | 17603431E-06 | 0 | 09999894 | 10616134E-05 | 4 | 8076515E-05 | 56053756E-05 | 099986315 | ||||||||||||
TCGA-GBM | TCGA-06-0240 | 0 | 09989956 | 00010044163 | 0 | 099948466 | 00005152657 | 4 | 00016040986 | 021931975 | 077907616 | ||||||||||||
TCGA-GBM | TCGA-06-0241 | 0 | 099959785 | 000040211933 | 0 | 099910825 | 00008917038 | 4 | 00023411359 | 0007850656 | 098980826 | ||||||||||||
TCGA-GBM | TCGA-06-0644 | 0 | 09871044 | 0012895588 | 0 | 09859228 | 00140771745 | 4 | 0013671214 | 009819665 | 088813215 | ||||||||||||
TCGA-GBM | TCGA-06-0646 | 0 | 099959 | 00004100472 | 0 | 099936503 | 000063495064 | 4 | 00019223108 | 0040443853 | 095763385 | ||||||||||||
TCGA-GBM | TCGA-06-0648 | 0 | 09999709 | 29083441E-05 | 0 | 099982435 | 000017571273 | 4 | 000077678583 | 000038868992 | 099883455 | ||||||||||||
TCGA-GBM | TCGA-06-0649 | 0 | 09997805 | 000021952427 | 0 | 099951684 | 000048311835 | 4 | 0042641632 | 00058432207 | 095151514 | ||||||||||||
TCGA-GBM | TCGA-06-1084 | 0 | 099985826 | 000014174655 | 0 | 099968565 | 00003144242 | 4 | 00002676724 | 020492287 | 079480946 | ||||||||||||
TCGA-GBM | TCGA-06-1802 | -1 | 09991928 | 00008072305 | 0 | 09956176 | 0004382337 | 4 | 000043478087 | 00019495043 | 09976157 | ||||||||||||
TCGA-GBM | TCGA-06-2570 | 1 | 096841115 | 0031588882 | 0 | 09842457 | 0015754245 | 4 | 0015369608 | 0030956635 | 09536738 | ||||||||||||
TCGA-GBM | TCGA-06-5408 | 0 | 099857306 | 00014269598 | 0 | 09962638 | 00037362208 | 4 | 00027690146 | 0016195394 | 098103565 | ||||||||||||
TCGA-GBM | TCGA-06-5412 | 0 | 099366105 | 0006338921 | 0 | 099193794 | 0008061992 | 4 | 0011476759 | 006606435 | 09224589 | ||||||||||||
TCGA-GBM | TCGA-06-5413 | 0 | 09994105 | 000058955856 | 0 | 09983026 | 00016974095 | 4 | 00027100197 | 0021083053 | 097620696 | ||||||||||||
TCGA-GBM | TCGA-06-5417 | 1 | 01521267 | 08478733 | -1 | 03064492 | 06935508 | 4 | 013736826 | 037757674 | 048505494 | ||||||||||||
TCGA-GBM | TCGA-06-6389 | 1 | 099987435 | 000012558252 | 0 | 09997017 | 000029827762 | 4 | 00014020519 | 00020044278 | 099659353 | ||||||||||||
TCGA-GBM | TCGA-08-0350 | 0 | 019229275 | 08077072 | 0 | 0033211168 | 09667888 | 4 | 0051619414 | 022280572 | 072557485 | ||||||||||||
TCGA-GBM | TCGA-08-0352 | 0 | 099997497 | 25071595E-05 | 0 | 099992514 | 74846226E-05 | 4 | 000024192198 | 000048111935 | 099927694 | ||||||||||||
TCGA-GBM | TCGA-08-0353 | 0 | 09901496 | 0009850325 | 0 | 09967775 | 0003222484 | 4 | 00053748637 | 0004291497 | 09903336 | ||||||||||||
TCGA-GBM | TCGA-08-0354 | 0 | 076413894 | 023586108 | 0 | 07554566 | 024454337 | 4 | 008784444 | 02004897 | 071166587 | ||||||||||||
TCGA-GBM | TCGA-08-0355 | 0 | 09998349 | 000016506859 | 0 | 099984336 | 000015659066 | 4 | 000076689845 | 0023648744 | 09755844 | ||||||||||||
TCGA-GBM | TCGA-08-0356 | 0 | 097673583 | 0023264103 | 0 | 097773504 | 0022264915 | 4 | 001175834 | 0031075679 | 095716596 | ||||||||||||
TCGA-GBM | TCGA-08-0357 | 0 | 099509466 | 0004905406 | 0 | 099300176 | 00069982093 | 4 | 0005191745 | 0038681854 | 095612645 | ||||||||||||
TCGA-GBM | TCGA-08-0358 | 0 | 099999785 | 2199356E-06 | 0 | 099999034 | 9628425E-06 | 4 | 6113315E-06 | 00011283219 | 09988656 | ||||||||||||
TCGA-GBM | TCGA-08-0359 | 0 | 097885466 | 0021145396 | 0 | 09956006 | 00043994132 | 4 | 0009885523 | 0066605434 | 092350906 | ||||||||||||
TCGA-GBM | TCGA-08-0360 | 0 | 09922444 | 00077555366 | -1 | 09948704 | 00051296344 | 4 | 0013318472 | 003317344 | 095350814 | ||||||||||||
TCGA-GBM | TCGA-08-0385 | 0 | 099605453 | 00039454065 | -1 | 099686414 | 0003135836 | 4 | 00050293226 | 0029977333 | 096499336 | ||||||||||||
TCGA-GBM | TCGA-08-0389 | 0 | 099964714 | 000035281325 | 0 | 09991272 | 000087276706 | 4 | 00017554013 | 00024730961 | 099577147 | ||||||||||||
TCGA-GBM | TCGA-08-0390 | 0 | 099945146 | 000054847915 | 0 | 099936 | 00006399274 | 4 | 00036811908 | 00050958768 | 0991223 | ||||||||||||
TCGA-GBM | TCGA-08-0392 | 0 | 099962366 | 000037629317 | 0 | 09993575 | 00006424303 | 4 | 000036593352 | 0010291994 | 09893421 | ||||||||||||
TCGA-GBM | TCGA-08-0512 | -1 | 09982893 | 00017106998 | 0 | 099193794 | 0008061992 | 4 | 00016200381 | 00027773918 | 09956026 | ||||||||||||
TCGA-GBM | TCGA-08-0520 | -1 | 099603915 | 00039607873 | 0 | 09981933 | 00018066854 | 4 | 00007140295 | 0019064669 | 09802213 | ||||||||||||
TCGA-GBM | TCGA-08-0521 | -1 | 09975274 | 0002472623 | 0 | 099490017 | 00050998176 | 4 | 0001514669 | 0020103427 | 09783819 | ||||||||||||
TCGA-GBM | TCGA-08-0522 | -1 | 099960107 | 000039899128 | -1 | 09992053 | 00007947255 | 4 | 0000269389 | 0006173321 | 09935573 | ||||||||||||
TCGA-GBM | TCGA-08-0524 | -1 | 09964619 | 0003538086 | 0 | 099620515 | 0003794834 | 4 | 000019140428 | 0010096702 | 09897119 | ||||||||||||
TCGA-GBM | TCGA-08-0529 | -1 | 09996567 | 000034329997 | 0 | 099952066 | 00004793605 | 4 | 000032077235 | 0035970636 | 09637086 | ||||||||||||
TCGA-GBM | TCGA-12-0616 | 0 | 098521465 | 0014785408 | 0 | 098704207 | 001295789 | 4 | 001592791 | 012875569 | 08553164 | ||||||||||||
TCGA-GBM | TCGA-12-0776 | -1 | 099899167 | 00010083434 | 0 | 09987031 | 00012968953 | 4 | 0019219175 | 00637484 | 09170324 | ||||||||||||
TCGA-GBM | TCGA-12-0829 | 0 | 099913067 | 00008693674 | 0 | 099821776 | 00017821962 | 4 | 00021031094 | 0055067167 | 09428297 | ||||||||||||
TCGA-GBM | TCGA-12-1093 | 0 | 099992585 | 7411892E-05 | 0 | 09999448 | 5518923E-05 | 4 | 000046803855 | 0012115157 | 098741674 | ||||||||||||
TCGA-GBM | TCGA-12-1094 | -1 | 09980045 | 00019955388 | 0 | 09866105 | 0013389497 | 4 | 00053194338 | 001599471 | 097868586 | ||||||||||||
TCGA-GBM | TCGA-12-1098 | -1 | 09998406 | 000015936712 | 0 | 09977216 | 00022783307 | 4 | 000010218692 | 0035607774 | 09642901 | ||||||||||||
TCGA-GBM | TCGA-12-1598 | 0 | 096309197 | 0036908068 | 0 | 097933435 | 0020665688 | 4 | 0012952217 | 052912676 | 045792103 | ||||||||||||
TCGA-GBM | TCGA-12-1601 | 0 | 09875683 | 0012431651 | -1 | 0991891 | 0008108984 | -1 | 00118053425 | 0105477065 | 088271755 | ||||||||||||
TCGA-GBM | TCGA-12-1602 | 0 | 099830914 | 00016908031 | 0 | 099858415 | 00014158705 | 4 | 0008427611 | 0025996923 | 09655755 | ||||||||||||
TCGA-GBM | TCGA-12-3650 | 0 | 09761519 | 0023848088 | 0 | 097467697 | 0025323058 | 4 | 0010450666 | 043705726 | 05524921 | ||||||||||||
TCGA-GBM | TCGA-14-0789 | 0 | 099856466 | 00014353332 | 0 | 099666256 | 00033374047 | 4 | 0001406897 | 0008273975 | 099031913 | ||||||||||||
TCGA-GBM | TCGA-14-1456 | 1 | 006299064 | 093700933 | 0 | 08656222 | 013437784 | 4 | 016490369 | 047177824 | 036331803 | ||||||||||||
TCGA-GBM | TCGA-14-1794 | 0 | 08579393 | 014206071 | 0 | 09850429 | 0014957087 | 4 | 0023009384 | 009868736 | 08783033 | ||||||||||||
TCGA-GBM | TCGA-14-1825 | 0 | 099960107 | 000039899128 | 0 | 099968123 | 00003187511 | 4 | 0008552247 | 0010156045 | 09812918 | ||||||||||||
TCGA-GBM | TCGA-14-1829 | 0 | 090690076 | 009309922 | 0 | 09907856 | 0009214366 | 4 | 0008461936 | 0102735735 | 088880235 | ||||||||||||
TCGA-GBM | TCGA-14-3477 | 0 | 099796116 | 0002038787 | 0 | 09990728 | 00009271923 | 4 | 00032272525 | 0021644868 | 09751279 | ||||||||||||
TCGA-GBM | TCGA-19-0963 | -1 | 099876726 | 00012327607 | 0 | 09983612 | 00016388679 | 4 | 00031698826 | 013153598 | 086529416 | ||||||||||||
TCGA-GBM | TCGA-19-1390 | 0 | 099913234 | 00008676725 | 0 | 099703634 | 00029636684 | 4 | 00015592943 | 0026028048 | 097241265 | ||||||||||||
TCGA-GBM | TCGA-19-1789 | 0 | 09809491 | 00190509 | 0 | 09915216 | 0008478402 | 4 | 0038703684 | 014341596 | 08178804 | ||||||||||||
TCGA-GBM | TCGA-19-2624 | 0 | 07535573 | 024644265 | 0 | 09816127 | 0018387254 | 4 | 012311598 | 012769651 | 07491875 | ||||||||||||
TCGA-GBM | TCGA-19-2631 | 0 | 099860877 | 0001391234 | 0 | 09981178 | 00018821858 | 4 | 00009839778 | 001843531 | 09805807 | ||||||||||||
TCGA-GBM | TCGA-19-5951 | 0 | 09999031 | 9685608E-05 | 0 | 099977034 | 000022960825 | 4 | 00020246736 | 0004014765 | 09939606 | ||||||||||||
TCGA-GBM | TCGA-19-5954 | 0 | 099456257 | 00054374957 | 0 | 09968273 | 00031726828 | 4 | 00073725334 | 006310084 | 09295266 | ||||||||||||
TCGA-GBM | TCGA-19-5958 | 0 | 099999475 | 5234907E-06 | 0 | 0999941 | 58978338E-05 | 4 | 35422294E-05 | 86819025E-05 | 09998777 | ||||||||||||
TCGA-GBM | TCGA-19-5960 | 0 | 09683962 | 003160382 | 0 | 09013577 | 009864227 | 4 | 0011394806 | 018114014 | 08074651 | ||||||||||||
TCGA-GBM | TCGA-27-1834 | 0 | 099998164 | 18342893E-05 | 0 | 09999685 | 31446623E-05 | 4 | 8611921E-05 | 000031686216 | 0999597 | ||||||||||||
TCGA-GBM | TCGA-27-1838 | 0 | 09993625 | 000063743413 | 0 | 09940428 | 0005957154 | 4 | 00006736379 | 0007191195 | 099213517 | ||||||||||||
TCGA-GBM | TCGA-27-2526 | 0 | 099983776 | 000016219281 | 0 | 09996898 | 000031015594 | 4 | 000016658282 | 00006323714 | 09992011 | ||||||||||||
TCGA-GBM | TCGA-76-4932 | 0 | 09867389 | 0013261103 | -1 | 09949397 | 0005060332 | 4 | 00007321126 | 0003016794 | 099625117 | ||||||||||||
TCGA-GBM | TCGA-76-4934 | 0 | 099318933 | 0006810731 | 0 | 09995073 | 000049264234 | 4 | 00061555947 | 00070025027 | 098684186 | ||||||||||||
TCGA-GBM | TCGA-76-4935 | 0 | 074562997 | 025437003 | 0 | 098242307 | 001757688 | 4 | 076535034 | 006437644 | 017027317 | ||||||||||||
TCGA-GBM | TCGA-76-6191 | 0 | 09981067 | 00018932257 | 0 | 09970879 | 00029121784 | 4 | 00044340584 | 00096095055 | 098595643 | ||||||||||||
TCGA-GBM | TCGA-76-6193 | 0 | 09966168 | 0003383191 | 0 | 099850464 | 00014953383 | 4 | 00037061477 | 007873953 | 09175543 | ||||||||||||
TCGA-GBM | TCGA-76-6280 | 0 | 099948776 | 00005122569 | 0 | 099908185 | 000091819017 | 4 | 00001475792 | 000846075 | 099139166 | ||||||||||||
TCGA-GBM | TCGA-76-6282 | 0 | 0995906 | 0004093958 | 0 | 099861956 | 00013804223 | 4 | 00006694951 | 0009437619 | 09898929 | ||||||||||||
TCGA-GBM | TCGA-76-6285 | 0 | 099949074 | 00005092657 | 0 | 09971661 | 00028338495 | 4 | 00031175872 | 004005614 | 095682627 | ||||||||||||
TCGA-GBM | TCGA-76-6656 | 0 | 09996917 | 000030834455 | 0 | 09983897 | 00016103574 | 4 | 002648366 | 00017969633 | 09717193 | ||||||||||||
TCGA-GBM | TCGA-76-6657 | 0 | 099987245 | 000012755992 | 0 | 099951494 | 00004850083 | 4 | 000096620515 | 0005599633 | 09934342 | ||||||||||||
TCGA-GBM | TCGA-76-6661 | 0 | 093211424 | 006788577 | 0 | 09640178 | 0035982177 | 4 | 003490037 | 0026863772 | 09382358 | ||||||||||||
TCGA-GBM | TCGA-76-6662 | 0 | 096425414 | 0035745807 | 0 | 09963924 | 0003607617 | 4 | 002845819 | 002544755 | 09460942 | ||||||||||||
TCGA-GBM | TCGA-76-6663 | 0 | 088664144 | 0113358565 | 0 | 09984207 | 00015792594 | 4 | 0010206689 | 043740335 | 05523899 | ||||||||||||
TCGA-GBM | TCGA-76-6664 | 0 | 011047115 | 08895289 | 0 | 09559813 | 004401865 | 4 | 00049677677 | 08806894 | 011434281 | ||||||||||||
TCGA-LGG | TCGA-CS-4941 | 0 | 088931274 | 011068726 | 0 | 087037706 | 012962292 | 3 | 002865127 | 0048591908 | 092275685 | ||||||||||||
TCGA-LGG | TCGA-CS-4942 | 1 | 00031327847 | 099686724 | 0 | 096309197 | 0036908068 | 3 | 096261597 | 00148612335 | 0022522787 | ||||||||||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0005265965 | 099473405 | 0 | 09940544 | 00059455987 | 3 | 09439103 | 0023049146 | 003304057 | ||||||||||||
TCGA-LGG | TCGA-CS-4944 | 1 | 009363656 | 09063635 | 0 | 08755211 | 0124478824 | 2 | 034047556 | 033881712 | 03207073 | ||||||||||||
TCGA-LGG | TCGA-CS-5393 | 1 | 009623762 | 09037624 | 0 | 098178816 | 001821182 | 3 | 014111634 | 042021698 | 043866673 | ||||||||||||
TCGA-LGG | TCGA-CS-5395 | 0 | 08502822 | 014971776 | 0 | 09932025 | 00067975316 | 2 | 0052374925 | 018397054 | 076365453 | ||||||||||||
TCGA-LGG | TCGA-CS-5396 | 1 | 099839586 | 00016040892 | 1 | 099967945 | 000032062363 | 3 | 00016345463 | 029090768 | 07074577 | ||||||||||||
TCGA-LGG | TCGA-CS-5397 | 0 | 049304244 | 050695753 | 0 | 08829839 | 0117016025 | 3 | 038702008 | 021211159 | 040086827 | ||||||||||||
TCGA-LGG | TCGA-CS-6186 | 0 | 099913234 | 00008676725 | 0 | 099956185 | 000043818905 | 3 | 00008662089 | 016898473 | 083014905 | ||||||||||||
TCGA-LGG | TCGA-CS-6188 | 0 | 052768165 | 047231838 | 0 | 08584221 | 014157787 | 3 | 019437431 | 047675493 | 03288707 | ||||||||||||
TCGA-LGG | TCGA-CS-6290 | 1 | 09102666 | 008973339 | 0 | 09462997 | 0053700306 | 3 | 0104100704 | 025633416 | 06395651 | ||||||||||||
TCGA-LGG | TCGA-CS-6665 | 1 | 099600047 | 0003999501 | 0 | 099756086 | 00024391294 | 3 | 0011873978 | 001634113 | 097178483 | ||||||||||||
TCGA-LGG | TCGA-CS-6666 | 1 | 021655986 | 07834402 | 0 | 09327296 | 0067270435 | 3 | 017667453 | 036334327 | 045998225 | ||||||||||||
TCGA-LGG | TCGA-CS-6667 | 1 | 012061995 | 087938 | 0 | 095699733 | 0043002643 | 2 | 063733935 | 019323014 | 016943048 | ||||||||||||
TCGA-LGG | TCGA-CS-6668 | 1 | 0076787576 | 09232124 | 1 | 04240933 | 057590663 | 2 | 06810894 | 013706882 | 018184178 | ||||||||||||
TCGA-LGG | TCGA-CS-6669 | 0 | 08488156 | 01511844 | 0 | 094018847 | 005981148 | 2 | 0037862387 | 002352077 | 09386168 | ||||||||||||
TCGA-LGG | TCGA-DU-5849 | 1 | 005773187 | 094226813 | 1 | 08664153 | 013358466 | 2 | 072753835 | 015028271 | 012217898 | ||||||||||||
TCGA-LGG | TCGA-DU-5851 | 1 | 09963994 | 0003600603 | 0 | 099808073 | 00019192374 | 3 | 00060602655 | 012558761 | 08683521 | ||||||||||||
TCGA-LGG | TCGA-DU-5852 | 0 | 09998591 | 000014091856 | 0 | 099954873 | 000045121062 | 3 | 0002267452 | 00038046916 | 099392784 | ||||||||||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0010986943 | 09890131 | 0 | 09549844 | 0045015533 | 2 | 08603989 | 0077804394 | 0061796777 | ||||||||||||
TCGA-LGG | TCGA-DU-5854 | 0 | 09567354 | 0043264627 | 0 | 098768765 | 0012312326 | 3 | 01194655 | 027027336 | 06102612 | ||||||||||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0009312956 | 09906871 | 0 | 046602532 | 053397465 | 3 | 0008289882 | 097042197 | 0021288157 | ||||||||||||
TCGA-LGG | TCGA-DU-5871 | 1 | 005623634 | 09437636 | 0 | 09449439 | 005505607 | 2 | 042517176 | 020180763 | 037302068 | ||||||||||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0062359583 | 09376405 | 0 | 015278916 | 08472108 | 2 | 012133307 | 048199505 | 039667192 | ||||||||||||
TCGA-LGG | TCGA-DU-5874 | 1 | 022858672 | 077141327 | 1 | 06457066 | 03542934 | 2 | 058503634 | 020639434 | 02085693 | ||||||||||||
TCGA-LGG | TCGA-DU-6397 | 1 | 097691274 | 002308724 | 1 | 09908213 | 0009178773 | 3 | 00048094327 | 00412339 | 09539566 | ||||||||||||
TCGA-LGG | TCGA-DU-6399 | 1 | 00023920655 | 099760795 | 0 | 09970073 | 00029926652 | 2 | 098691386 | 0007037292 | 0006048777 | ||||||||||||
TCGA-LGG | TCGA-DU-6400 | 1 | 0030923586 | 09690764 | 1 | 037771282 | 06222872 | 2 | 09710506 | 0015339471 | 001360994 | ||||||||||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0014545513 | 098545444 | 0 | 045332992 | 054667014 | 2 | 0878585 | 006398724 | 005742785 | ||||||||||||
TCGA-LGG | TCGA-DU-6404 | 0 | 08563024 | 014369765 | 0 | 09857318 | 00142681915 | 3 | 0012578745 | 08931047 | 009431658 | ||||||||||||
TCGA-LGG | TCGA-DU-6405 | 0 | 094122344 | 0058776554 | 0 | 09657707 | 0034229323 | 3 | 0015099723 | 0858934 | 012596628 | ||||||||||||
TCGA-LGG | TCGA-DU-6407 | 1 | 00046772743 | 099532276 | 0 | 095787287 | 0042127114 | 2 | 095650303 | 0019410672 | 0024086302 | ||||||||||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0032852467 | 09671475 | 0 | 02978783 | 070212173 | 3 | 046377006 | 04552443 | 008098562 | ||||||||||||
TCGA-LGG | TCGA-DU-6410 | 1 | 084198 | 015801999 | 1 | 09610981 | 0038901985 | 3 | 0029748935 | 0547783 | 042246798 | ||||||||||||
TCGA-LGG | TCGA-DU-6542 | 1 | 099541724 | 0004582765 | 0 | 099690056 | 00030994152 | 3 | 00036504513 | 0033356518 | 0962993 | ||||||||||||
TCGA-LGG | TCGA-DU-7008 | 1 | 00027017966 | 09972982 | 0 | 09924154 | 0007584589 | 2 | 0945233 | 0033200152 | 0021566862 | ||||||||||||
TCGA-LGG | TCGA-DU-7010 | 1 | 09090629 | 0090937115 | 0 | 083999664 | 016000335 | 3 | 0011747591 | 011156695 | 08766855 | ||||||||||||
TCGA-LGG | TCGA-DU-7014 | -1 | 00067384504 | 09932615 | 0 | 09144437 | 008555635 | 2 | 090214694 | 005846623 | 003938676 | ||||||||||||
TCGA-LGG | TCGA-DU-7015 | 1 | 011059116 | 08894088 | 0 | 09457512 | 005424881 | 2 | 04990067 | 023008518 | 027090812 | ||||||||||||
TCGA-LGG | TCGA-DU-7018 | 1 | 06190684 | 038093168 | 1 | 09720721 | 0027927874 | 3 | 002608347 | 03462771 | 06276394 | ||||||||||||
TCGA-LGG | TCGA-DU-7019 | 1 | 006866228 | 09313377 | 0 | 068647516 | 031352484 | 3 | 06280373 | 02546188 | 011734395 | ||||||||||||
TCGA-LGG | TCGA-DU-7294 | 1 | 039513415 | 06048658 | 1 | 04910898 | 05089102 | 2 | 044678423 | 011827048 | 04349453 | ||||||||||||
TCGA-LGG | TCGA-DU-7298 | 1 | 002178117 | 097821885 | 0 | 058896303 | 041103697 | 3 | 04621931 | 040058115 | 013722575 | ||||||||||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0050494254 | 094950575 | 0 | 09805993 | 0019400762 | 3 | 088520575 | 003754964 | 0077244624 | ||||||||||||
TCGA-LGG | TCGA-DU-7300 | 1 | 020334144 | 07966585 | 1 | 021174264 | 078825736 | 3 | 06957292 | 014594184 | 015832895 | ||||||||||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0028517082 | 09714829 | 0 | 07594931 | 024050693 | 2 | 07559878 | 013617343 | 010783881 | ||||||||||||
TCGA-LGG | TCGA-DU-7302 | 1 | 007878401 | 092121595 | 1 | 097414124 | 0025858777 | 3 | 059945434 | 013100924 | 02695364 | ||||||||||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0049359404 | 09506406 | 0 | 09947084 | 0005291605 | 3 | 05746174 | 017312215 | 025226048 | ||||||||||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0774658 | 022534202 | 0 | 09720191 | 0027980946 | 2 | 007909051 | 04979186 | 042299092 | ||||||||||||
TCGA-LGG | TCGA-DU-7309 | 1 | 002068546 | 097931457 | 0 | 091696864 | 008303132 | 3 | 091011685 | 0041825026 | 0048058107 | ||||||||||||
TCGA-LGG | TCGA-DU-8162 | 0 | 019030987 | 08096902 | 0 | 084344435 | 015655571 | 3 | 06724078 | 015660264 | 017098951 | ||||||||||||
TCGA-LGG | TCGA-DU-8164 | 1 | 0026989132 | 09730109 | 1 | 06119184 | 038808158 | 2 | 078654927 | 011851947 | 00949313 | ||||||||||||
TCGA-LGG | TCGA-DU-8165 | 0 | 099918324 | 000081673806 | 0 | 09982692 | 00017308301 | 3 | 00077142627 | 001586733 | 097641844 | ||||||||||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0062617026 | 093738294 | 0 | 052265906 | 047734097 | 2 | 0571523 | 027175376 | 015672325 | ||||||||||||
TCGA-LGG | TCGA-DU-8167 | 1 | 008068282 | 09193171 | 0 | 08626991 | 013730097 | 2 | 07117111 | 014616342 | 014212546 | ||||||||||||
TCGA-LGG | TCGA-DU-8168 | 1 | 04501781 | 05498219 | 1 | 09405718 | 005942822 | 3 | 028535154 | 039651006 | 031813842 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 013576113 | 08642388 | 0 | 098667485 | 0013325148 | 3 | 06805368 | 011191124 | 020755199 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0038810804 | 09611892 | 0 | 094154674 | 005845324 | 2 | 07418394 | 01198958 | 013826479 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 036534345 | 06346565 | 0 | 097664696 | 0023353029 | 2 | 0076500095 | 07058904 | 021760948 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 057493186 | 042506814 | 0 | 08586593 | 014134066 | 3 | 024835269 | 018135522 | 05702921 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 017411166 | 082588834 | 0 | 08903419 | 0109658085 | 2 | 026840523 | 031951824 | 041207647 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 00015382263 | 099846184 | 0 | 09784259 | 0021574067 | 3 | 099424005 | 00014788082 | 0004281163 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 099497885 | 000502115 | 0 | 09904406 | 0009559399 | 3 | 00076062134 | 003340487 | 09589889 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 01338958 | 08661042 | 1 | 010181248 | 08981875 | 2 | 08703488 | 0033631936 | 0096019216 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 007097701 | 092902297 | 1 | 0049773447 | 09502266 | 2 | 08236395 | 0043779366 | 013258114 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 00054852334 | 09945148 | 1 | 00052813343 | 09947187 | 2 | 095740056 | 0030734295 | 0011865048 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 00015218158 | 099847823 | 0 | 09977216 | 00022783307 | 3 | 097611564 | 0011231668 | 0012652792 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 090418625 | 009581377 | 1 | 09320215 | 006797852 | 3 | 015433969 | 006605734 | 0779603 | ||||||||||||
TCGA-LGG | TCGA-EZ-7265A | -1 | 001654544 | 09834546 | -1 | 092290026 | 0077099696 | -1 | 091443384 | 0045349486 | 0040216673 | ||||||||||||
TCGA-LGG | TCGA-FG-5964 | 1 | 095945925 | 004054074 | 1 | 09480585 | 0051941562 | 2 | 0052469887 | 018844457 | 075908554 | ||||||||||||
TCGA-LGG | TCGA-FG-6688 | 0 | 041685596 | 0583144 | 0 | 0400786 | 0599214 | 3 | 032869554 | 028211078 | 03891937 | ||||||||||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0040960647 | 09590394 | 0 | 088871056 | 01112895 | 2 | 078484637 | 01247657 | 009038795 | ||||||||||||
TCGA-LGG | TCGA-FG-6691 | 1 | 00066411127 | 09933589 | 0 | 09705485 | 002945148 | 2 | 082394814 | 01353293 | 004072261 | ||||||||||||
TCGA-LGG | TCGA-FG-6692 | 0 | 099044985 | 0009550158 | 0 | 098370695 | 0016293105 | 3 | 002482948 | 023811981 | 07370507 | ||||||||||||
TCGA-LGG | TCGA-FG-7643 | 0 | 067991304 | 032008696 | 0 | 094600123 | 0053998843 | 2 | 032237333 | 020420441 | 047342223 | ||||||||||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 00037180893 | 09962819 | 0 | 098237246 | 0017627545 | 2 | 09685786 | 0019877713 | 0011543726 | ||||||||||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 0023916386 | 097608364 | 1 | 003330537 | 096669465 | 2 | 016134319 | 0751304 | 0087352775 | ||||||||||||
TCGA-LGG | TCGA-FG-A713 | 1 | 020932822 | 07906717 | 1 | 053740746 | 046259254 | 2 | 068370515 | 013241291 | 018388201 | ||||||||||||
TCGA-LGG | TCGA-HT-7473 | 1 | 026437023 | 073562974 | 0 | 09914391 | 0008560891 | 2 | 009070598 | 05834457 | 032584828 | ||||||||||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0014885316 | 09851147 | 0 | 093397486 | 006602513 | 3 | 09713343 | 0009202645 | 0019462984 | ||||||||||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0078306936 | 09216931 | 0 | 044295275 | 055704725 | 2 | 06683338 | 024550638 | 00861598 | ||||||||||||
TCGA-LGG | TCGA-HT-7616 | 1 | 0994089 | 0005911069 | 1 | 08912444 | 010875558 | 3 | 00015109215 | 00081261955 | 09903628 | ||||||||||||
TCGA-LGG | TCGA-HT-7680 | 0 | 01775255 | 08224745 | 0 | 079779327 | 020220678 | 2 | 06160002 | 021518312 | 016881672 | ||||||||||||
TCGA-LGG | TCGA-HT-7684 | 1 | 099250317 | 00074968883 | 0 | 09977216 | 00022783307 | 3 | 0001585032 | 0011880362 | 09865346 | ||||||||||||
TCGA-LGG | TCGA-HT-7686 | 1 | 043986762 | 05601324 | 0 | 09985134 | 00014866153 | 3 | 08800356 | 0017893802 | 010207067 | ||||||||||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0508178 | 049182203 | 0 | 09986749 | 00013250223 | 3 | 007351807 | 06479417 | 027854022 | ||||||||||||
TCGA-LGG | TCGA-HT-7692 | 1 | 0006764646 | 09932354 | 1 | 00017718028 | 099822825 | 2 | 084003216 | 009709251 | 00628754 | ||||||||||||
TCGA-LGG | TCGA-HT-7693 | 1 | 08835126 | 011648734 | 0 | 098880965 | 0011190402 | 2 | 00389769 | 060259813 | 035842496 | ||||||||||||
TCGA-LGG | TCGA-HT-7694 | 1 | 006299064 | 093700933 | 1 | 06663645 | 03336355 | 3 | 06368097 | 02515797 | 011161056 | ||||||||||||
TCGA-LGG | TCGA-HT-7855 | 1 | 013434944 | 086565053 | 0 | 06805072 | 031949285 | 3 | 03900612 | 035394293 | 02559958 | ||||||||||||
TCGA-LGG | TCGA-HT-7856 | 1 | 0037151825 | 09628482 | 1 | 045108467 | 05489153 | 3 | 0024809493 | 094240344 | 0032787096 | ||||||||||||
TCGA-LGG | TCGA-HT-7860 | 0 | 09996338 | 000036614697 | 0 | 099890125 | 00010987312 | 3 | 00023981468 | 004139189 | 095620996 | ||||||||||||
TCGA-LGG | TCGA-HT-7874 | 1 | 027373514 | 07262649 | 1 | 061277324 | 03872268 | 3 | 030690825 | 04321765 | 026091516 | ||||||||||||
TCGA-LGG | TCGA-HT-7879 | 1 | 006545533 | 09345446 | 0 | 07643643 | 023563562 | 3 | 07316188 | 01349482 | 0133433 | ||||||||||||
TCGA-LGG | TCGA-HT-7882 | 0 | 099826247 | 00017375927 | 0 | 099920684 | 0000793176 | 3 | 00026636408 | 0011237644 | 098609877 | ||||||||||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0045437213 | 09545628 | 0 | 09804874 | 0019512545 | 2 | 067944294 | 020575646 | 011480064 | ||||||||||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0090937115 | 09090629 | 0 | 08061669 | 019383314 | 2 | 069696444 | 017501967 | 012801588 | ||||||||||||
TCGA-LGG | TCGA-HT-8105 | 1 | 09291196 | 0070880495 | 1 | 09865976 | 0013402403 | 3 | 036353382 | 007970196 | 055676425 | ||||||||||||
TCGA-LGG | TCGA-HT-8106 | 1 | 09987081 | 00012918457 | 0 | 099922514 | 00007748164 | 3 | 0019909225 | 0057560045 | 09225307 | ||||||||||||
TCGA-LGG | TCGA-HT-8107 | 0 | 006150854 | 09384914 | 0 | 018944609 | 08105539 | 2 | 071847403 | 015055439 | 013097167 | ||||||||||||
TCGA-LGG | TCGA-HT-8111 | 1 | 062096643 | 037903354 | 0 | 09220272 | 007797278 | 3 | 00015017459 | 090417147 | 009432678 | ||||||||||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0003941571 | 099605846 | 0 | 00025608707 | 099743915 | 2 | 088384247 | 009021307 | 002594447 | ||||||||||||
TCGA-LGG | TCGA-HT-8114 | 1 | 09404078 | 005959219 | 0 | 09970708 | 00029292419 | 3 | 0015292862 | 028022403 | 07044831 | ||||||||||||
TCGA-LGG | TCGA-HT-8563 | 1 | 099999154 | 843094E-06 | 0 | 099999607 | 39515203E-06 | 3 | 32918017E-06 | 031742522 | 068257153 | ||||||||||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 06915494 | 030845058 | 0 | 045883363 | 054116637 | 3 | 012257236 | 02777765 | 059965116 | ||||||||||||
TCGA-LGG | TCGA-HT_A614 | 1 | 08180474 | 018195263 | 0 | 09584989 | 00415011 | 2 | 0067164555 | 0059489973 | 087334543 | ||||||||||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0035779487 | 09642206 | 0 | 07277821 | 0272218 | 2 | 07607039 | 014429174 | 009500429 |
Abstract
Accurate characterization of glioma is crucial for clinical decision mak-ing A delineation of the tumor is also desirable in the initial decisionstages but is a time-consuming task Leveraging the latest GPU capabil-ities we developed a single multi-task convolutional neural network thatuses the full 3D structural pre-operative MRI scans to can predict theIDH mutation status the 1p19q co-deletion status and the grade of a tu-mor while simultaneously segmenting the tumor We trained our methodusing the largest most diverse patient cohort to date containing 1508glioma patients from 16 institutes We tested our method on an indepen-dent dataset of 240 patients from 13 different institutes and achieved anIDH-AUC of 090 1p19q-AUC of 085 grade-AUC of 081 and a meanwhole tumor DICE score of 084 Thus our method non-invasively pre-dicts multiple clinically relevant parameters and generalizes well to thebroader clinical population
1 Introduction
Glioma is the most common primary brain tumor and is one of the deadliestforms of cancer [1] Differences in survival and treatment response of glioma areattributed to their genetic and histological features specifically the isocitratedehydrogenase (IDH) mutation status the 1p19q co-deletion status and thetumor grade [2 3] Therefore in 2016 the World Health Organization (WHO)updated its brain tumor classification categorizing glioma based on these ge-netic and histological features [4] In current clinical practice these features aredetermined from tumor tissue While this is not an issue in patients in whomthe tumor can be resected this is problematic when resection can not safelybe performed In these instances surgical biopsy is performed with the solepurpose of obtaining tissue for diagnosis which although relatively safe is notwithout risk [5 6] Therefore there has been an increasing interest in comple-mentary non-invasive alternatives that can provide the genetic and histologicalinformation used in the WHO 2016 categorization [7 8]
Magnetic resonance imaging (MRI) has been proposed as a possible candi-date because of its non-invasive nature and its current place in routine clinicalcare [9] Research has shown that certain MRI features such as the tumor het-erogeneity correlate with the genetic and histological features of glioma [10 11]This notion has popularized in addition to already popular applications suchas tumor segmentation the use of machine learning methods for the predictionof genetic and histological features known as radiomics [12 13 14] Althougha plethora of such methods now exist they have found little translation to theclinic [12]
An often discussed challenge for the adoption of machine learning methodsin clinical practice is the lack of standardization resulting in heterogeneity ofpatient populations imaging protocols and scan quality [15 16] Since machinelearning methods are prone to overfitting this heterogeneity questions the va-lidity of such methods in a broader patient population [16] Furthermore it has
2
been noted that most current research concerns narrow task-specific methodsthat lack the context between different related tasks which might restrict theperformance of these methods [17]
An important technical limitation when using deep learning methods is thelimited GPU memory which restricts the size of models that can be trained[18] This is a problem especially for clinical data which is often 3D requiringeven more memory than the commonly used 2D networks This further limitsthe size of these models resulting in shallower models and the use of patches ofa scan instead of using the full 3D scan as an input which limits the amount ofcontext these methods can extract from the scans
Here we present a new method that addresses the above problems Ourmethod consists of a single multi-task convolutional neural network (CNN)that can predict the IDH mutation status the 1p19q co-deletion status andthe grade (grade IIIIIIV) of a tumor while also simultaneously segmenting thetumor see Figure 1 To the best of our knowledge this is the first method thatprovides all of this information at the same time allowing clinical experts to de-rive the WHO category from the individually predicted genetic and histologicalfeatures while also allowing them to consider or disregard specific predictionsas they deem fit Exploiting the capabilities of the latest GPUs optimizing ourimplementation to reduce the memory footprint and using distributed multi-GPU training we were able to train a model that uses the full 3D scan as aninput We trained our method using the largest most diverse patient cohortto date with 1508 patients included from 16 different institutes To ensurethe broad applicability of our method we used minimal inclusion criteria onlyrequiring the four most commonly used MRI sequences pre- and post-contrastT1-weighted (T1w) T2-weighted (T2w) and T2-weighted fluid attenuated in-version recovery (T2w-FLAIR) [19 20] No constraints were placed on thepatientsrsquo clinical characteristics such as the tumor grade or the radiologicalcharacteristics of scans such as the scan quality In this way our method couldcapture the heterogeneity that is naturally present in clinical data We testedour method on an independent dataset of 240 patients from 13 different insti-tutes to evaluate the true generalizability of our method Our results show thatwe can predict multiple clinical features of glioma from MRI scans in a diversepatient population
3
Convolutionalneural network
IDH status
Wildtype Mutated
1p19q status
Intact Co-deleted
Grade
II III IV
WHO 2016categorization
MRI scansPreprocessed
scansSegmentation
Figure 1 Overview of our method Pre- and post-contrast T1w T2w and T2w-FLAIR scans are used as an input The scans are registered to an atlas biasfield corrected skull stripped and normalized before being passed through ourconvolutional neural network One branch of the network segments the tumorwhile at the same time the features are combined to predict the IDH status1p19q status and grade of the tumor
4
2 Results
21 Patient characteristics
We included a total of 1748 patients in our study 1508 as a train set and240 as an independent test set The patients in the train set originated fromnine different data collections and 16 different institutes and the test set wascollected from two different data collections and 13 different institutes Table 1provides a full overview of the patient characteristics in the train and test setand Figure 2 shows the inclusion flowchart and the distribution of the patientsover the different data collections in the train set and test set
Table 1 Patient characteristics for the train set and test set
Train set Test setN N
Patients 1508 240IDH status
Mutated 226 150 88 367Wildtype 440 292 129 537Unknown 842 558 23 96
1p19q co-deletion statusCo-deleted 103 68 26 108Intact 337 224 207 863Unknown 1068 708 7 29
GradeII 230 153 47 196III 114 76 59 246IV 830 550 132 550Unknown 334 221 2 08
WHO 2016 categorizationOligodendroglioma 96 64 26 108Astrocytoma IDH wildtype 31 21 22 92Astrocytoma IDH mutated 98 64 57 237GBM IDH wildtype 331 219 106 442GBM IDH mutated 16 11 5 21Unknown 936 621 24 100
SegmentationManual 716 475 240 100Automatic 792 525 0 0
IDH isocitrate dehydrogenase WHO World Health Organization GBMglioblastoma
5
Patient screening
Train set2181 Glioma patients
1241 Erasmus MC491 Haaglanden Medical Center168 BraTS130 REMBRANDT66 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set461 Glioma patients
199 TCGA-LGG262 TCGA-GBM
Patient inclusion
Train set1508 Patients in train set
816 Erasmus MC279 Haaglanden Medical Center168 BraTS109 REMBRANDT51 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set240 Patients in test set
107 TCGA-LGG133 TCGA-GBM
Patient exclusion
Train set673 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
425 Erasmus MC212 Haaglanden Medical Center
0 BraTS21 REMBRANDT15 CPTAC-GBM0 Ivy GAP0 Amsterdam UMC0 Brain-Tumor-Progression0 University Medical Center Utrecht
Test set221 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
92 TCGA-LGG129 TCGA-GBM
Figure 2 Inclusion flowchart of the train set and test set
6
22 Algorithm performance
We used 15 of the train set as a validation set and selected the model pa-rameters that achieved the best performance on this validation set where themodel achieved an area under receiver operating characteristic curve (AUC) of088 for the IDH mutation status prediction an AUC of 076 for the 1p19qco-deletion prediction an AUC of 075 for the grade prediction and a meansegmentation DICE score of 081 The selected model parameters are shown inAppendix F We then trained a model using these parameters and the full trainset and evaluated it on the independent test set
For the genetic and histological feature predictions we achieved an AUC of090 for the IDH mutation status prediction an AUC of 085 for the 1p19qco-deletion prediction and an AUC of 081 for the grade prediction in thetest set The full results are shown in Table 2 with the corresponding receiveroperating characteristic (ROC)-curves in Figure 3 Table 2 also shows the resultsin (clinically relevant) subgroups of patients This shows that we achieved anIDH-AUC of 081 in low grade glioma (LGG) (grade IIIII) an IDH-AUC of064 in high grade glioma (HGG) (grade IV) and a 1p19q-AUC of 073 in LGGWhen only predicting LGG vs HGG instead of predicting the individual gradeswe achieved an AUC of 091 In Appendix A we provide confusion matrices forthe IDH 1p19q and grade predictions as well as a confusion matrix for thefinal WHO 2016 subtype which shows that only one patient was predicted asa non-existing WHO 2016 subtype In Appendix C we provide the individualpredictions and ground truth labels for all patients in the test set to allow forthe calculation of additional metrics
For the automatic segmentation we achieved a mean DICE score of 084 amean Hausdorff distance of 189 mm and a mean volumetric similarity coeffi-cient of 090 Figure 4 shows boxplots of the DICE scores Hausdorff distancesand volumetric similarity coefficients for the different patients in the test set InAppendix B we show five patients that were randomly selected from both theTCGA-LGG and TCGA-GBM data collections to demonstrate the automaticsegmentations made by our method
23 Model interpretability
To provide insight into the behavior of our model we created saliency mapswhich show which parts of the scans contributed the most to the predictionThese saliency maps are shown in Figure 5 for two example patients from thetest set It can be seen that for the LGG the network focused on a bright rim inthe T2w-FLAIR scan whereas for the HGG it focused on the enhancement in thepost-contrast T1w scan To aid further interpretation we provide visualizationsof selected filter outputs in the network in Appendix D which also show thatthe network focuses on the tumor and these filters seem to recognize specificimaging features such as the contrast enhancement and T2w-FLAIR brightness
7
Table 2 Evaluation results of the final model on the test set
Patientgroup
Task AUC Accuracy Sensitivity Specificity
All IDH 090 084 072 0931p19q 085 089 039 095Grade (IIIIIIV) 081 071 NA NAGrade II 091 086 075 089Grade III 069 075 017 094Grade IV 091 082 095 066LGG vs HGG 091 084 072 093
LGG IDH 081 074 073 0771p19q 073 076 039 089
HGG IDH 064 094 040 096
Abbreviations AUC area under receiver operating characteristic curve IDHisocitrate dehydrogenase LGG low grade glioma HGG high grade glioma
Figure 3 Receiver operating characteristic (ROC)-curves of the genetic andhistological features evaluated on the test set The crosses indicate the locationof the decision threshold for the reported accuracy sensitivity and specificity
8
Figure 4 DICE scores Hausdorff distances and volumetric similarity coeffi-cients for all patients in the test set The DICE score is a measure of theoverlap between the ground truth and predicted segmentation (where 1 indi-cates perfect overlap) The Hausdorff distance is a measure of the agreementbetween the boundaries of the ground truth and predicted segmentation (loweris better) The volumetric similarity coefficient is a measure of the agreementin volume (where 1 indicates perfect agreement)
9
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Saliency maps of a low grade glioma patient (TCGA-DU-6400) This is an IDH mu-tated 1p19q co-deleted grade II tumor The network focuses on a rim of brightnessin the T2w-FLAIR scan
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) Saliency maps of a high grade glioma patient (TCGA-06-0238) This is an IDHwildtype grade IV tumor The network focuses on enhancing spots around the necrosison the post-contrast T1w scan
Figure 5 Saliency maps of two patients from the test set showing areas thatare relevant for the prediction
10
24 Model robustness
By not excluding scans from our train set based on radiological characteristicswe were able to make our model robust to low scan quality as can be seen in anexample from the test set in Figure 6 Even though this example scan containedimaging artifacts our method was able to properly segment the tumor (DICEscore of 087) and correctly predict the tumor as an IDH wildtype grade IVtumor
Figure 6 Example of a T2w-FLAIR scan containing imaging artifacts and theautomatic segmentation (red overlay) made by our method It was correctlypredicted as an IDH wildtype grade IV glioma This is patient TCGA-06-5408from the TCGA-GBM collection
Finally we considered two examples of scans that were incorrectly predictedby our method see Figure 7 These two examples were chosen because ournetwork assigned high prediction scores to the wrong classes for these casesFigure 7a shows an example of a grade II IDH mutated 1p19q co-deletedglioma that was predicted as grade IV IDH wildtype by our method Ourmethodrsquos prediction was most likely caused by the hyperintensities in the post-contrast T1w scan being interpreted as contrast enhancement Since these hy-perintensities are also present in the pre-contrast T1w scan they are most likelycalcifications and the radiological appearance of this tumor is indicative of anoligodendroglioma Figure 7b shows an example of a grade IV IDH wildtypeglioma that was predicted as a grade III IDH mutated glioma by our method
11
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) TCGA-DU-6410 from the TCGA-LGG collection The ground truth histopatho-logical analysis indicated this glioma was grade II IDH mutated 1p19q co-deletedbut our method predicted it as a grade IV IDH wildtype
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) TCGA-76-7664 from the TCGA-HGG collection Histopathologically this gliomawas grade IV IDH wildtype but our method predicted it as grade III IDH mutated
Figure 7 Examples of scans that were incorrectly predicted by our method
12
3 Discussion
We have developed a method that can predict the IDH mutation status 1p19qco-deletion status and grade of glioma while simultaneously providing the tu-mor segmentation based on pre-operative MRI scans For the genetic andhistological feature predictions we achieved an AUC of 090 for the IDH mu-tation status prediction an AUC of 085 for the 1p19q co-deletion predictionand an AUC of 081 for the grade prediction in the test set
In an independent test set which contained data from 13 different instituteswe demonstrated that our method predicts these features with good overall per-formance we achieved an AUC of 090 for the IDH mutation status predictionan AUC of 085 for the 1p19q co-deletion prediction and an AUC of 081 forthe grade prediction and a mean whole tumor DICE score of 084 This per-formance on unseen data that was only used during the final evaluation of thealgorithm and that was purposefully not used to guide any decisions regardingthe method design shows the true generalizability of our method Using thelatest GPU capabilities we were able to train a large model which uses thefull 3D scan as input Furthermore by using the largest most diverse patientcohort to date we were able to make our method robust to the heterogeneitythat is naturally present in clinical imaging data such that it generalizes forbroad application in clinical practice
By using a multi-task network our method could learn the context betweendifferent features For example IDH wildtype and 1p19q co-deletion are mu-tually exclusive [21] If two separate methods had been used one to predict theIDH status and one to predict the 1p19q co-deletion status an IDH wildtypeglioma might be predicted to be 1p19q co-deleted which does not stroke withthe clinical reality Since our method learns both of these genetic features si-multaneously it correctly learned not to predict 1p19q co-deletion in tumorsthat were IDH wildtype there was only one patient in which our algorithm pre-dicted a tumor to be both IDH wildtype and 1p19q co-deleted Furthermoreby predicting the genetic and histological features individually instead of onlypredicting the WHO 2016 category it is possible to adopt updated guidelinessuch as cIMPACT-NOW future-proofing our method [22]
Some previous studies also used multi-task networks to predict the geneticand histological features of glioma [23 24 25] Tang et al [23] used a multi-tasknetwork that predicts multiple genetic features as well as the overall survivalof glioblastoma Since their method only works for glioblastoma patients thetumor grade must be known in advance complicating the use of their methodin the pre-operative setting when tumor grade is not yet known Furthermoretheir method requires a tumor segmentation prior to application of their methodwhich is a time-consuming expert task In a study by Xue et al [24] a multi-task network was used with a structure similar to the one proposed in thispaper to segment the tumor and predict the grade (LGG or HGG) and IDHmutation status However they do not predict the 1p19q co-deletion statusneeded for the WHO 2016 categorization Lastly Decuyper et al [25] useda multi-task network that predicts the IDH mutation and 1p19q co-deletion
13
status and the tumor grade (LGG or HGG) Their method requires a tumorsegmentation as input which they obtain from a U-Net that is applied earlierin their pipeline thus their method requires two networks instead of the singlenetwork we use in our method These differences aside the most importantlimitation of each of these studies is the lack of an independent test set forevaluating their results It is now considered essential that an independent testset is used to prevent an overly optimistic estimate of a methodrsquos performance[15 26 27 28] Thus our study improves on this previous work by providinga single network that combines the different tasks being trained on a moreextensive and diverse dataset not requiring a tumor segmentation as an in-put providing all information needed for the WHO 2016 categorization andcrucially by being evaluated in an independent test set
An important genetic feature that is not predicted by our method is theO6-methylguanine-methyltransferase (MGMT) methylation status Althoughthe MGMT methylation status is not part of the WHO 2016 categorizationit is part of clinical management guidelines and is an important prognosticmarker in glioblastoma [4] In the initial stages of this study we attemptedto predict the MGMT methylation status however the performance of thisprediction was poor Furthermore the methylation cutoff level which is usedto determine whether a tumor is MGMT methylated shows a wide varietybetween institutes leading to inconsistent results [29] We therefore opted not toinclude the MGMT prediction at all rather than to provide a poor prediction ofan unsharply defined parameter Although some methods attempted to predictthe MGMT status with varying degrees of success there is still an ongoingdiscussion on the validity of MR imaging features of the MGMT status [23 3031 32 33]
Our method shows good overall performance but there are noticeable per-formance differences between tumor categories For example when our methodpredicts a tumor as an IDH wildtype glioblastoma it is correct almost all of thetime On the other hand it has some difficulty differentiating IDH mutated1p19q co-deleted low-grade glioma from other low-grade glioma The sensitiv-ity for the prediction of grade III glioma was low which might be caused bythe lack of a central pathology review Because of this there were differences inmolecular testing and histological analysis and it is known that distinguishingbetween grade II and grade III has a poor observer reliability [34] Althoughour method can be relevant for certain subgroups our methodrsquos performancestill needs to be improved to ensure relevancy for the full patient population
In future work we aim to increase the performance of our method by includ-ing perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI)since there has been an increasing amount of evidence that these physiologi-cal imaging modalities contain additional information that correlates with thetumorrsquos genetic status and aggressiveness [35 36] They were not included inthis study since PWI and to a lesser extent DWI are not as ingrained in theclinical imaging routine as the structural scans used in this work [19 20] Thusincluding these modalities would limit our methodrsquos clinical applicability andsubstantially reduce the number of patients in the train and test set However
14
PWI and DWI are increasingly becoming more commonplace which will allowincluding these in future research and which might improve performance
In conclusion we have developed a non-invasive method that can predict theIDH mutation status 1p19q co-deletion status and grade of glioma while atthe same time segmenting the tumor based on pre-operative MRI scans withhigh overall performance Although the performance of our method might needto be improved before it will find widespread clinical acceptance we believe thatthis research is an important step forward in the field of radiomics Predict-ing multiple clinical features simultaneously steps away from the conventionalsingle-task methods and is more in line with the clinical practice where multipleclinical features are considered simultaneously and may even be related Fur-thermore by not limiting the patient population used to develop our method toa selection based on clinical or radiological characteristics we alleviate the needfor a priori (expert) knowledge which may not always be available Althoughsteps still have to be taken before radiomics will find its way into the clinicespecially in terms of performance our work provides a crucial step forward byresolving some of the hurdles of clinical implementation now and paving theway for a full transition in the future
4 Methods
41 Patient population
The train set was collected from four in-house datasets and five publicly avail-able datasets In-house datasets were collected from four different institutesErasmus MC (EMC) Haaglanden Medical Center (HMC) Amsterdam UMC(AUMC) [37] and University Medical Center Utrecht (UMCU) Four of the fivepublic datasets were collected from The Cancer Imaging Archive (TCIA) [38]the Repository of Molecular Brain Neoplasia Data (REMBRANDT) collection[39] the Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multi-forme (CPTAC-GBM) collection [40] the Ivy Glioblastoma Atlas Project (IvyGAP) collection [41 42] and the Brain-Tumor-Progression collection [43] Thefifth dataset was the 2019 Brain Tumor Segmentation challenge (BraTS) chal-lenge dataset [44 45 46] from which we excluded the patients that were alsoavailable in the TCGA-LGG and TCGA-GBM collections [47 48]
For the internal datasets from the EMC and the HMC manual segmentationswere available which were made by four different clinical experts For patientswhere segmentations from more than one observer were available we randomlypicked one of the segmentations to use in the train set The segmentationsfrom the AUMC data were made by a single observer of the study by Visseret al [37] From the public datasets only the BraTS dataset and the Brain-Tumor-Progression dataset provided manual segmentations Segmentations ofthe BraTS dataset as provided in the 2019 training and validation set wereused For the Brain-Tumor-Progression dataset the segmentations as providedin the TCIA data collection were used
15
Patients were included if pre-operative pre- and post-contrast T1w T2wand T2w-FLAIR scans were available no further inclusion criteria were setFor example patients were not excluded based on the radiological characteristicsof the scan such as low imaging quality or imaging artifacts or the gliomarsquosclinical characteristics such as the grade If multiple scans of the same contrasttype were available in a single scan session (eg multiple T2w scans) the scanupon which the segmentation was made was selected If no segmentation wasavailable or the segmentation was not made based on that scan contrast thescan with the highest axial resolution was used where a 3D acquisition waspreferred over a 2D acquisition
For the in-house data genetic and histological data were available for theEMC HMC and UMCU dataset which were obtained from analysis of tumortissue after biopsy or resection Genetic and histological data of the publicdatasets were also available for the REMBRANDT CPTAC-GBM and IvyGAP collections Data for the REMBRANDT and CPTAC-GBM collectionswas collected from the clinical data available at the TCIA [40 39] For theIvy GAP collection the genetic and histological data were obtained from theSwedish Institute at httpsivygapswedishorghome
As a test set we used the TCGA-LGG and TCGA-GBM collections from theTCIA [47 48] Genetic and histological labels were obtained from the clinicaldata available at the TCIA Segmentations were used as available from theTCIA based on the 2018 BraTS challenge [45 49 50] The inclusion criteriafor the patients included in the BraTS challenge were the same as our inclusioncriteria the presence of a pre-operative pre- and post-contrast T1w T2w andT2w-FLAIR scan Thus patients from the TCGA-LGG and TCGA-GBM wereincluded if a segmentation from the BraTS challenge was available Howeverfor three patients we found that although they did have manual segmentationsthey did not meet our inclusion requirements TCGA-08-0509 and TCGA-08-0510 from TCGA-GBM because they did not have a pre-contrast T1w scan andTCGA-FG-7634 from TCGA-LGG because there was no post-contrast T1wscan
42 Automatic segmentation in the train set
To present our method with a large diversity in scans we wanted to include asmany patients in the train set as possible from the different datasets Thereforewe performed automatic segmentation in patients that did not have manualsegmentations To this end we used an initial version of our network (presentedin Section 44) without the additional layers that were needed for the predictionof the genetic and histological features This network was initially trained usingall patients in the train set for whom a manual segmentation was availableand this trained network was then applied to all patients for which a manualsegmentation was not available The resulting automatic segmentations wereinspected and if their quality was acceptable they were added to the trainset The network was then trained again using this increased dataset and wasapplied to scans that did not yet have a segmentation of acceptable quality
16
This process was repeated until an acceptable segmentation was available forall patients which constituted our final complete train set
43 Pre-processing
For all datasets except for the BraTS dataset for which the scans were alreadyprovided in NIfTI format the scans were converted from DICOM format toNIfTI format using dcm2niix version v1020190410 [51] We then registeredall scans to the MNI152 T1w and T2w atlases version ICBM 2009a whichhad a resolution of 1x1x1 mm3 and a size of 197x233x189 voxels [52 53] Thescans were affinely registered using Elastix 50 [54 55] The pre- and post-contrast T1w scans were registered to the T1w atlas the T2w and T2w-FLAIRscans were registered to the T2w atlas When a manual segmentation wasavailable for patients from the in-house datasets the registration parametersthat resulted from registering the scan used during the segmentation were usedto transform the segmentation to the atlas In the case of the public datasetswe used the registration parameters of the T2w-FLAIR scans to transform thesegmentations
After the registration all scans were N4 bias field corrected using SimpleITKversion 124 [56] A brain mask was made for the atlas using HD-BET both forthe T1w atlas and the T2w atlas [57] This brain mask was used to skull stripall registered scans and crop them to a bounding box around the brain maskreducing the amount of background present in the scans resulting in a scan sizeof 152x182x145 voxels Subsequently the scans were normalized such that foreach scan the average image intensity was 0 and the standard deviation of theimage intensity was 1 within the brain mask Finally the background outsidethe brain mask was set to the minimum intensity value within the brain mask
Since the segmentation could sometimes be rugged at the edges after regis-tration especially when the segmentations were initially made on low-resolutionscans we smoothed the segmentation using a 3x3x3 median filter (this was onlydone in the train set) For segmentations that contained more than one la-bel eg when the tumor necrosis and enhancement were separately segmentedall labels were collapsed into a single label to obtain a single segmentation ofthe whole tumor The genetic and histological labels and the segmentations ofeach patient were one-hot encoded The four scans ground truth labels andsegmentation of each patient were then used as the input to the network
44 Model
We based the architecture of our model on the U-Net architecture with someadaptations made to allow for a full 3D input and the auxiliary tasks [58] Ournetwork architecture which we have named PrognosAIs Structure-Net or PS-Net for short can be seen in Figure 8
To use the full 3D scan as an input to the network we replaced the firstpooling layer that is usually present in the U-Net with a strided convolutionwith a kernel size of 9x9x9 and a stride of 3x3x3 In the upsampling branch of
17
32
32 64
128
256
512 256
7x8x7256 128
128 64
64 32
32 2
Segmentation
145x182x152
49x61x51
25x31x26
13x16x13
1472
512 2IDH
512 2
1p19q
512 3Grade
Batch normalization Concatenation Convolution amp ReLU3x3x3
Convolution amp Softmax1x1x1
(De)convolution amp ReLU9x9x9
stride 3x3x3
Dense amp ReLU Dense amp Softmax Dropout
Max pooling2x2x2
Up-convolution amp ReLU2x2x2
Global maxpooling
Figure 8 Overview of the PrognosAIs Structure-Net (PS-Net) architecture usedfor our model The numbers below the different layers indicate the number offilters dense units or features at that layer We have also indicated the featuremap size at the different depths of the network
the network the last up-convolution is replaced by a deconvolution with thesame kernel size and stride
At each depth of the network we have added global max-pooling layersdirectly after the dropout layer to obtain imaging features that can be used topredict the genetic and histological features We chose global pooling layers asthey do not introduce any additional parameters that need to be trained thuskeeping the memory required by our model manageable The features from thedifferent depths of the network were concatenated and fed into three differentdense layers one for each of the genetic and histological outputs
l2 kernel regularization was used in all convolutional layers except for thelast convolutional layer used for the output of the segmentation In total thismodel contained 27042473 trainable an 2944 non-trainable parameters
18
45 Model training
Training of the model was done on eight NVidia RTX2080Tirsquos with 11GB ofmemory using TensorFlow 220 [59] To be able to use the full 3D scan asinput to the network without running into memory issues we had to optimizethe memory efficiency of the model training Most importantly we used mixed-precision training which means that most of the variables of the model (such asthe weights) were stored in float16 which requires half the memory of float32which is typically used to store these variables [60] Only the last softmaxactivation layers of each output were stored as float32 We also stored ourpre-processed scans as float16 to further reduce memory usage
However even with these settings we could not use a batch size larger than1 It is known that a larger batch size is preferable as it increases the stabilityof the gradient updates and allows for a better estimation of the normalizationparameters in batch normalization layers [61] Therefore we distributed thetraining over the eight GPUs using the NCCL AllReduce algorithm whichcombines the gradients calculated on each GPU before calculating the updateto the model parameters [62] We also used synchronized batch normalizationlayers which synchronize the updates of their parameters over the distributedmodels In this way our model had a virtual batch size of eight for the gradientupdates and the batch normalization layers parameters
To provide more samples to the algorithm and prevent potential overtrain-ing we applied four types of data augmentation during training croppingrotation brightness shifts and contrast shifts Each augmentation was appliedwith a certain augmentation probability which determined the probability ofthat augmentation type being applied to a specific sample When an image wascropped a random number of voxels between 0 and 20 was cropped from eachdimension and filled with zeros For the random rotation an angle betweenminus30 and 30 degrees was selected from a uniform distribution for each dimen-sion The brightness shift was applied with a delta uniformly drawn between0 and 02 and the contrast shift factor was randomly drawn between 085 and115 We also introduced an augmentation factor which determines how ofteneach sample was parsed as an input sample during a single epoch where eachtime it could be augmented differently
For the IDH 1p19q and grade output we used a masked categorical cross-entropy loss and for the segmentation we used a DICE loss see Appendix E fordetails We used AdamW as an optimizer which has shown improved general-ization performance over Adam by introducing the weight decay parameter as aseparate parameter from the learning rate [63] The learning rate was automat-ically reduced by a factor of 025 if the loss did not improve during the last fiveepochs with a minimum learning rate of 1 middot 10minus11 The model could train for amaximum of 150 epochs and training was stopped early if the average loss overthe last five epochs did not improve Once the model was finished training theweights from the epoch with the lowest loss were restored
19
46 Hyperparameter tuning
Hyperparameters involved in the training of the model needed to be tuned toachieve the best performance We tuned a total of six hyper parameters the l2-norm the dropout rate the augmentation factor the augmentation probabilitythe optimizerrsquos initial learning rate and the optimizerrsquos weight decay A fulloverview of the trained parameters and the values tested for the different settingsis presented in Appendix F
To tune these hyperparameters we split the train set into a hyperparametertraining set (851282 patients of the full train data) and a hyperparametervalidation set (15226 patients of the full train data) Models were trained fordifferent hyperparameter settings via an exhaustive search using the hyperpa-rameter train set and then evaluated on the hyperparameter validation set Nodata augmentation was applied to the hyperparameter validation to ensure thatresults between trained models were comparable The hyperparameters that ledto the lowest overall loss in the hyperparameter validation set were chosen asthe optimal hyperparameters We trained the final model using these optimalhyperparameters and the full train set
47 Post-processing
The predictions of the network were post-processed to obtain the final predictedlabels and segmentations for the samples Since softmax activations were usedfor the genetic and histological outputs a prediction between 0 and 1 was out-putted for each class where the individual predictions summed to 1 The finalpredicted label was then considered as the class with the highest prediction scoreFor the prediction of LGG (grade IIIII) vs HGG (grade IV) the predictionscores of grade II and grade III were combined to obtain the prediction scorefor LGG the prediction score of grade IV was used as the prediction score forHGG If a segmentation contained multiple unconnected components we onlyretained the largest component to obtain a single whole tumor segmentation
48 Model evaluation
The performance of the final trained model was evaluated on the independenttest set comparing the predicted labels with the ground truth labels For thegenetic and histological features we evaluated the AUC the accuracy the sen-sitivity and the specificity using scikit-learn version 0231 for details see Ap-pendix G [64] We evaluated these metrics on the full test set and in subcate-gories relevant to the WHO 2016 guidelines We evaluated the IDH performanceseparately in the LGG (grade IIIII) and HGG (grade IV) subgroups the 1p19qperformance in LGG and we also evaluated the performance of distinguishingbetween LGG and HGG instead of predicting the individual grades
To evaluate the performance of the segmentation we calculated the DICEscores Hausdorff distances and volumetric similarity coefficient comparing theautomatic segmentation of our method and the manual ground truth segmen-
20
tations for all patients in the test set These metrics were calculated usingthe EvaluateSegmentation toolbox version 20170425 [65] for details see Ap-pendix G
To prevent an overly optimistic estimation of our modelrsquos predictive valuewe only evaluated our model on the test set once all hyperparameters werechosen and the final model was trained In this way the performance in thetest set did not influence decisions made during the development of the modelpreventing possible overfitting by fine-tuning to the test set
To gain insight into the model we made saliency maps that show whichparts of the scan contribute the most to the prediction of the CNN [66] Saliencymaps were made using tf-keras-vis 052 changing the activation function of alloutput layers from softmax to linear activations using SmoothGrad to reducethe noisiness of the saliency maps [66]
Another way to gain insight into the networkrsquos behavior is to visualize thefilter outputs of the convolutional layers as they can give some idea as to whatoperations the network applies to the scans We visualized the filter outputs ofthe last convolutional layers in the downsample and upsample path at the firstdepth (at an image size of 49x61x51) of our network These filter outputs werevisualized by passing a sample through the network and showing the convolu-tional layersrsquo outputs replacing the ReLU activation with linear activations
49 Data availability
An overview of the patients included from the public datasets used in the train-ing and testing of the algorithm and their ground truth label is available inAppendix H The data from the public datasets are available in TCIA un-der DOIs 107937K9TCIA2015588OZUZB 107937k9tcia20183rje41q1107937K9TCIA2016XLwaN6nL and 107937K9TCIA201815quzvnb Datafrom the BraTS are available at httpbraintumorsegmentationorg Datafrom the in-house datasets are not publicly available due to participant privacyand consent
410 Code availability
The code used in this paper is available on GitHub under an Apache 2 license athttpsgithubcomSvdvoortPrognosAIs_glioma This code includes thefull pipeline from registration of the patients to the final post-processing of thepredictions The trained model is also available on GitHub along with code toapply it to new patients
21
Appendices
A Confusion matrices
Tables 3 4 and 5 show the confusion matrices for the IDH 1p19q and gradepredictions and Table 6 shows the confusion matrix for the WHO 2016 subtypes
Table 4 shows that the algorithm mainly has difficulty recognizing 1p19qco-deleted tumors which are mostly predicted as 1p19q intact Table 5 showsthat most of the incorrectly predicted grade III tumors are predicted as gradeIV tumors
Table 6 shows that our algorithm often incorrectly predicts IDH-wildtypeastrocytoma as IDH-wildtype glioblastoma The latest cIMPACT-NOW guide-lines propose a new categorization in which IDH-wildtype astrocytoma thatshow either TERT promoter methylation or EFGR gene amplification or chro-mosome 7 gainchromsome 10 loss are classified as IDH-wildtype glioblastoma[22] This new categorization is proposed since the survival of patients withthose IDH-wildtype astrocytoma is similar to the survival of patients with IDH-wildtype glioblastoma [22] From the 13 IDH-wildtype astrocytoma that werewrongly predicted as IDH-wildtype glioblastoma 12 would actually be cate-gorized as IDH-wildtype glioblastoma under this new categorization Thusalthough our method wrongly predicted the WHO 2016 subtype it might ac-tually have picked up on imaging features related to the aggressiveness of thetumor which might lead to a better categorization
Table 3 Confusion matrix of the IDH predictions
Predicted
Wildtype Mutated
Actu
al
Wildtype 120 9
Mutated 25 63
Table 4 Confusion matrix of the 1p19q predictions
Predicted
Intact Co-deleted
Actu
al
Intact 197 10
Co-deleted 16 10
22
Table 5 Confusion matrix of the grade predictions
Predicted
Grade II Grade III Grade IV
Actu
al Grade II 35 6 6
Grade III 19 10 30
Grade IV 2 5 125
Table 6 Confusion matrix of the WHO 2016 predictions The rsquootherrsquo categoryindicates patients that were predicted as a non-existing WHO 2016 subtype forexample IDH wildtype 1p19q co-deleted tumors Only one patient (TCGA-HT-A5RC) was predicted as a non-existing category It was predicted as anIDH wildtype 1p19q co-deleted grade IV tumor
Predicted
Oligodendrogliom
a
IDH-m
utated
astrocytoma
IDH-w
ildtype
astrocytoma
IDH-m
utated
glioblastoma
IDH-w
ildtype
glioblastoma
Other
Actu
al
Oligodendroglioma 10 8 1 0 7 0
IDH-mutatedastrocytoma 6 34 4 3 10 0
IDH-wildtypeastrocytoma 1 2 3 2 13 1
IDH-mutatedglioblastoma 0 1 0 0 3 0
IDH-wildtypeglioblastoma 0 3 3 1 96 0
Oligodendroglioma are IDH-mutated 1p19q co-deleted grade IIIII giomaIDH-mutated astrocytoma are IDH-mutated 1p19q intact grade IIIII gliomaIDH-wildtype astrocytoma are IDH-wildtype 1p19q intact grade IIIII gliomaIDH-mutated glioblastoma are IDH-mutated grade IV gliomaIDH-wildtype glioblastoma are IDH-wildtype grade IV glioma
23
B Segmentation examples
To demonstrate the automatic segmentations made by our method we randomlyselected five patients from both the TCGA-LGG and the TCGA-GBM datasetThe scans and segmentations of the five patients from the TCGA-LGG datasetand the TCGA-GBM dataset are shown in Figures 9 and 10 respectively TheDICE score Hausdorff distance and volumetric similarity coefficient for thesepatients are given in Table 7 The method seems to mostly focus on the hyper-intensities of the T2w-FLAIR scan Despite the registrations issues that can beseen for the T2w scan in Figure 10d the tumor was still properly segmenteddemonstrating the robustness of our method
Patient DICE HD (mm) VSC
TCGA-LGG
TCGA-DU-7301 089 103 095TCGA-FG-5964 080 58 082TCGA-FG-A713 073 78 088TCGA-HT-7475 087 149 090TCGA-HT-8106 088 112 099
TCGA-GBM
TCGA-02-0037 082 226 099TCGA-08-0353 091 130 098TCGA-12-1094 090 73 093TCGA-14-3477 090 165 099TCGA-19-5951 073 197 073
Table 7 The DICE score Hausdorff distance (HD) and volumetric similaritycoefficient (VSC) for the randomly selected patients from the TCGA-LGG andTCGA-GBM data collections
24
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-DU-7301 from the TCGA-LGG data collection
(b) Patient TCGA-FG-5964 from the TCGA-LGG data collection
(c) Patient TCGA-FG-A713 from the TCGA-LGG data collection
(d) Patient TCGA-HT-7475 from the TCGA-LGG data collection
(e) Patient TCGA-HT-8106 from the TCGA-LGG data collection
Figure 9 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-LGG data collection
25
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-02-0037 from the TCGA-GBM data collection
(b) Patient TCGA-08-0353 from the TCGA-GBM data collection
(c) Patient TCGA-12-1094 from the TCGA-GBM data collection
(d) Patient TCGA-14-3477 from the TCGA-GBM data collection
(e) Patient TCGA-19-5951 from the TCGA-GBM data collection
Figure 10 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-GBM data collection
26
C Prediction results in the test set
27
D Filter output visualizations
Figures 11 and 12 show the output of the convolution filters for the same LGGpatient as shown in Figure 5a and Figures 13 and 14 show the output of theconvolution filters for the same HGG patient as shown in Figure 5b Figures 11and 13 show the outputs of the last convolution layer in the downsample pathat the feature size of 49x61x51 (the fourth convolutional layer in the network)Figures 12 and 14 show the outputs of the last convolution layer in the upsamplepath at the feature size of 49x61x51 (the nineteenth convolutional layer in thenetwork)
Comparing Figure 11 to Figure 12 and Figure 13 to Figure 14 we can seethat the convolutional layers in the upsample path do not keep a lot of detailfor the healthy part of the brain as this region seems blurred However withinthe tumor different regions can still be distinguished The different parts ofthe tumor from the scans can also be seen such as the contrast-enhancing partand the high signal intensity on the T2w-FLAIR For the grade IV glioma inFigure 14 some filters such as filter 26 also seem to focus on the necrotic partof the tumor
28
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 11 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
29
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 12 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
30
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 13 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-06-0238 This is an IDH wildtype grade IV glioma
31
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 14 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-06-0238 This is an IDH wildtype grade IV glioma
32
E Training losses
During the training of the network we used masked categorical cross-entropy lossfor the IDH 1p19q and grade outputs The normal categorical cross-entropyloss is defined as
LCEbatch = minus 1
Nbatch
sumj
sumiisinC
yij log (yij) (1)
where LCEbatch is the total cross-entropy loss over a batch yij is the ground truth
label of sample j for class i yij is the prediction score for sample j for class iC is the set of classes and Nbatch is the number of samples in the batch Here itis assumed that the ground truth labels are one-hot-encoded thus yij is either0 or 1 for each class In our case the ground truth is not known for all sampleswhich can be incorporated in Equation (1) by setting yij to 0 for all classesfor a sample for which the ground truth is not known That sample wouldthen not contribute to the overall loss and would not contribute to the gradientupdate However this can skew the total loss over a batch since the loss is stillaveraged over the total number of samples in a batch regardless of whether theground truth is known resulting in a lower loss for batches that contained moresamples with unknown ground truth Therefore we used a masked categoricalcross-entropy loss
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
yij log (yij) (2)
where
microbatchj =
Nbatchsumij yij
sumi
yij (3)
is the batch weight for sample j In this way the total batch loss is only averagedover the samples that actually have a ground truth
Since there was an imbalance between the number of ground truth samplesfor each class we used class weights to compensate for this imbalance Thusthe loss becomes
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
microclassi yij log (yij) (4)
where
microclassi =
N
Ni |C|(5)
is the class weight for class i N is the total number of samples with knownground truth Ni is the number of samples of class i and |C| is the number ofclasses By determining the class weight in this way we ensured that
microclassi Ni =
N
|C|= constant (6)
33
Thus each class would have the same contribution to the overall loss Theseclass weights were (individually) determined for the IDH output the 1p19qoutput and the grade output
For the segmentation output we used the DICE loss
LDICEbatch =
sumj
1minus 2 middotsumvoxels
k yjk middot yjksumvoxelsk yjk + yjk
(7)
where yjk is the ground truth label in voxel k of sample j and yjk is theprediction score outputted for voxel k of sample j
The total loss that was optimized for the model was a weighted sum of thefour individual losses
Ltotal =summ
micromLm (8)
with
microm =1
Xm (9)
where Lm is the loss for output m microm is the loss weight for loss m (either theIDH 1p19q grade or segmentation loss) and Xm is the number of sampleswith known ground truth for output m In this way we could counteract theeffect of certain outputs having more known labels than other outputs
34
F Parameter tuning
Table 8 Hyperparameters that were tuned and the values that were testedValues in bold show the selected values used in the final model
Tuning parameter Tested values
Dropout rate 015 02 025 030 035 040l2-norm 00001 000001 0000001Learning rate 001 0001 00001 000001 00000001Weight decay 0001 00001 000001Augmentation factor 1 2 3Augmentation probability 025 030 035 040 045
35
G Evaluation metrics
We calculated the AUC accuracy sensitivity and specificity metrics for thegenetic and histological features for the definitions of these metrics see [67]
For the IDH and 1p19q co-deletion outputs the IDH mutated and the1p19q co-deleted samples were regarded as the positive class respectively Sincethe grade was a multi-class problem no single positive class could be determinedFor the prediction of the individual grades that grade was seen as the positiveclass and all other grades as the negative class (eg in the case of the gradeIII prediction grade III was regarded as the positive class and grade II and IVwere regarded as the negative class) For the LGG vs HGG prediction LGGwas considered as the positive class and HGG as the negative class For theevaluation of these metrics for the genetic and histological features only thesubjects with known ground truth were taken into account
The overall AUC for the grade was a multi-class AUC determined in a one-vs-one approach comparing each class against the others in this way thismetric was insensitive to class imbalance [68] A multi-class accuracy was usedto determine the overall accuracy for the grade predictions [67]
To evaluate the performance of the automated segmentation we evaluatedthe DICE score the Hausdorff distance and the volumetric similarity coefficientThe DICE score is a measure of overlap between two segmentations where avalue of 1 indicates perfect overlap and the Hausdorff distance is a measureof the closeness of the borders of the segmentations The volumetric similaritycoefficient is a measure of the agreement between the volumes of two segmen-tations without taking account the actual location of the tumor where a valueof 1 indicates perfect agreement See [65] for the definitions of these metrics
36
H Ground truth labels of patients included frompublic datasets
Acknowledgments
Sebastian van der Voort and Fatih Incekara acknowledge funding by the DutchCancer Society (KWF project number EMCR 2015-7859)
Data used in this publication were generated by the National Cancer Insti-tute Clinical Proteomic Tumor Analysis Consortium (CPTAC)
The results published here are in whole or part based upon data generatedby the TCGA Research Network httpcancergenomenihgov
Author contributions
SRvdV FI WJN MS and SK contrived the study and designed theexperiments FI MMJW JWS RNT GJL PCDWH RSEAJPEV MJvdB and MS included patients in the different studiesSRvdV FI MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV MJvdB and MS collected the dataSRvdV carried out the experiments SRvdV FI MS and SK inter-preted the results SRvdV FI MJvdB MS and SK created the initialdraft of the paper MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV WJN and MJvdB revised the paper
References
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[2] Hendrikus J Dubbink Peggy N Atmodimedjo Johan M Kros Pim JFrench Marc Sanson Ahmed Idbaih Pieter Wesseling Roelien EntingWim Spliet Cees Tijssen Winand N M Dinjens Thierry Gorlia andMartin J van den Bent Molecular classification of anaplastic oligoden-droglioma using next-generation sequencing a report of the prospectiverandomized EORTC brain tumor group 26951 phase III trial Neuro-Oncology 18(3)388ndash400 March 2016 ISSN 1522-8517 URL https
doiorg101093neuoncnov182
[3] Jeanette E Eckel-Passow Daniel H Lachance Annette M Molinaro Kyle MWalsh Paul A Decker Hugues Sicotte Melike Pekmezci Terri Rice Matt LKosel Ivan V Smirnov Gobinda Sarkar Alissa A Caron Thomas M
37
Kollmeyer Corinne E Praska Anisha R Chada Chandralekha Halder He-len M Hansen Lucie S McCoy Paige M Bracci Roxanne Marshall ShichunZheng Gerald F Reis Alexander R Pico Brian P OrsquoNeill Jan C BucknerCaterina Giannini Jason T Huse Arie Perry Tarik Tihan Mitchell SBerger Susan M Chang Michael D Prados Joseph Wiemels John KWiencke Margaret R Wrensch and Robert B Jenkins Glioma groupsbased on 1p19q IDH and TERT promoter mutations in tumors NewEngland Journal of Medicine 372(26)2499ndash2508 June 2015 ISSN 0028-4793 URL httpsdoiorg101056NEJMoa1407279
[4] David N Louis Arie Perry Guido Reifenberger Andreas von Deimling Do-minique Figarella-Branger Webster K Cavenee Hiroko Ohgaki Otmar DWiestler Paul Kleihues and David W Ellison The 2016 world health orga-nization classification of tumors of the central nervous system a summaryActa Neuropathologica 131(6)803ndash820 June 2016 ISSN 0001-6322 URLhttpsdoiorg101007s00401-016-1545-1
[5] Ching-Chang Chen Peng-Wei Hsu Tai-Wei Erich Wu Shih-Tseng LeeChen-Nen Chang Kuo-chen Wei Chih-Cheng Chuang Chieh-Tsai WuTai-Ngar Lui Yung-Hsin Hsu Tzu-Kang Lin Sai-Cheung Lee and Yin-Cheng Huang Stereotactic brain biopsy Single center retrospective anal-ysis of complications Clinical Neurology and Neurosurgery 111(10)835ndash839 December 2009 ISSN 0303-8467 URL httpsdoiorg101016
jclineuro200908013
[6] Robert J Jackson Gregory N Fuller Dima Abi-Said Frederick F LangZiya L Gokaslan Wei Ming Shi David M Wildrick and Raymond SawayaLimitations of stereotactic biopsy in the initial management of gliomasNeuro-Oncology 3(3)193ndash200 July 2001 ISSN 1522-8517 URL https
doiorg101093neuonc33193
[7] M Zhou J Scott B Chaudhury L Hall D Goldgof KW Yeom M IvY Ou J Kalpathy-Cramer S Napel R Gillies O Gevaert and R GatenbyRadiomics in brain tumor Image assessment quantitative feature de-scriptors and machine-learning approaches American Journal of Neu-roradiology 39(2)208ndash216 February 2018 ISSN 0195-6108 URL https
doiorg103174ajnrA5391
[8] Wenya Linda Bi Ahmed Hosny Matthew B Schabath Maryellen LGiger Nicolai J Birkbak Alireza Mehrtash Tavis Allison Omar ArnaoutChristopher Abbosh Ian F Dunn Raymond H Mak Rulla M TamimiClare M Tempany Charles Swanton Udo Hoffmann Lawrence H SchwartzRobert J Gillies Raymond Y Huang and Hugo J W L Aerts Artificialintelligence in cancer imaging Clinical challenges and applications CAA Cancer Journal for Clinicians 69(2)caac21552 February 2019 ISSN0007-9235 URL httpsdoiorg103322caac21552
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[9] Ahmad Chaddad Michael Jonathan Kucharczyk Paul Daniel SihamSabri Bertrand J Jean-Claude Tamim Niazi and Bassam AbdulkarimRadiomics in glioblastoma Current status and challenges facing clinicalimplementation Frontiers in Oncology 9374ndash374 May 2019 ISSN 2234-943X URL httpsdoiorg103389fonc201900374
[10] Marion Smits Imaging of oligodendroglioma The British Journal of Ra-diology 89(1060)20150857 April 2016 ISSN 0007-1285 URL https
doiorg101259bjr20150857
[11] Rachel L Delfanti David E Piccioni Jason Handwerker Naeim BahramiAnithaPriya Krishnan Roshan Karunamuni Jona A Hattangadi-GluthTyler M Seibert Ashwin Srikant Karra A Jones Vivian S Snyder An-ders M Dale Nathan S White Carrie R McDonald and Nikdokht FaridImaging correlates for the 2016 update on WHO classification of gradeIIIII gliomas implications for IDH 1p19q and ATRX status Journal ofNeuro-Oncology 135(3)601ndash609 December 2017 ISSN 0167-594X URLhttpsdoiorg101007s11060-017-2613-7
[12] Sonal Gore Tanay Chougule Jayant Jagtap Jitender Saini and MadhuraIngalhalikar A review of radiomics and deep predictive modeling in gliomacharacterization Academic Radiology July 2020 ISSN 1076-6332 URLhttpsdoiorg101016jacra202006016
[13] Hugo J W L Aerts Emmanuel Rios Velazquez Ralph T H LeijenaarChintan Parmar Patrick Grossmann Sara Carvalho Johan BussinkRene Monshouwer Benjamin Haibe-Kains Derek Rietveld Frank HoebersMichelle M Rietbergen C Rene Leemans Andre Dekker John Quacken-bush Robert J Gillies and Philippe Lambin Decoding tumour pheno-type by noninvasive imaging using a quantitative radiomics approach Na-ture Communications 5(1)4006 September 2014 ISSN 2041-1723 URLhttpsdoiorg101038ncomms5006
[14] Sarah Chihati and Djamel Gaceb A review of recent progress in deeplearning-based methods for MRI brain tumor segmentation In 202011th International Conference on Information and Communication Sys-tems (ICICS) pages 149ndash154 Institute of Electrical and Electronics Engi-neers (IEEE) April 2020 ISBN 9781728162270 URL httpsdoiorg
101109icics494692020239550
[15] Robert J Gillies Paul E Kinahan and Hedvig Hricak Radiomics Imagesare more than pictures they are data Radiology 278(2)563ndash577 Febru-ary 2016 ISSN 0033-8419 URL httpsdoiorg101148radiol
2015151169
[16] James H Thrall Xiang Li Quanzheng Li Cinthia Cruz Synho Do KeithDreyer and James Brink Artificial intelligence and machine learning in ra-diology Opportunities challenges pitfalls and criteria for success Journal
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of the American College of Radiology 15(3 Part B)504ndash508 March 2018ISSN 1546-1440 URL httpsdoiorg101016jjacr201712026
[17] Ahmed Hosny Chintan Parmar John Quackenbush Lawrence H Schwartzand Hugo J W L Aerts Artificial intelligence in radiology Nature ReviewsCancer 18(8)500ndash510 August 2018 ISSN 1474-175X URL https
doiorg101038s41568-018-0016-5
[18] Okan Kopuklu Neslihan Kose Ahmet Gunduz and Gerhard Rigoll Re-source efficient 3D convolutional neural networks In 2019 IEEECVFInternational Conference on Computer Vision Workshop (ICCVW) Insti-tute of Electrical and Electronics Engineers (IEEE) October 2019 ISBN9781728150239 URL httpsdoiorg101109iccvw201900240
[19] S C Thust S Heiland A Falini H R Jager A D Waldman P C SundgrenC Godi V K Katsaros A Ramos N Bargallo M W Vernooij T YousryM Bendszus and M Smits Glioma imaging in europe A survey of 220centres and recommendations for best clinical practice European Radiology28(8)3306ndash3317 August 2018 ISSN 0938-7994 URL httpsdoiorg
101007s00330-018-5314-5
[20] Christian F Freyschlag Sandro M Krieg Johannes Kerschbaumer DanielPinggera Marie-Therese Forster Dominik Cordier Marco Rossi GabrieleMiceli Alexandre Roux Andres Reyes Silvio Sarubbo Anja Smits JoannaSierpowska Pierre A Robe Geert-Jan Rutten Thomas Santarius TomaszMatys Marc Zanello Fabien Almairac Lydiane Mondot Asgeir S JakolaMaria Zetterling Adria Rofes Gord von Campe Remy Guillevin DanieleBagatto Vincent Lubrano Marion Rapp John Goodden Philip C De WittHamer Johan Pallud Lorenzo Bello Claudius Thome Hugues Duffauand Emmanuel Mandonnet Imaging practice in low-grade gliomas amongeuropean specialized centers and proposal for a minimum core of imagingJournal of Neuro-Oncology 139(3)699ndash711 September 2018 ISSN 0167-594X URL httpsdoiorg101007s11060-018-2916-3
[21] M Labussiere A Idbaih X-W Wang Y Marie B Boisselier C FaletS Paris J Laffaire C Carpentier E Criniere F Ducray S El HallaniK Mokhtari K Hoang-Xuan J-Y Delattre and M Sanson All the 1p19qcodeleted gliomas are mutated on IDH1 or IDH2 Neurology 74(23)1886ndash1890 June 2010 ISSN 0028-3878 URL httpsdoiorg101212WNL
0b013e3181e1cf3a
[22] David N Louis Pieter Wesseling Kenneth Aldape Daniel J Brat DavidCapper Ian A Cree Charles Eberhart Dominique Figarella-BrangerMaryam Fouladi Gregory N Fuller Caterina Giannini Christine Haber-ler Cynthia Hawkins Takashi Komori Johan M Kros HK Ng Brent AOrr Sung-Hye Park Werner Paulus Arie Perry Torsten Pietsch GuidoReifenberger Marc Rosenblum Brian Rous Felix Sahm Chitra SarkarDavid A Solomon Uri Tabori Martin J Bent Andreas Deimling Michael
40
Weller Valerie A White and David W Ellison cIMPACT-NOW up-date 6 new entity and diagnostic principle recommendations of thecIMPACT-Utrecht meeting on future CNS tumor classification and grad-ing Brain Pathology 30(4)844ndash856 July 2020 ISSN 1015-6305 URLhttpsdoiorg101111bpa12832
[23] Zhenyu Tang Yuyun Xu Zhicheng Jiao Junfeng Lu Lei Jin Abudumi-jiti Aibaidula Jinsong Wu Qian Wang Han Zhang and Dinggang ShenPre-operative overall survival time prediction for glioblastoma patients us-ing deep learning on both imaging phenotype and genotype In Ding-gang Shen Tianming Liu Terry M Peters Lawrence H Staib CarolineEssert Sean Zhou Pew-Thian Yap and Ali Khan editors Lecture Notesin Computer Science pages 415ndash422 Springer Science and Business Me-dia LLC 2019 ISBN 9783030322380 URL httpsdoiorg101007
978-3-030-32239-7_46
[24] Zhiyuan Xue Bowen Xin Dingqian Wang and Xiuying Wang Radiomics-enhanced multi-task neural network for non-invasive glioma subtypingand segmentation In Hassan Mohy-ud Din and Saima Rathore editorsRadiomics and Radiogenomics in Neuro-oncology pages 81ndash90 SpringerScience and Business Media LLC 2020 ISBN 9783030401238 URLhttpsdoiorg101007978-3-030-40124-5_9
[25] Milan Decuyper Stijn Bonte Karel Deblaere and Roel Van Holen Au-tomated MRI based pipeline for glioma segmentation and prediction ofgrade IDH mutation and 1p19q co-deletion 2020 Preprint at https
arxivorgabs200511965
[26] Stefania Rizzo Francesca Botta Sara Raimondi Daniela Origgi Cris-tiana Fanciullo Alessio Giuseppe Morganti and Massimo Bellomi Ra-diomics the facts and the challenges of image analysis European Ra-diology Experimental 2(1)36 December 2018 ISSN 2509-9280 URLhttpsdoiorg101186s41747-018-0068-z
[27] Philipp Lohmann Norbert Galldiks Martin Kocher Alexander HeinzelChristian P Filss Carina Stegmayr Felix M Mottaghy Gereon R FinkN Jon Shah and Karl-Josef Langen Radiomics in neuro-oncology Basicsworkflow and applications Methods June 2020 ISSN 1046-2023 URLhttpsdoiorg101016jymeth202006003
[28] Stephen S F Yip and Hugo J W L Aerts Applications and limitationsof radiomics Physics in Medicine and Biology 61(13)R150ndashR166 July2016 ISSN 0031-9155 URL httpsdoiorg1010880031-915561
13r150
[29] Annika Malmstrom Ma lgorzata Lysiak Bjarne Winther Kristensen Eliz-abeth Hovey Roger Henriksson and Peter Soderkvist Do we really knowwho has an MGMT methylated glioma Results of an international survey
41
regarding use of MGMT analyses for glioma Neuro-Oncology Practice 7(1)68ndash76 09 2019 ISSN 2054-2577 URL httpsdoiorg101093
nopnpz039
[30] Takahiro Sasaki Manabu Kinoshita Koji Fujita Junya Fukai NobuhideHayashi Yuji Uematsu Yoshiko Okita Masahiro Nonaka Shusuke Mo-riuchi Takehiro Uda Naohiro Tsuyuguchi Hideyuki Arita Kanji MoriKenichi Ishibashi Koji Takano Namiko Nishida Tomoko Shofuda EmaYoshioka Daisuke Kanematsu Yoshinori Kodama Masayuki ManoNaoyuki Nakao and Yonehiro Kanemura Radiomics and MGMT pro-moter methylation for prognostication of newly diagnosed glioblastomaScientific Reports 9(1)14435 December 2019 ISSN 2045-2322 URLhttpsdoiorg101038s41598-019-50849-y
[31] A Gupta A Prager RJ Young W Shi AMP Omuro and JJ GraberDiffusion-weighted MR imaging and MGMT methylation status in glioblas-toma A reappraisal of the role of preoperative quantitative ADC measure-ments American Journal of Neuroradiology 34(1)E10ndashE11 January 2013ISSN 0195-6108 URL httpsdoiorg103174ajnrA3467
[32] JA Carrillo A Lai PL Nghiemphu HJ Kim HS Phillips S KharbandaP Moftakhar S Lalaezari W Yong BM Ellingson TF Cloughesy andWB Pope Relationship between tumor enhancement edema IDH1 muta-tional status MGMT promoter methylation and survival in glioblastomaAmerican Journal of Neuroradiology 33(7)1349ndash1355 August 2012 ISSN0195-6108 URL httpsdoiorg103174ajnrA2950
[33] Vilde Elisabeth Mikkelsen Hong Yan Dai Anne Line Stensjoslashen Erik Mag-nus Berntsen Oslashyvind Salvesen Ole Solheim and Sverre Helge TorpMGMT promoter methylation status is not related to histological or radio-logical features in IDH wild-type glioblastomas Journal of Neuropathologyamp Experimental Neurology 79(8)855ndash862 August 2020 ISSN 0022-3069URL httpsdoiorg101093jnennlaa060
[34] Martin J van den Bent Interobserver variation of the histopathologicaldiagnosis in clinical trials on glioma a clinicianrsquos perspective Acta Neu-ropathologica 120(3)297ndash304 September 2010 ISSN 1432-0533 URLhttpsdoiorg101007s00401-010-0725-7
[35] Ji Eun Park Ho Sung Kim Youngheun Jo Roh-Eul Yoo Seung Hong ChoiSoo Jung Nam and Jeong Hoon Kim Radiomics prognostication modelin glioblastoma using diffusion- and perfusion-weighted MRI ScientificReports 10(1)4250 December 2020 ISSN 2045-2322 URL httpsdoi
org101038s41598-020-61178-w
[36] Minjae Kim So Yeong Jung Ji Eun Park Yeongheun Jo Seo YoungPark Soo Jung Nam Jeong Hoon Kim and Ho Sung Kim Diffusion-and perfusion-weighted MRI radiomics model may predict isocitrate dehy-drogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade
42
glioma European Radiology 30(4)2142ndash2151 April 2020 ISSN 0938-7994URL httpsdoiorg101007s00330-019-06548-3
[37] M Visser DMJ Muller RJM van Duijn M Smits N Verburg EJ HendriksRJA Nabuurs JCJ Bot RS Eijgelaar M Witte MB van Herk F BarkhofPC de WittHamer and JC de Munck Inter-rater agreement in glioma seg-mentations on longitudinal MRI NeuroImage Clinical 22101727 2019ISSN 2213-1582 URL httpsdoiorg101016jnicl2019101727
[38] Kenneth Clark Bruce Vendt Kirk Smith John Freymann Justin KirbyPaul Koppel Stephen Moore Stanley Phillips David Maffitt MichaelPringle Lawrence Tarbox and Fred Prior The cancer imaging archive(TCIA) Maintaining and operating a public information repository Jour-nal of Digital Imaging 26(6)1045ndash1057 December 2013 ISSN 0897-1889URL httpsdoiorg101007s10278-013-9622-7
[39] Lisa Scarpace Adam E Flanders Rajan Jain Tom Mikkelsen and David WAndrews Data from REMBRANDT The Cancer Imaging Archive 2015URL httpsdoiorg107937K9TCIA2015588OZUZB
[40] National Cancer Institute Clinical Proteomic Tumor Analysis Consortium(CPTAC) Radiology data from the clinical proteomic tumor analysisconsortium glioblastoma multiforme CPTAC-GBM collection The CancerImaging Archive 2018 URL httpsdoiorg107937k9tcia2018
3rje41q1
[41] Nameeta Shah Xu Feng Michael Lankerovich Ralph B Puchalski andBart Keogh Data from Ivy GAP The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016XLwaN6nL
[42] Ralph B Puchalski Nameeta Shah Jeremy Miller Rachel Dalley Steve RNomura Jae-Guen Yoon Kimberly A Smith Michael Lankerovich DarrenBertagnolli Kris Bickley Andrew F Boe Krissy Brouner Stephanie But-ler Shiella Caldejon Mike Chapin Suvro Datta Nick Dee Tsega DestaTim Dolbeare Nadezhda Dotson Amanda Ebbert David Feng Xu FengMichael Fisher Garrett Gee Jeff Goldy Lindsey Gourley Benjamin WGregor Guangyu Gu Nika Hejazinia John Hohmann Parvinder HothiRobert Howard Kevin Joines Ali Kriedberg Leonard Kuan Chris LauFelix Lee Hwahyung Lee Tracy Lemon Fuhui Long Naveed Mastan ErikaMott Chantal Murthy Kiet Ngo Eric Olson Melissa Reding Zack RileyDavid Rosen David Sandman Nadiya Shapovalova Clifford R Slaughter-beck Andrew Sodt Graham Stockdale Aaron Szafer Wayne WakemanPaul E Wohnoutka Steven J White Don Marsh Robert C RostomilyLydia Ng Chinh Dang Allan Jones Bart Keogh Haley R GittlemanJill S Barnholtz-Sloan Patrick J Cimino Megha S Uppin C Dirk KeeneFarrokh R Farrokhi Justin D Lathia Michael E Berens Antonio IavaroneAmy Bernard Ed Lein John W Phillips Steven W Rostad Charles Cobbs
43
Michael J Hawrylycz and Greg D Foltz An anatomic transcriptional at-las of human glioblastoma Science 360(6389)660ndash663 May 2018 ISSN0036-8075 URL httpsdoiorg101126scienceaaf2666
[43] Kathleen Schmainda and Melissa Prah Data from Brain-Tumor-Progression The Cancer Imaging Archive 2018 URL httpsdoiorg
107937K9TCIA201815quzvnb
[44] B H Menze A Jakab S Bauer J Kalpathy-Cramer K FarahaniJ Kirby Y Burren N Porz J Slotboom R Wiest L Lanczi E GerstnerM Weber T Arbel B B Avants N Ayache P Buendia D L CollinsN Cordier J J Corso A Criminisi T Das H Delingette C Demi-ralp C R Durst M Dojat S Doyle J Festa F Forbes E GeremiaB Glocker P Golland X Guo A Hamamci K M IftekharuddinR Jena N M John E Konukoglu D Lashkari J A Mariz R MeierS Pereira D Precup S J Price T R Raviv S M S Reza M RyanD Sarikaya L Schwartz H Shin J Shotton C A Silva N SousaN K Subbanna G Szekely T J Taylor O M Thomas N J Tusti-son G Unal F Vasseur M Wintermark D H Ye L Zhao B ZhaoD Zikic M Prastawa M Reyes and K Van Leemput The mul-timodal brain tumor image segmentation benchmark (BRATS) IEEETransactions on Medical Imaging 34(10)1993ndash2024 2015 URL https
doiorg101109TMI20142377694
[45] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin S Kirby John B Freymann Keyvan Farahani and ChristosDavatzikos Advancing the cancer genome atlas glioma MRI collectionswith expert segmentation labels and radiomic features Scientific Data 4(1)170117 December 2017 ISSN 2052-4463 URL httpsdoiorg10
1038sdata2017117
[46] Spyridon Bakas Mauricio Reyes Andras Jakab Stefan Bauer MarkusRempfler Alessandro Crimi Russell Takeshi Shinohara Christoph BergerSung Min Ha Martin Rozycki Marcel Prastawa Esther Alberts JanaLipkova John Freymann Justin Kirby Michel Bilello Hassan Fathallah-Shaykh Roland Wiest Jan Kirschke Benedikt Wiestler Rivka ColenAikaterini Kotrotsou Pamela Lamontagne Daniel Marcus MikhailMilchenko Arash Nazeri Marc-Andre Weber Abhishek Mahajan UjjwalBaid Elizabeth Gerstner Dongjin Kwon Gagan Acharya Manu Agar-wal Mahbubul Alam Alberto Albiol Antonio Albiol Francisco J AlbiolVarghese Alex Nigel Allinson Pedro H A Amorim Abhijit AmrutkarGanesh Anand Simon Andermatt Tal Arbel Pablo Arbelaez Aaron Av-ery Muneeza Azmat Pranjal B W Bai Subhashis Banerjee Bill BarthThomas Batchelder Kayhan Batmanghelich Enzo Battistella AndrewBeers Mikhail Belyaev Martin Bendszus Eze Benson Jose Bernal Ha-landur Nagaraja Bharath George Biros Sotirios Bisdas James BrownMariano Cabezas Shilei Cao Jorge M Cardoso Eric N Carver Adria
44
Casamitjana Laura Silvana Castillo Marcel Cata Philippe Cattin Al-bert Cerigues Vinicius S Chagas Siddhartha Chandra Yi-Ju ChangShiyu Chang Ken Chang Joseph Chazalon Shengcong Chen Wei ChenJefferson W Chen Zhaolin Chen Kun Cheng Ahana Roy ChoudhuryRoger Chylla Albert Clerigues Steven Colleman Ramiro German Ro-driguez Colmeiro Marc Combalia Anthony Costa Xiaomeng Cui Zhen-zhen Dai Lutao Dai Laura Alexandra Daza Eric Deutsch ChangxingDing Chao Dong Shidu Dong Wojciech Dudzik Zach Eaton-Rosen GaryEgan Guilherme Escudero Theo Estienne Richard Everson JonathanFabrizio Yong Fan Longwei Fang Xue Feng Enzo Ferrante Lucas Fi-don Martin Fischer Andrew P French Naomi Fridman Huan Fu DavidFuentes Yaozong Gao Evan Gates David Gering Amir Gholami WilliGierke Ben Glocker Mingming Gong Sandra Gonzalez-Villa T Gros-ges Yuanfang Guan Sheng Guo Sudeep Gupta Woo-Sup Han Il SongHan Konstantin Harmuth Huiguang He Aura Hernandez-Sabate EvelynHerrmann Naveen Himthani Winston Hsu Cheyu Hsu Xiaojun Hu Xi-aobin Hu Yan Hu Yifan Hu Rui Hua Teng-Yi Huang Weilin HuangSabine Van Huffel Quan Huo Vivek HV Khan M Iftekharuddin FabianIsensee Mobarakol Islam Aaron S Jackson Sachin R Jambawalikar An-drew Jesson Weijian Jian Peter Jin V Jeya Maria Jose Alain JungoB Kainz Konstantinos Kamnitsas Po-Yu Kao Ayush Karnawat ThomasKellermeier Adel Kermi Kurt Keutzer Mohamed Tarek Khadir Mahen-dra Khened Philipp Kickingereder Geena Kim Nik King Haley KnappUrspeter Knecht Lisa Kohli Deren Kong Xiangmao Kong Simon Kop-pers Avinash Kori Ganapathy Krishnamurthi Egor Krivov Piyush Ku-mar Kaisar Kushibar Dmitrii Lachinov Tryphon Lambrou Joon LeeChengen Lee Yuehchou Lee M Lee Szidonia Lefkovits Laszlo LefkovitsJames Levitt Tengfei Li Hongwei Li Wenqi Li Hongyang Li XiaochuanLi Yuexiang Li Heng Li Zhenye Li Xiaoyu Li Zeju Li XiaoGang LiWenqi Li Zheng-Shen Lin Fengming Lin Pietro Lio Chang Liu Bo-qiang Liu Xiang Liu Mingyuan Liu Ju Liu Luyan Liu Xavier LladoMarc Moreno Lopez Pablo Ribalta Lorenzo Zhentai Lu Lin Luo ZhigangLuo Jun Ma Kai Ma Thomas Mackie Anant Madabushi Issam Mah-moudi Klaus H Maier-Hein Pradipta Maji CP Mammen Andreas MangB S Manjunath Michal Marcinkiewicz S McDonagh Stephen McKennaRichard McKinley Miriam Mehl Sachin Mehta Raghav Mehta RaphaelMeier Christoph Meinel Dorit Merhof Craig Meyer Robert Miller Sush-mita Mitra Aliasgar Moiyadi David Molina-Garcia Miguel A B Mon-teiro Grzegorz Mrukwa Andriy Myronenko Jakub Nalepa Thuyen NgoDong Nie Holly Ning Chen Niu Nicholas K Nuechterlein Eric Oer-mann Arlindo Oliveira Diego D C Oliveira Arnau Oliver AlexanderF I Osman Yu-Nian Ou Sebastien Ourselin Nikos Paragios Moo SungPark Brad Paschke J Gregory Pauloski Kamlesh Pawar Nick PawlowskiLinmin Pei Suting Peng Silvio M Pereira Julian Perez-Beteta Vic-tor M Perez-Garcia Simon Pezold Bao Pham Ashish Phophalia GemmaPiella G N Pillai Marie Piraud Maxim Pisov Anmol Popli Michael P
45
Pound Reza Pourreza Prateek Prasanna Vesna Prkovska Tony P Prid-more Santi Puch Elodie Puybareau Buyue Qian Xu Qiao MartinRajchl Swapnil Rane Michael Rebsamen Hongliang Ren Xuhua RenKarthik Revanuru Mina Rezaei Oliver Rippel Luis Carlos Rivera Char-lotte Robert Bruce Rosen Daniel Rueckert Mohammed Safwan MostafaSalem Joaquim Salvi Irina Sanchez Irina Sanchez Heitor M Santos Em-mett Sartor Dawid Schellingerhout Klaudius Scheufele Matthew R ScottArtur A Scussel Sara Sedlar Juan Pablo Serrano-Rubio N Jon ShahNameetha Shah Mazhar Shaikh B Uma Shankar Zeina Shboul HaipengShen Dinggang Shen Linlin Shen Haocheng Shen Varun Shenoy FengShi Hyung Eun Shin Hai Shu Diana Sima M Sinclair Orjan SmedbyJames M Snyder Mohammadreza Soltaninejad Guidong Song MehulSoni Jean Stawiaski Shashank Subramanian Li Sun Roger Sun JiaweiSun Kay Sun Yu Sun Guoxia Sun Shuang Sun Yannick R Suter Las-zlo Szilagyi Sanjay Talbar Dacheng Tao Dacheng Tao Zhongzhao TengSiddhesh Thakur Meenakshi H Thakur Sameer Tharakan Pallavi Ti-wari Guillaume Tochon Tuan Tran Yuhsiang M Tsai Kuan-Lun TsengTran Anh Tuan Vadim Turlapov Nicholas Tustison Maria VakalopoulouSergi Valverde Rami Vanguri Evgeny Vasiliev Jonathan Ventura LuisVera Tom Vercauteren C A Verrastro Lasitha Vidyaratne Veronica Vi-laplana Ajeet Vivekanandan Guotai Wang Qian Wang Chiatse J WangWeichung Wang Duo Wang Ruixuan Wang Yuanyuan Wang ChunliangWang Guotai Wang Ning Wen Xin Wen Leon Weninger Wolfgang WickShaocheng Wu Qiang Wu Yihong Wu Yong Xia Yanwu Xu XiaowenXu Peiyuan Xu Tsai-Ling Yang Xiaoping Yang Hao-Yu Yang JunlinYang Haojin Yang Guang Yang Hongdou Yao Xujiong Ye ChangchangYin Brett Young-Moxon Jinhua Yu Xiangyu Yue Songtao Zhang AngelaZhang Kun Zhang Xuejie Zhang Lichi Zhang Xiaoyue Zhang YazhuoZhang Lei Zhang Jianguo Zhang Xiang Zhang Tianhao Zhang SichengZhao Yu Zhao Xiaomei Zhao Liang Zhao Yefeng Zheng Liming ZhongChenhong Zhou Xiaobing Zhou Fan Zhou Hongtu Zhu Jin Zhu YingZhuge Weiwei Zong Jayashree Kalpathy-Cramer Keyvan Farahani Chris-tos Davatzikos Koen van Leemput and Bjoern Menze Identifying the bestmachine learning algorithms for brain tumor segmentation progression as-sessment and overall survival prediction in the BRATS challenge 2018Preprint at httpsarxivorgabs181102629
[47] Nancy Pedano Adam E Flanders Lisa Scarpace Tom Mikkelsen Jen-nifer M Eschbacher Beth Hermes Victor Sisneros Jill Barnholtz-Sloanand Quinn Ostrom Radiology data from the cancer genome atlas lowgrade glioma [TCGA-LGG] collection The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016L4LTD3TK
[48] Lisa Scarpace Tom Mikkelsen Soonmee Cha Sujaya Rao SangeetaTekchandani David Gutman Joel H Saltz Bradley J Erickson NancyPedano Adam E Flanders Jill Barnholtz-Sloan Quinn Ostrom DanielBarboriak and Laura J Pierce Radiology data from the cancer genome
46
atlas glioblastoma multiforme [TCGA-GBM] collection The Cancer Imag-ing Archive 2016 URL httpsdoiorg107937K9TCIA2016
RNYFUYE9
[49] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin Kirby John Freymann Keyvan Farahani and ChristosDavatzikos Segmentation labels and radiomic features for the pre-operativescans of the TCGA-LGG collection [data set] The Cancer Imaging Archive2017 URL httpsdoiorg107937K9TCIA2017GJQ7R0EF
[50] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello Mar-tin Rozycki Justin Kirby John Freymann Keyvan Farahani and Chris-tos Davatzikos Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [data set] The CancerImaging Archive 2017 URL httpsdoiorg107937K9TCIA2017
KLXWJJ1Q
[51] Xiangrui Li Paul S Morgan John Ashburner Jolinda Smith and Christo-pher Rorden The first step for neuroimaging data analysis DICOM toNIfTI conversion Journal of Neuroscience Methods 26447ndash56 May 2016ISSN 0165-0270 URL httpsdoiorg101016jjneumeth201603
001
[52] Vladimir Fonov Alan C Evans Kelly Botteron C Robert Almli Robert CMcKinstry and D Louis Collins Unbiased average age-appropriate at-lases for pediatric studies NeuroImage 54(1)313ndash327 January 2011ISSN 1053-8119 URL httpsdoiorg101016jneuroimage2010
07033
[53] VS Fonov AC Evans RC McKinstry CR Almli and DL Collins Unbiasednonlinear average age-appropriate brain templates from birth to adulthoodNeuroImage 47S102 July 2009 ISSN 1053-8119 URL httpsdoi
org101016S1053-8119(09)70884-5 Organization for Human BrainMapping 2009 Annual Meeting
[54] S Klein M Staring K Murphy MA Viergever and J Pluim elastix Atoolbox for intensity-based medical image registration IEEE Transactionson Medical Imaging 29(1)196ndash205 January 2010 ISSN 0278-0062 URLhttpsdoiorg101109TMI20092035616
[55] Denis Shamonin Fast parallel image registration on CPU and GPU fordiagnostic classification of alzheimerrsquos disease Frontiers in Neuroinfor-matics 750 2013 ISSN 1662-5196 URL httpsdoiorg103389
fninf201300050
[56] Bradley C Lowekamp David T Chen Luis Ibanez and Daniel Blezek Thedesign of SimpleITK Frontiers in Neuroinformatics 745 2013 ISSN1662-5196 URL httpsdoiorg103389fninf201300045
47
[57] Fabian Isensee Marianne Schell Irada Pflueger Gianluca Brugnara DavidBonekamp Ulf Neuberger Antje Wick Heinz-Peter Schlemmer SabineHeiland Wolfgang Wick Martin Bendszus Klaus H Maier-Hein andPhilipp Kickingereder Automated brain extraction of multisequence MRIusing artificial neural networks Human Brain Mapping 40(17)4952ndash4964December 2019 ISSN 1065-9471 URL httpsdoiorg101002hbm
24750
[58] Olaf Ronneberger Invited talk U-net convolutional networks for biomed-ical image segmentation In geb Fritzsche K Maier-Hein geb Lehmann TDeserno Heinz Handels and Thomas Tolxdorff editors Bildverarbeitungfur die Medizin 2017 Informatik aktuell pages 3ndash3 Springer Scienceand Business Media LLC 2017 ISBN 9783662543443 URL https
doiorg101007978-3-662-54345-0_3
[59] Martın Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy DavisJeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving MichaelIsard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry MooreDerek G Murray Benoit Steiner Paul Tucker Vijay Vasudevan PeteWarden Martin Wicke Yuan Yu and Xiaoqiang Zheng Tensor-Flow A system for large-scale machine learning In 12th USENIXSymposium on Operating Systems Design and Implementation (OSDI16) pages 265ndash283 USENIX Association November 2016 ISBN978-1-931971-33-1 URL httpswwwusenixorgconferenceosdi16
technical-sessionspresentationabadi
[60] Dipankar Das Naveen Mellempudi Dheevatsa Mudigere Dhiraj KalamkarSasikanth Avancha Kunal Banerjee Srinivas Sridharan KarthikVaidyanathan Bharat Kaul Evangelos Georganas Alexander HeineckePradeep Dubey Jesus Corbal Nikita Shustrov Roma Dubtsov EvaristFomenko and Vadim Pirogov Mixed precision training of convolutionalneural networks using integer operations In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=H135uzZ0-
[61] Samuel L Smith Pieter-Jan Kindermans and Quoc V Le Donrsquot decaythe learning rate increase the batch size In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=B1Yy1BxCZ
[62] Cliff Woolley NCCL accelarated multi-GPU collective communicationsURL httpsimagesnvidiacomeventssc15pdfsNCCL-Woolley
pdf Accessed on 2020-09-30
[63] Ilya Loshchilov and Frank Hutter Decoupled weight decay regularization2017 Preprint at httpsarxivorgabs171105101
48
[64] F Pedregosa G Varoquaux A Gramfort V Michel B Thirion O GriselM Blondel P Prettenhofer R Weiss V Dubourg J Vanderplas A Pas-sos D Cournapeau M Brucher M Perrot and E Duchesnay Scikit-learn Machine learning in python Journal of Machine Learning Re-search 122825ndash2830 2011 URL httpswwwjmlrorgpapersv12
pedregosa11ahtml
[65] Abdel Aziz Taha and Allan Hanbury Metrics for evaluating 3d medicalimage segmentation analysis selection and tool BMC Medical Imaging15(1)29 August 2015 ISSN 1471-2342 URL httpsdoiorg101186
s12880-015-0068-x
[66] Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viegas and MartinWattenberg SmoothGrad removing noise by adding noise 2017 Preprintat httpsarxivorgabs170603825
[67] Alaa Tharwat Classification assessment methods Applied Computing andInformatics 2018 ISSN 2210-8327 URL httpsdoiorg101016j
aci201808003
[68] David J Hand and Robert J Till A simple generalisation of the areaunder the ROC curve for multiple class classification problems MachineLearning 45(2)171ndash186 November 2001 ISSN 1573-0565 URL https
doiorg101023A1010920819831
49
Data Collection | Patient | IDH_mutated | 1p19q_codeleted | Grade | |||||
BTumorP | PGBM-001 | -1 | -1 | -1 | |||||
BTumorP | PGBM-002 | -1 | -1 | -1 | |||||
BTumorP | PGBM-003 | -1 | -1 | -1 | |||||
BTumorP | PGBM-004 | -1 | -1 | -1 | |||||
BTumorP | PGBM-005 | -1 | -1 | -1 | |||||
BTumorP | PGBM-006 | -1 | -1 | -1 | |||||
BTumorP | PGBM-007 | -1 | -1 | -1 | |||||
BTumorP | PGBM-008 | -1 | -1 | -1 | |||||
BTumorP | PGBM-009 | -1 | -1 | -1 | |||||
BTumorP | PGBM-010 | -1 | -1 | -1 | |||||
BTumorP | PGBM-011 | -1 | -1 | -1 | |||||
BTumorP | PGBM-012 | -1 | -1 | -1 | |||||
BTumorP | PGBM-013 | -1 | -1 | -1 | |||||
BTumorP | PGBM-014 | -1 | -1 | -1 | |||||
BTumorP | PGBM-015 | -1 | -1 | -1 | |||||
BTumorP | PGBM-016 | -1 | -1 | -1 | |||||
BTumorP | PGBM-017 | -1 | -1 | -1 | |||||
BTumorP | PGBM-018 | -1 | -1 | -1 | |||||
BTumorP | PGBM-019 | -1 | -1 | -1 | |||||
BTumorP | PGBM-020 | -1 | -1 | -1 | |||||
BraTS | 2013_0 | -1 | -1 | -1 | |||||
BraTS | 2013_10 | -1 | -1 | -1 | |||||
BraTS | 2013_11 | -1 | -1 | -1 | |||||
BraTS | 2013_12 | -1 | -1 | -1 | |||||
BraTS | 2013_13 | -1 | -1 | -1 | |||||
BraTS | 2013_14 | -1 | -1 | -1 | |||||
BraTS | 2013_15 | -1 | -1 | -1 | |||||
BraTS | 2013_16 | -1 | -1 | -1 | |||||
BraTS | 2013_17 | -1 | -1 | -1 | |||||
BraTS | 2013_18 | -1 | -1 | -1 | |||||
BraTS | 2013_19 | -1 | -1 | -1 | |||||
BraTS | 2013_1 | -1 | -1 | -1 | |||||
BraTS | 2013_20 | -1 | -1 | -1 | |||||
BraTS | 2013_21 | -1 | -1 | -1 | |||||
BraTS | 2013_22 | -1 | -1 | -1 | |||||
BraTS | 2013_23 | -1 | -1 | -1 | |||||
BraTS | 2013_24 | -1 | -1 | -1 | |||||
BraTS | 2013_25 | -1 | -1 | -1 | |||||
BraTS | 2013_26 | -1 | -1 | -1 | |||||
BraTS | 2013_27 | -1 | -1 | -1 | |||||
BraTS | 2013_28 | -1 | -1 | -1 | |||||
BraTS | 2013_29 | -1 | -1 | -1 | |||||
BraTS | 2013_2 | -1 | -1 | -1 | |||||
BraTS | 2013_3 | -1 | -1 | -1 | |||||
BraTS | 2013_4 | -1 | -1 | -1 | |||||
BraTS | 2013_5 | -1 | -1 | -1 | |||||
BraTS | 2013_6 | -1 | -1 | -1 | |||||
BraTS | 2013_7 | -1 | -1 | -1 | |||||
BraTS | 2013_8 | -1 | -1 | -1 | |||||
BraTS | 2013_9 | -1 | -1 | -1 | |||||
BraTS | CBICA_AAB | -1 | -1 | -1 | |||||
BraTS | CBICA_AAG | -1 | -1 | -1 | |||||
BraTS | CBICA_AAL | -1 | -1 | -1 | |||||
BraTS | CBICA_AAP | -1 | -1 | -1 | |||||
BraTS | CBICA_ABB | -1 | -1 | -1 | |||||
BraTS | CBICA_ABE | -1 | -1 | -1 | |||||
BraTS | CBICA_ABM | -1 | -1 | -1 | |||||
BraTS | CBICA_ABN | -1 | -1 | -1 | |||||
BraTS | CBICA_ABO | -1 | -1 | -1 | |||||
BraTS | CBICA_ABY | -1 | -1 | -1 | |||||
BraTS | CBICA_ALN | -1 | -1 | -1 | |||||
BraTS | CBICA_ALU | -1 | -1 | -1 | |||||
BraTS | CBICA_ALX | -1 | -1 | -1 | |||||
BraTS | CBICA_AME | -1 | -1 | -1 | |||||
BraTS | CBICA_AMH | -1 | -1 | -1 | |||||
BraTS | CBICA_ANG | -1 | -1 | -1 | |||||
BraTS | CBICA_ANI | -1 | -1 | -1 | |||||
BraTS | CBICA_ANP | -1 | -1 | -1 | |||||
BraTS | CBICA_ANV | -1 | -1 | -1 | |||||
BraTS | CBICA_ANZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AOC | -1 | -1 | -1 | |||||
BraTS | CBICA_AOD | -1 | -1 | -1 | |||||
BraTS | CBICA_AOH | -1 | -1 | -1 | |||||
BraTS | CBICA_AOO | -1 | -1 | -1 | |||||
BraTS | CBICA_AOP | -1 | -1 | -1 | |||||
BraTS | CBICA_AOS | -1 | -1 | -1 | |||||
BraTS | CBICA_AOZ | -1 | -1 | -1 | |||||
BraTS | CBICA_APK | -1 | -1 | -1 | |||||
BraTS | CBICA_APR | -1 | -1 | -1 | |||||
BraTS | CBICA_APY | -1 | -1 | -1 | |||||
BraTS | CBICA_APZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQA | -1 | -1 | -1 | |||||
BraTS | CBICA_AQD | -1 | -1 | -1 | |||||
BraTS | CBICA_AQG | -1 | -1 | -1 | |||||
BraTS | CBICA_AQJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQN | -1 | -1 | -1 | |||||
BraTS | CBICA_AQO | -1 | -1 | -1 | |||||
BraTS | CBICA_AQP | -1 | -1 | -1 | |||||
BraTS | CBICA_AQQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQR | -1 | -1 | -1 | |||||
BraTS | CBICA_AQT | -1 | -1 | -1 | |||||
BraTS | CBICA_AQU | -1 | -1 | -1 | |||||
BraTS | CBICA_AQV | -1 | -1 | -1 | |||||
BraTS | CBICA_AQY | -1 | -1 | -1 | |||||
BraTS | CBICA_AQZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ARF | -1 | -1 | -1 | |||||
BraTS | CBICA_ARW | -1 | -1 | -1 | |||||
BraTS | CBICA_ARZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ASA | -1 | -1 | -1 | |||||
BraTS | CBICA_ASE | -1 | -1 | -1 | |||||
BraTS | CBICA_ASF | -1 | -1 | -1 | |||||
BraTS | CBICA_ASG | -1 | -1 | -1 | |||||
BraTS | CBICA_ASH | -1 | -1 | -1 | |||||
BraTS | CBICA_ASK | -1 | -1 | -1 | |||||
BraTS | CBICA_ASN | -1 | -1 | -1 | |||||
BraTS | CBICA_ASO | -1 | -1 | -1 | |||||
BraTS | CBICA_ASR | -1 | -1 | -1 | |||||
BraTS | CBICA_ASU | -1 | -1 | -1 | |||||
BraTS | CBICA_ASV | -1 | -1 | -1 | |||||
BraTS | CBICA_ASW | -1 | -1 | -1 | |||||
BraTS | CBICA_ASY | -1 | -1 | -1 | |||||
BraTS | CBICA_ATB | -1 | -1 | -1 | |||||
BraTS | CBICA_ATD | -1 | -1 | -1 | |||||
BraTS | CBICA_ATF | -1 | -1 | -1 | |||||
BraTS | CBICA_ATN | -1 | -1 | -1 | |||||
BraTS | CBICA_ATP | -1 | -1 | -1 | |||||
BraTS | CBICA_ATV | -1 | -1 | -1 | |||||
BraTS | CBICA_ATX | -1 | -1 | -1 | |||||
BraTS | CBICA_AUA | -1 | -1 | -1 | |||||
BraTS | CBICA_AUN | -1 | -1 | -1 | |||||
BraTS | CBICA_AUQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AUR | -1 | -1 | -1 | |||||
BraTS | CBICA_AUW | -1 | -1 | -1 | |||||
BraTS | CBICA_AUX | -1 | -1 | -1 | |||||
BraTS | CBICA_AVB | -1 | -1 | -1 | |||||
BraTS | CBICA_AVF | -1 | -1 | -1 | |||||
BraTS | CBICA_AVG | -1 | -1 | -1 | |||||
BraTS | CBICA_AVJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AVT | -1 | -1 | -1 | |||||
BraTS | CBICA_AVV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWG | -1 | -1 | -1 | |||||
BraTS | CBICA_AWH | -1 | -1 | -1 | |||||
BraTS | CBICA_AWI | -1 | -1 | -1 | |||||
BraTS | CBICA_AWV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWX | -1 | -1 | -1 | |||||
BraTS | CBICA_AXJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXL | -1 | -1 | -1 | |||||
BraTS | CBICA_AXM | -1 | -1 | -1 | |||||
BraTS | CBICA_AXN | -1 | -1 | -1 | |||||
BraTS | CBICA_AXO | -1 | -1 | -1 | |||||
BraTS | CBICA_AXQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXW | -1 | -1 | -1 | |||||
BraTS | CBICA_AYA | -1 | -1 | -1 | |||||
BraTS | CBICA_AYC | -1 | -1 | -1 | |||||
BraTS | CBICA_AYG | -1 | -1 | -1 | |||||
BraTS | CBICA_AYI | -1 | -1 | -1 | |||||
BraTS | CBICA_AYU | -1 | -1 | -1 | |||||
BraTS | CBICA_AYW | -1 | -1 | -1 | |||||
BraTS | CBICA_AZD | -1 | -1 | -1 | |||||
BraTS | CBICA_AZH | -1 | -1 | -1 | |||||
BraTS | CBICA_BAN | -1 | -1 | -1 | |||||
BraTS | CBICA_BAP | -1 | -1 | -1 | |||||
BraTS | CBICA_BAX | -1 | -1 | -1 | |||||
BraTS | CBICA_BBG | -1 | -1 | -1 | |||||
BraTS | CBICA_BCF | -1 | -1 | -1 | |||||
BraTS | CBICA_BCL | -1 | -1 | -1 | |||||
BraTS | CBICA_BDK | -1 | -1 | -1 | |||||
BraTS | CBICA_BEM | -1 | -1 | -1 | |||||
BraTS | CBICA_BFB | -1 | -1 | -1 | |||||
BraTS | CBICA_BFP | -1 | -1 | -1 | |||||
BraTS | CBICA_BGE | -1 | -1 | -1 | |||||
BraTS | CBICA_BGG | -1 | -1 | -1 | |||||
BraTS | CBICA_BGN | -1 | -1 | -1 | |||||
BraTS | CBICA_BGO | -1 | -1 | -1 | |||||
BraTS | CBICA_BGR | -1 | -1 | -1 | |||||
BraTS | CBICA_BGT | -1 | -1 | -1 | |||||
BraTS | CBICA_BGW | -1 | -1 | -1 | |||||
BraTS | CBICA_BGX | -1 | -1 | -1 | |||||
BraTS | CBICA_BHB | -1 | -1 | -1 | |||||
BraTS | CBICA_BHK | -1 | -1 | -1 | |||||
BraTS | CBICA_BHM | -1 | -1 | -1 | |||||
BraTS | CBICA_BHQ | -1 | -1 | -1 | |||||
BraTS | CBICA_BHV | -1 | -1 | -1 | |||||
BraTS | CBICA_BHZ | -1 | -1 | -1 | |||||
BraTS | CBICA_BIC | -1 | -1 | -1 | |||||
BraTS | CBICA_BJY | -1 | -1 | -1 | |||||
BraTS | CBICA_BKV | -1 | -1 | -1 | |||||
BraTS | CBICA_BLJ | -1 | -1 | -1 | |||||
BraTS | CBICA_BNR | -1 | -1 | -1 | |||||
BraTS | TMC_6290 | -1 | -1 | -1 | |||||
BraTS | TMC_6643 | -1 | -1 | -1 | |||||
BraTS | TMC_9043 | -1 | -1 | -1 | |||||
BraTS | TMC_11964 | -1 | -1 | -1 | |||||
BraTS | TMC_12866 | -1 | -1 | -1 | |||||
BraTS | TMC_15477 | -1 | -1 | -1 | |||||
BraTS | TMC_21360 | -1 | -1 | -1 | |||||
BraTS | TMC_27374 | -1 | -1 | -1 | |||||
BraTS | TMC_30014 | -1 | -1 | -1 | |||||
CPTAC-GBM | C3L-00016 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00019 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00265 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00278 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00349 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00424 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00429 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00506 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00528 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00591 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00631 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00636 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00671 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00674 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00677 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01045 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01046 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01142 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01156 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01327 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02041 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02465 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02504 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02704 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02706 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02707 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02708 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03260 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03266 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03727 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03728 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03747 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03748 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-04084 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00661 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00662 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00663 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00665 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01192 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01196 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01505 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01849 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01851 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01852 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02255 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02256 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02286 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03001 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03003 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03755 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-04686 | -1 | -1 | 4 | |||||
IvyGAP | W10 | 1 | 1 | 4 | |||||
IvyGAP | W11 | 0 | 0 | 4 | |||||
IvyGAP | W12 | 0 | 0 | 4 | |||||
IvyGAP | W13 | 0 | 0 | 4 | |||||
IvyGAP | W16 | 0 | 0 | 4 | |||||
IvyGAP | W18 | 0 | 0 | 4 | |||||
IvyGAP | W19 | 0 | 0 | 4 | |||||
IvyGAP | W1 | 0 | 0 | 4 | |||||
IvyGAP | W20 | 0 | 0 | 4 | |||||
IvyGAP | W21 | 0 | 0 | 4 | |||||
IvyGAP | W22 | 0 | 0 | -1 | |||||
IvyGAP | W26 | 0 | -1 | 4 | |||||
IvyGAP | W29 | 0 | 0 | 4 | |||||
IvyGAP | W2 | 0 | 1 | 4 | |||||
IvyGAP | W30 | 0 | 0 | 4 | |||||
IvyGAP | W31 | 1 | 1 | 4 | |||||
IvyGAP | W32 | 0 | 0 | 4 | |||||
IvyGAP | W33 | 0 | 0 | 4 | |||||
IvyGAP | W34 | 0 | 0 | 4 | |||||
IvyGAP | W35 | 1 | 0 | 3 | |||||
IvyGAP | W36 | 0 | 0 | 4 | |||||
IvyGAP | W38 | 0 | 0 | 4 | |||||
IvyGAP | W39 | 0 | 0 | 4 | |||||
IvyGAP | W3 | 1 | 0 | 4 | |||||
IvyGAP | W40 | 0 | 0 | 4 | |||||
IvyGAP | W42 | 0 | -1 | 4 | |||||
IvyGAP | W43 | 0 | -1 | 4 | |||||
IvyGAP | W45 | -1 | -1 | 4 | |||||
IvyGAP | W48 | 0 | -1 | 4 | |||||
IvyGAP | W4 | 1 | 0 | 4 | |||||
IvyGAP | W50 | 0 | -1 | 3 | |||||
IvyGAP | W53 | 1 | -1 | 4 | |||||
IvyGAP | W54 | 0 | -1 | 4 | |||||
IvyGAP | W55 | 0 | -1 | 4 | |||||
IvyGAP | W5 | 0 | 0 | 4 | |||||
IvyGAP | W6 | 0 | 0 | 4 | |||||
IvyGAP | W7 | 0 | 0 | 4 | |||||
IvyGAP | W8 | 0 | 0 | 4 | |||||
IvyGAP | W9 | 0 | 0 | 4 | |||||
REMBRANDT | 900-00-5299 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5303 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5308 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5316 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5317 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5332 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5339 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5341 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5342 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5346 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5380 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5381 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5382 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5385 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5396 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5404 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5412 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5414 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5458 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5459 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5462 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5468 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5476 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5477 | -1 | -1 | 2 | |||||
REMBRANDT | HF0763 | -1 | -1 | -1 | |||||
REMBRANDT | HF0828 | -1 | -1 | 3 | |||||
REMBRANDT | HF0835 | -1 | -1 | 2 | |||||
REMBRANDT | HF0855 | -1 | -1 | 2 | |||||
REMBRANDT | HF0868 | -1 | -1 | -1 | |||||
REMBRANDT | HF0883 | -1 | -1 | -1 | |||||
REMBRANDT | HF0899 | -1 | -1 | 2 | |||||
REMBRANDT | HF0920 | -1 | -1 | 2 | |||||
REMBRANDT | HF0931 | -1 | -1 | 2 | |||||
REMBRANDT | HF0953 | -1 | -1 | 2 | |||||
REMBRANDT | HF0960 | -1 | -1 | 2 | |||||
REMBRANDT | HF0966 | -1 | -1 | 3 | |||||
REMBRANDT | HF0986 | -1 | -1 | 4 | |||||
REMBRANDT | HF0990 | -1 | -1 | 4 | |||||
REMBRANDT | HF1000 | -1 | -1 | 2 | |||||
REMBRANDT | HF1058 | -1 | -1 | 4 | |||||
REMBRANDT | HF1059 | -1 | -1 | 3 | |||||
REMBRANDT | HF1071 | -1 | -1 | 4 | |||||
REMBRANDT | HF1077 | -1 | -1 | 4 | |||||
REMBRANDT | HF1078 | -1 | -1 | 4 | |||||
REMBRANDT | HF1097 | -1 | -1 | 4 | |||||
REMBRANDT | HF1113 | -1 | -1 | -1 | |||||
REMBRANDT | HF1122 | -1 | -1 | 4 | |||||
REMBRANDT | HF1136 | -1 | -1 | 3 | |||||
REMBRANDT | HF1139 | -1 | -1 | 4 | |||||
REMBRANDT | HF1150 | -1 | -1 | 3 | |||||
REMBRANDT | HF1156 | -1 | -1 | 2 | |||||
REMBRANDT | HF1167 | -1 | -1 | 2 | |||||
REMBRANDT | HF1185 | -1 | -1 | 3 | |||||
REMBRANDT | HF1191 | -1 | -1 | 4 | |||||
REMBRANDT | HF1199 | -1 | -1 | -1 | |||||
REMBRANDT | HF1219 | -1 | -1 | 3 | |||||
REMBRANDT | HF1227 | -1 | -1 | 2 | |||||
REMBRANDT | HF1232 | -1 | -1 | 3 | |||||
REMBRANDT | HF1235 | -1 | -1 | 2 | |||||
REMBRANDT | HF1242 | -1 | -1 | 3 | |||||
REMBRANDT | HF1246 | -1 | -1 | 2 | |||||
REMBRANDT | HF1264 | -1 | -1 | 2 | |||||
REMBRANDT | HF1269 | -1 | -1 | 4 | |||||
REMBRANDT | HF1280 | -1 | -1 | 3 | |||||
REMBRANDT | HF1292 | -1 | -1 | 4 | |||||
REMBRANDT | HF1293 | -1 | -1 | -1 | |||||
REMBRANDT | HF1297 | -1 | -1 | 4 | |||||
REMBRANDT | HF1300 | -1 | -1 | -1 | |||||
REMBRANDT | HF1307 | -1 | -1 | -1 | |||||
REMBRANDT | HF1316 | -1 | -1 | 2 | |||||
REMBRANDT | HF1318 | -1 | -1 | -1 | |||||
REMBRANDT | HF1325 | -1 | -1 | 2 | |||||
REMBRANDT | HF1331 | -1 | -1 | -1 | |||||
REMBRANDT | HF1334 | -1 | -1 | 2 | |||||
REMBRANDT | HF1344 | -1 | -1 | 2 | |||||
REMBRANDT | HF1345 | -1 | -1 | 2 | |||||
REMBRANDT | HF1357 | -1 | -1 | 3 | |||||
REMBRANDT | HF1381 | -1 | -1 | 2 | |||||
REMBRANDT | HF1397 | -1 | -1 | 4 | |||||
REMBRANDT | HF1398 | -1 | -1 | 3 | |||||
REMBRANDT | HF1407 | -1 | -1 | 2 | |||||
REMBRANDT | HF1409 | -1 | -1 | 3 | |||||
REMBRANDT | HF1420 | -1 | -1 | -1 | |||||
REMBRANDT | HF1429 | -1 | -1 | -1 | |||||
REMBRANDT | HF1433 | -1 | -1 | 2 | |||||
REMBRANDT | HF1437 | -1 | -1 | -1 | |||||
REMBRANDT | HF1442 | -1 | -1 | 2 | |||||
REMBRANDT | HF1458 | -1 | -1 | 3 | |||||
REMBRANDT | HF1463 | -1 | -1 | 2 | |||||
REMBRANDT | HF1489 | -1 | -1 | 2 | |||||
REMBRANDT | HF1490 | -1 | -1 | 3 | |||||
REMBRANDT | HF1493 | -1 | -1 | -1 | |||||
REMBRANDT | HF1510 | -1 | -1 | -1 | |||||
REMBRANDT | HF1511 | -1 | -1 | 2 | |||||
REMBRANDT | HF1517 | -1 | -1 | 4 | |||||
REMBRANDT | HF1538 | -1 | -1 | 4 | |||||
REMBRANDT | HF1551 | -1 | -1 | 2 | |||||
REMBRANDT | HF1553 | -1 | -1 | 2 | |||||
REMBRANDT | HF1560 | -1 | -1 | 4 | |||||
REMBRANDT | HF1568 | -1 | -1 | 2 | |||||
REMBRANDT | HF1587 | -1 | -1 | 3 | |||||
REMBRANDT | HF1588 | -1 | -1 | 2 | |||||
REMBRANDT | HF1606 | -1 | -1 | 2 | |||||
REMBRANDT | HF1613 | -1 | -1 | 3 | |||||
REMBRANDT | HF1628 | -1 | -1 | 4 | |||||
REMBRANDT | HF1652 | -1 | -1 | -1 | |||||
REMBRANDT | HF1677 | -1 | -1 | 2 | |||||
REMBRANDT | HF1702 | -1 | -1 | 3 | |||||
REMBRANDT | HF1708 | -1 | -1 | 2 | |||||
TCGA-GBM | TCGA-02-0003 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0006 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0009 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0011 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0027 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0033 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0034 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0037 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0046 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0047 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0048 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0054 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0059 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0060 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0064 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0068 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0069 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0070 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0075 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0085 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0086 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0087 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0102 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0106 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0116 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0119 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0122 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0128 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0130 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0132 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0133 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0137 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0138 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0139 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0142 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0145 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0149 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0154 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0158 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0162 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0164 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0166 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0168 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0175 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0176 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0177 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0179 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0182 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0184 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0185 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0187 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0188 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0189 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0190 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0192 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0213 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0238 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0240 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0241 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0644 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0646 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0648 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0649 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1084 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1802 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-2570 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5408 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5412 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5413 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5417 | 1 | -1 | 4 | |||||
TCGA-GBM | TCGA-06-6389 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0350 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0352 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0353 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0354 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0355 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0356 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0357 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0358 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0359 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0360 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0385 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0389 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0392 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0512 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0520 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0521 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0522 | -1 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0524 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0529 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0616 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0776 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1093 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1094 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1098 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1598 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1601 | 0 | -1 | -1 | |||||
TCGA-GBM | TCGA-12-1602 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-3650 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-0789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1456 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1794 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1825 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-3477 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-0963 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2624 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2631 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5951 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5954 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5958 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5960 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1834 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1838 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-2526 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4932 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-76-4934 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4935 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6191 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6193 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6280 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6282 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6285 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6656 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6657 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6661 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6662 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6663 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6664 | 0 | 0 | 4 | |||||
TCGA-LGG | TCGA-CS-4941 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4942 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4944 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5393 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-5395 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5396 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-CS-5397 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6186 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6188 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6290 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6665 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6666 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6667 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-6668 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-CS-6669 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5849 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-5851 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5852 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5854 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5871 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5874 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6397 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6399 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6400 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6404 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6405 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6407 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6410 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6542 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7008 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7010 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7014 | -1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7015 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7018 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7019 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7294 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-7298 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7300 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7302 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7309 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8162 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8164 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-8165 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8167 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8168 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-EZ-7265A | -1 | -1 | -1 | |||||
TCGA-LGG | TCGA-FG-5964 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-6688 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6691 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6692 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-7643 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-A713 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7473 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7616 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7680 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7684 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7686 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7692 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7693 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7694 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7856 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7860 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7874 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7879 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7882 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8105 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-8106 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8107 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8111 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8114 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8563 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A614 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0 | 2 |
Data_collection | Patient | IDH_mutated | Prediction_score_IDH_wildtype | Prediction_score_IDH_mutated | 1p19q_codeleted | Prediction_score_1p19q_codeleted | Prediction_score_1p19q_intact | Grade | Prediction_score_grade_2 | Prediction_score_grade_3 | Prediction_score_grade_4 | ||||||||||||
TCGA-GBM | TCGA-02-0003 | 0 | 099998915 | 10867886E-05 | 0 | 099996686 | 3308471E-05 | 4 | 7377526E-05 | 000074111245 | 099918514 | ||||||||||||
TCGA-GBM | TCGA-02-0006 | 0 | 042321962 | 05767803 | 0 | 068791837 | 031208166 | 4 | 060229343 | 026596427 | 013174225 | ||||||||||||
TCGA-GBM | TCGA-02-0009 | 0 | 099306935 | 0006930672 | 0 | 09906961 | 0009303949 | 4 | 0056565534 | 010282235 | 08406121 | ||||||||||||
TCGA-GBM | TCGA-02-0011 | 0 | 013531776 | 08646823 | 0 | 085318035 | 01468197 | 4 | 0015055533 | 092510724 | 005983725 | ||||||||||||
TCGA-GBM | TCGA-02-0027 | 0 | 09997279 | 000027212297 | 0 | 09986827 | 00013172914 | 4 | 00016104137 | 00038575265 | 0994532 | ||||||||||||
TCGA-GBM | TCGA-02-0033 | 0 | 099974436 | 000025564007 | 0 | 099940693 | 0000593021 | 4 | 00020670628 | 0003761288 | 09941717 | ||||||||||||
TCGA-GBM | TCGA-02-0034 | 0 | 091404164 | 008595832 | 0 | 089209336 | 01079066 | 4 | 00116944825 | 0061110377 | 092719513 | ||||||||||||
TCGA-GBM | TCGA-02-0037 | 0 | 09999577 | 42315594E-05 | 0 | 099992716 | 72827526E-05 | 4 | 82080274E-05 | 0009249337 | 09906686 | ||||||||||||
TCGA-GBM | TCGA-02-0046 | 0 | 0999129 | 00008710656 | 0 | 09989637 | 00010362669 | 4 | 0004290756 | 0022799779 | 097290945 | ||||||||||||
TCGA-GBM | TCGA-02-0047 | 0 | 099991703 | 83008505E-05 | 0 | 09999292 | 70863265E-05 | 4 | 000016252015 | 0040118434 | 095971906 | ||||||||||||
TCGA-GBM | TCGA-02-0048 | 0 | 09998785 | 000012148175 | 0 | 099959475 | 000040527192 | 4 | 00002215901 | 000039696065 | 09993814 | ||||||||||||
TCGA-GBM | TCGA-02-0054 | 0 | 09999831 | 1689829E-05 | 0 | 09999442 | 5583975E-05 | 4 | 00010063206 | 0060579527 | 093841416 | ||||||||||||
TCGA-GBM | TCGA-02-0059 | -1 | 09993749 | 000062511285 | 0 | 09996424 | 00003576683 | 4 | 00007046657 | 0010920537 | 09883748 | ||||||||||||
TCGA-GBM | TCGA-02-0060 | 0 | 07197039 | 028029615 | 0 | 09016612 | 009833879 | 4 | 017739706 | 03728545 | 04497484 | ||||||||||||
TCGA-GBM | TCGA-02-0064 | 0 | 09999083 | 9170197E-05 | 0 | 09995073 | 000049264234 | 4 | 000043781495 | 00028024286 | 099675983 | ||||||||||||
TCGA-GBM | TCGA-02-0068 | 0 | 099187535 | 0008124709 | 0 | 099528164 | 00047183693 | 4 | 00030539853 | 059695286 | 039999318 | ||||||||||||
TCGA-GBM | TCGA-02-0069 | 0 | 09890871 | 0010912909 | 0 | 099704784 | 0002952148 | 4 | 00057067247 | 0061368063 | 09329252 | ||||||||||||
TCGA-GBM | TCGA-02-0070 | 0 | 09940659 | 00059340666 | 0 | 0957794 | 0042206 | 4 | 0008216515 | 003556913 | 09562143 | ||||||||||||
TCGA-GBM | TCGA-02-0075 | 0 | 099933076 | 00006693099 | 0 | 099735296 | 00026470982 | 4 | 000044697264 | 00035736929 | 09959793 | ||||||||||||
TCGA-GBM | TCGA-02-0085 | 0 | 099114406 | 0008855922 | 0 | 09756698 | 002433019 | 4 | 00065203947 | 0035171553 | 095830804 | ||||||||||||
TCGA-GBM | TCGA-02-0086 | 0 | 099965334 | 000034666777 | 0 | 0998698 | 00013019645 | 4 | 000032699382 | 00018025768 | 099787045 | ||||||||||||
TCGA-GBM | TCGA-02-0087 | -1 | 09974885 | 00025114634 | 0 | 09990638 | 000093628286 | 4 | 0007505083 | 0008562708 | 098393226 | ||||||||||||
TCGA-GBM | TCGA-02-0102 | 0 | 09797647 | 0020235319 | 0 | 098292196 | 0017078074 | 4 | 003512482 | 03901857 | 05746895 | ||||||||||||
TCGA-GBM | TCGA-02-0106 | -1 | 099993694 | 6302759E-05 | 0 | 099980897 | 000019110431 | 4 | 60797247E-05 | 00008735659 | 09990657 | ||||||||||||
TCGA-GBM | TCGA-02-0116 | 0 | 09999778 | 22125667E-05 | 0 | 09996886 | 00003113695 | 4 | 000015498884 | 000051770627 | 09993273 | ||||||||||||
TCGA-GBM | TCGA-06-0119 | 0 | 09999362 | 63770494E-05 | 0 | 09999355 | 6452215E-05 | 4 | 000013225728 | 00028902534 | 099697745 | ||||||||||||
TCGA-GBM | TCGA-06-0122 | 0 | 09915298 | 0008470196 | 0 | 09859093 | 00140907345 | 4 | 00121390615 | 027333176 | 071452916 | ||||||||||||
TCGA-GBM | TCGA-06-0128 | 1 | 099988174 | 000011820537 | 0 | 099980634 | 000019373452 | 4 | 000016409029 | 0007865882 | 099197 | ||||||||||||
TCGA-GBM | TCGA-06-0130 | 0 | 099998784 | 12123987E-05 | 0 | 09999323 | 6775062E-05 | 4 | 80872844E-05 | 00026260202 | 099729306 | ||||||||||||
TCGA-GBM | TCGA-06-0132 | 0 | 09998566 | 000014341719 | 0 | 099988496 | 000011501736 | 4 | 000072843547 | 0005115947 | 099415565 | ||||||||||||
TCGA-GBM | TCGA-06-0133 | 0 | 097782 | 002218004 | 0 | 0993807 | 00061929906 | 4 | 0026753133 | 004659919 | 09266477 | ||||||||||||
TCGA-GBM | TCGA-06-0137 | 0 | 096448904 | 003551094 | 0 | 099403125 | 0005968731 | 4 | 000649511 | 038909483 | 060441005 | ||||||||||||
TCGA-GBM | TCGA-06-0138 | 0 | 09977743 | 00022256707 | 0 | 099736834 | 0002631674 | 4 | 00032954598 | 0011606657 | 09850979 | ||||||||||||
TCGA-GBM | TCGA-06-0139 | 0 | 09992649 | 00007350447 | 0 | 099898964 | 00010103129 | 4 | 00021781863 | 00069256434 | 099089617 | ||||||||||||
TCGA-GBM | TCGA-06-0142 | 0 | 099909425 | 00009057334 | 0 | 09985896 | 00014103584 | 4 | 0002598974 | 0046451908 | 09509491 | ||||||||||||
TCGA-GBM | TCGA-06-0145 | 0 | 099964654 | 000035350278 | 0 | 0999652 | 000034802416 | 4 | 00009068022 | 0021991275 | 0977102 | ||||||||||||
TCGA-GBM | TCGA-06-0149 | -1 | 09992161 | 00007839425 | 0 | 09981067 | 00018932257 | 4 | 00057726577 | 0013888515 | 09803388 | ||||||||||||
TCGA-GBM | TCGA-06-0154 | 0 | 099968064 | 000031937403 | 0 | 0999729 | 000027106237 | 4 | 000041507537 | 023430935 | 07652756 | ||||||||||||
TCGA-GBM | TCGA-06-0158 | 0 | 09999199 | 8014118E-05 | 0 | 099992514 | 74846226E-05 | 4 | 00026547876 | 020762624 | 0789719 | ||||||||||||
TCGA-GBM | TCGA-06-0162 | -1 | 099964297 | 00003569706 | 0 | 09997459 | 0000254147 | 4 | 000033955855 | 004318936 | 09564711 | ||||||||||||
TCGA-GBM | TCGA-06-0164 | -1 | 09983991 | 00016009645 | 0 | 09873262 | 0012673735 | 4 | 00016517473 | 00048346478 | 09935136 | ||||||||||||
TCGA-GBM | TCGA-06-0166 | 0 | 099991715 | 82846556E-05 | 0 | 0999554 | 00004459562 | 4 | 000013499439 | 0011635037 | 098823 | ||||||||||||
TCGA-GBM | TCGA-06-0168 | 0 | 09975561 | 00024438864 | 0 | 09964825 | 00035174883 | 4 | 0004766434 | 010448053 | 089075303 | ||||||||||||
TCGA-GBM | TCGA-06-0175 | -1 | 09996252 | 000037482675 | 0 | 09988098 | 00011902251 | 4 | 00026097735 | 004992068 | 094746953 | ||||||||||||
TCGA-GBM | TCGA-06-0176 | 0 | 099550986 | 00044901576 | 0 | 09998872 | 000011279297 | 4 | 0032868527 | 036690876 | 06002227 | ||||||||||||
TCGA-GBM | TCGA-06-0177 | -1 | 081774735 | 018225263 | 0 | 09946464 | 00053536464 | 4 | 0026683953 | 013013016 | 08431859 | ||||||||||||
TCGA-GBM | TCGA-06-0179 | -1 | 09997508 | 000024923254 | 0 | 09989778 | 00010222099 | 4 | 0002628482 | 0004127114 | 099324447 | ||||||||||||
TCGA-GBM | TCGA-06-0182 | -1 | 099999547 | 45838406E-06 | 0 | 099998736 | 12656287E-05 | 4 | 00002591103 | 000018499703 | 09995559 | ||||||||||||
TCGA-GBM | TCGA-06-0184 | 0 | 09935369 | 00064631375 | 0 | 099458355 | 00054164114 | 4 | 0023110552 | 0017436244 | 09594532 | ||||||||||||
TCGA-GBM | TCGA-06-0185 | 0 | 09999337 | 66310655E-05 | 0 | 099986255 | 000013738607 | 4 | 7657532E-05 | 0016089642 | 098383385 | ||||||||||||
TCGA-GBM | TCGA-06-0187 | 0 | 09991689 | 00008312097 | 0 | 099700147 | 00029984985 | 4 | 00020616595 | 0033111423 | 096482694 | ||||||||||||
TCGA-GBM | TCGA-06-0188 | 0 | 09883802 | 0011619771 | 0 | 09826743 | 0017325714 | 4 | 0013776424 | 0112841725 | 087338185 | ||||||||||||
TCGA-GBM | TCGA-06-0189 | 0 | 099906737 | 0000932636 | 0 | 09983865 | 00016135005 | 4 | 00022760795 | 00106745735 | 09870494 | ||||||||||||
TCGA-GBM | TCGA-06-0190 | 0 | 099954176 | 000045831292 | 0 | 09967013 | 00032986512 | 4 | 000040555766 | 0001246768 | 099834764 | ||||||||||||
TCGA-GBM | TCGA-06-0192 | 0 | 09997876 | 00002123566 | 0 | 09992735 | 00007264875 | 4 | 00004505576 | 00014473333 | 09981021 | ||||||||||||
TCGA-GBM | TCGA-06-0213 | 0 | 099986935 | 00001305845 | 0 | 099971646 | 000028351307 | 4 | 8755587E-05 | 00013480412 | 09985644 | ||||||||||||
TCGA-GBM | TCGA-06-0238 | 0 | 09999982 | 17603431E-06 | 0 | 09999894 | 10616134E-05 | 4 | 8076515E-05 | 56053756E-05 | 099986315 | ||||||||||||
TCGA-GBM | TCGA-06-0240 | 0 | 09989956 | 00010044163 | 0 | 099948466 | 00005152657 | 4 | 00016040986 | 021931975 | 077907616 | ||||||||||||
TCGA-GBM | TCGA-06-0241 | 0 | 099959785 | 000040211933 | 0 | 099910825 | 00008917038 | 4 | 00023411359 | 0007850656 | 098980826 | ||||||||||||
TCGA-GBM | TCGA-06-0644 | 0 | 09871044 | 0012895588 | 0 | 09859228 | 00140771745 | 4 | 0013671214 | 009819665 | 088813215 | ||||||||||||
TCGA-GBM | TCGA-06-0646 | 0 | 099959 | 00004100472 | 0 | 099936503 | 000063495064 | 4 | 00019223108 | 0040443853 | 095763385 | ||||||||||||
TCGA-GBM | TCGA-06-0648 | 0 | 09999709 | 29083441E-05 | 0 | 099982435 | 000017571273 | 4 | 000077678583 | 000038868992 | 099883455 | ||||||||||||
TCGA-GBM | TCGA-06-0649 | 0 | 09997805 | 000021952427 | 0 | 099951684 | 000048311835 | 4 | 0042641632 | 00058432207 | 095151514 | ||||||||||||
TCGA-GBM | TCGA-06-1084 | 0 | 099985826 | 000014174655 | 0 | 099968565 | 00003144242 | 4 | 00002676724 | 020492287 | 079480946 | ||||||||||||
TCGA-GBM | TCGA-06-1802 | -1 | 09991928 | 00008072305 | 0 | 09956176 | 0004382337 | 4 | 000043478087 | 00019495043 | 09976157 | ||||||||||||
TCGA-GBM | TCGA-06-2570 | 1 | 096841115 | 0031588882 | 0 | 09842457 | 0015754245 | 4 | 0015369608 | 0030956635 | 09536738 | ||||||||||||
TCGA-GBM | TCGA-06-5408 | 0 | 099857306 | 00014269598 | 0 | 09962638 | 00037362208 | 4 | 00027690146 | 0016195394 | 098103565 | ||||||||||||
TCGA-GBM | TCGA-06-5412 | 0 | 099366105 | 0006338921 | 0 | 099193794 | 0008061992 | 4 | 0011476759 | 006606435 | 09224589 | ||||||||||||
TCGA-GBM | TCGA-06-5413 | 0 | 09994105 | 000058955856 | 0 | 09983026 | 00016974095 | 4 | 00027100197 | 0021083053 | 097620696 | ||||||||||||
TCGA-GBM | TCGA-06-5417 | 1 | 01521267 | 08478733 | -1 | 03064492 | 06935508 | 4 | 013736826 | 037757674 | 048505494 | ||||||||||||
TCGA-GBM | TCGA-06-6389 | 1 | 099987435 | 000012558252 | 0 | 09997017 | 000029827762 | 4 | 00014020519 | 00020044278 | 099659353 | ||||||||||||
TCGA-GBM | TCGA-08-0350 | 0 | 019229275 | 08077072 | 0 | 0033211168 | 09667888 | 4 | 0051619414 | 022280572 | 072557485 | ||||||||||||
TCGA-GBM | TCGA-08-0352 | 0 | 099997497 | 25071595E-05 | 0 | 099992514 | 74846226E-05 | 4 | 000024192198 | 000048111935 | 099927694 | ||||||||||||
TCGA-GBM | TCGA-08-0353 | 0 | 09901496 | 0009850325 | 0 | 09967775 | 0003222484 | 4 | 00053748637 | 0004291497 | 09903336 | ||||||||||||
TCGA-GBM | TCGA-08-0354 | 0 | 076413894 | 023586108 | 0 | 07554566 | 024454337 | 4 | 008784444 | 02004897 | 071166587 | ||||||||||||
TCGA-GBM | TCGA-08-0355 | 0 | 09998349 | 000016506859 | 0 | 099984336 | 000015659066 | 4 | 000076689845 | 0023648744 | 09755844 | ||||||||||||
TCGA-GBM | TCGA-08-0356 | 0 | 097673583 | 0023264103 | 0 | 097773504 | 0022264915 | 4 | 001175834 | 0031075679 | 095716596 | ||||||||||||
TCGA-GBM | TCGA-08-0357 | 0 | 099509466 | 0004905406 | 0 | 099300176 | 00069982093 | 4 | 0005191745 | 0038681854 | 095612645 | ||||||||||||
TCGA-GBM | TCGA-08-0358 | 0 | 099999785 | 2199356E-06 | 0 | 099999034 | 9628425E-06 | 4 | 6113315E-06 | 00011283219 | 09988656 | ||||||||||||
TCGA-GBM | TCGA-08-0359 | 0 | 097885466 | 0021145396 | 0 | 09956006 | 00043994132 | 4 | 0009885523 | 0066605434 | 092350906 | ||||||||||||
TCGA-GBM | TCGA-08-0360 | 0 | 09922444 | 00077555366 | -1 | 09948704 | 00051296344 | 4 | 0013318472 | 003317344 | 095350814 | ||||||||||||
TCGA-GBM | TCGA-08-0385 | 0 | 099605453 | 00039454065 | -1 | 099686414 | 0003135836 | 4 | 00050293226 | 0029977333 | 096499336 | ||||||||||||
TCGA-GBM | TCGA-08-0389 | 0 | 099964714 | 000035281325 | 0 | 09991272 | 000087276706 | 4 | 00017554013 | 00024730961 | 099577147 | ||||||||||||
TCGA-GBM | TCGA-08-0390 | 0 | 099945146 | 000054847915 | 0 | 099936 | 00006399274 | 4 | 00036811908 | 00050958768 | 0991223 | ||||||||||||
TCGA-GBM | TCGA-08-0392 | 0 | 099962366 | 000037629317 | 0 | 09993575 | 00006424303 | 4 | 000036593352 | 0010291994 | 09893421 | ||||||||||||
TCGA-GBM | TCGA-08-0512 | -1 | 09982893 | 00017106998 | 0 | 099193794 | 0008061992 | 4 | 00016200381 | 00027773918 | 09956026 | ||||||||||||
TCGA-GBM | TCGA-08-0520 | -1 | 099603915 | 00039607873 | 0 | 09981933 | 00018066854 | 4 | 00007140295 | 0019064669 | 09802213 | ||||||||||||
TCGA-GBM | TCGA-08-0521 | -1 | 09975274 | 0002472623 | 0 | 099490017 | 00050998176 | 4 | 0001514669 | 0020103427 | 09783819 | ||||||||||||
TCGA-GBM | TCGA-08-0522 | -1 | 099960107 | 000039899128 | -1 | 09992053 | 00007947255 | 4 | 0000269389 | 0006173321 | 09935573 | ||||||||||||
TCGA-GBM | TCGA-08-0524 | -1 | 09964619 | 0003538086 | 0 | 099620515 | 0003794834 | 4 | 000019140428 | 0010096702 | 09897119 | ||||||||||||
TCGA-GBM | TCGA-08-0529 | -1 | 09996567 | 000034329997 | 0 | 099952066 | 00004793605 | 4 | 000032077235 | 0035970636 | 09637086 | ||||||||||||
TCGA-GBM | TCGA-12-0616 | 0 | 098521465 | 0014785408 | 0 | 098704207 | 001295789 | 4 | 001592791 | 012875569 | 08553164 | ||||||||||||
TCGA-GBM | TCGA-12-0776 | -1 | 099899167 | 00010083434 | 0 | 09987031 | 00012968953 | 4 | 0019219175 | 00637484 | 09170324 | ||||||||||||
TCGA-GBM | TCGA-12-0829 | 0 | 099913067 | 00008693674 | 0 | 099821776 | 00017821962 | 4 | 00021031094 | 0055067167 | 09428297 | ||||||||||||
TCGA-GBM | TCGA-12-1093 | 0 | 099992585 | 7411892E-05 | 0 | 09999448 | 5518923E-05 | 4 | 000046803855 | 0012115157 | 098741674 | ||||||||||||
TCGA-GBM | TCGA-12-1094 | -1 | 09980045 | 00019955388 | 0 | 09866105 | 0013389497 | 4 | 00053194338 | 001599471 | 097868586 | ||||||||||||
TCGA-GBM | TCGA-12-1098 | -1 | 09998406 | 000015936712 | 0 | 09977216 | 00022783307 | 4 | 000010218692 | 0035607774 | 09642901 | ||||||||||||
TCGA-GBM | TCGA-12-1598 | 0 | 096309197 | 0036908068 | 0 | 097933435 | 0020665688 | 4 | 0012952217 | 052912676 | 045792103 | ||||||||||||
TCGA-GBM | TCGA-12-1601 | 0 | 09875683 | 0012431651 | -1 | 0991891 | 0008108984 | -1 | 00118053425 | 0105477065 | 088271755 | ||||||||||||
TCGA-GBM | TCGA-12-1602 | 0 | 099830914 | 00016908031 | 0 | 099858415 | 00014158705 | 4 | 0008427611 | 0025996923 | 09655755 | ||||||||||||
TCGA-GBM | TCGA-12-3650 | 0 | 09761519 | 0023848088 | 0 | 097467697 | 0025323058 | 4 | 0010450666 | 043705726 | 05524921 | ||||||||||||
TCGA-GBM | TCGA-14-0789 | 0 | 099856466 | 00014353332 | 0 | 099666256 | 00033374047 | 4 | 0001406897 | 0008273975 | 099031913 | ||||||||||||
TCGA-GBM | TCGA-14-1456 | 1 | 006299064 | 093700933 | 0 | 08656222 | 013437784 | 4 | 016490369 | 047177824 | 036331803 | ||||||||||||
TCGA-GBM | TCGA-14-1794 | 0 | 08579393 | 014206071 | 0 | 09850429 | 0014957087 | 4 | 0023009384 | 009868736 | 08783033 | ||||||||||||
TCGA-GBM | TCGA-14-1825 | 0 | 099960107 | 000039899128 | 0 | 099968123 | 00003187511 | 4 | 0008552247 | 0010156045 | 09812918 | ||||||||||||
TCGA-GBM | TCGA-14-1829 | 0 | 090690076 | 009309922 | 0 | 09907856 | 0009214366 | 4 | 0008461936 | 0102735735 | 088880235 | ||||||||||||
TCGA-GBM | TCGA-14-3477 | 0 | 099796116 | 0002038787 | 0 | 09990728 | 00009271923 | 4 | 00032272525 | 0021644868 | 09751279 | ||||||||||||
TCGA-GBM | TCGA-19-0963 | -1 | 099876726 | 00012327607 | 0 | 09983612 | 00016388679 | 4 | 00031698826 | 013153598 | 086529416 | ||||||||||||
TCGA-GBM | TCGA-19-1390 | 0 | 099913234 | 00008676725 | 0 | 099703634 | 00029636684 | 4 | 00015592943 | 0026028048 | 097241265 | ||||||||||||
TCGA-GBM | TCGA-19-1789 | 0 | 09809491 | 00190509 | 0 | 09915216 | 0008478402 | 4 | 0038703684 | 014341596 | 08178804 | ||||||||||||
TCGA-GBM | TCGA-19-2624 | 0 | 07535573 | 024644265 | 0 | 09816127 | 0018387254 | 4 | 012311598 | 012769651 | 07491875 | ||||||||||||
TCGA-GBM | TCGA-19-2631 | 0 | 099860877 | 0001391234 | 0 | 09981178 | 00018821858 | 4 | 00009839778 | 001843531 | 09805807 | ||||||||||||
TCGA-GBM | TCGA-19-5951 | 0 | 09999031 | 9685608E-05 | 0 | 099977034 | 000022960825 | 4 | 00020246736 | 0004014765 | 09939606 | ||||||||||||
TCGA-GBM | TCGA-19-5954 | 0 | 099456257 | 00054374957 | 0 | 09968273 | 00031726828 | 4 | 00073725334 | 006310084 | 09295266 | ||||||||||||
TCGA-GBM | TCGA-19-5958 | 0 | 099999475 | 5234907E-06 | 0 | 0999941 | 58978338E-05 | 4 | 35422294E-05 | 86819025E-05 | 09998777 | ||||||||||||
TCGA-GBM | TCGA-19-5960 | 0 | 09683962 | 003160382 | 0 | 09013577 | 009864227 | 4 | 0011394806 | 018114014 | 08074651 | ||||||||||||
TCGA-GBM | TCGA-27-1834 | 0 | 099998164 | 18342893E-05 | 0 | 09999685 | 31446623E-05 | 4 | 8611921E-05 | 000031686216 | 0999597 | ||||||||||||
TCGA-GBM | TCGA-27-1838 | 0 | 09993625 | 000063743413 | 0 | 09940428 | 0005957154 | 4 | 00006736379 | 0007191195 | 099213517 | ||||||||||||
TCGA-GBM | TCGA-27-2526 | 0 | 099983776 | 000016219281 | 0 | 09996898 | 000031015594 | 4 | 000016658282 | 00006323714 | 09992011 | ||||||||||||
TCGA-GBM | TCGA-76-4932 | 0 | 09867389 | 0013261103 | -1 | 09949397 | 0005060332 | 4 | 00007321126 | 0003016794 | 099625117 | ||||||||||||
TCGA-GBM | TCGA-76-4934 | 0 | 099318933 | 0006810731 | 0 | 09995073 | 000049264234 | 4 | 00061555947 | 00070025027 | 098684186 | ||||||||||||
TCGA-GBM | TCGA-76-4935 | 0 | 074562997 | 025437003 | 0 | 098242307 | 001757688 | 4 | 076535034 | 006437644 | 017027317 | ||||||||||||
TCGA-GBM | TCGA-76-6191 | 0 | 09981067 | 00018932257 | 0 | 09970879 | 00029121784 | 4 | 00044340584 | 00096095055 | 098595643 | ||||||||||||
TCGA-GBM | TCGA-76-6193 | 0 | 09966168 | 0003383191 | 0 | 099850464 | 00014953383 | 4 | 00037061477 | 007873953 | 09175543 | ||||||||||||
TCGA-GBM | TCGA-76-6280 | 0 | 099948776 | 00005122569 | 0 | 099908185 | 000091819017 | 4 | 00001475792 | 000846075 | 099139166 | ||||||||||||
TCGA-GBM | TCGA-76-6282 | 0 | 0995906 | 0004093958 | 0 | 099861956 | 00013804223 | 4 | 00006694951 | 0009437619 | 09898929 | ||||||||||||
TCGA-GBM | TCGA-76-6285 | 0 | 099949074 | 00005092657 | 0 | 09971661 | 00028338495 | 4 | 00031175872 | 004005614 | 095682627 | ||||||||||||
TCGA-GBM | TCGA-76-6656 | 0 | 09996917 | 000030834455 | 0 | 09983897 | 00016103574 | 4 | 002648366 | 00017969633 | 09717193 | ||||||||||||
TCGA-GBM | TCGA-76-6657 | 0 | 099987245 | 000012755992 | 0 | 099951494 | 00004850083 | 4 | 000096620515 | 0005599633 | 09934342 | ||||||||||||
TCGA-GBM | TCGA-76-6661 | 0 | 093211424 | 006788577 | 0 | 09640178 | 0035982177 | 4 | 003490037 | 0026863772 | 09382358 | ||||||||||||
TCGA-GBM | TCGA-76-6662 | 0 | 096425414 | 0035745807 | 0 | 09963924 | 0003607617 | 4 | 002845819 | 002544755 | 09460942 | ||||||||||||
TCGA-GBM | TCGA-76-6663 | 0 | 088664144 | 0113358565 | 0 | 09984207 | 00015792594 | 4 | 0010206689 | 043740335 | 05523899 | ||||||||||||
TCGA-GBM | TCGA-76-6664 | 0 | 011047115 | 08895289 | 0 | 09559813 | 004401865 | 4 | 00049677677 | 08806894 | 011434281 | ||||||||||||
TCGA-LGG | TCGA-CS-4941 | 0 | 088931274 | 011068726 | 0 | 087037706 | 012962292 | 3 | 002865127 | 0048591908 | 092275685 | ||||||||||||
TCGA-LGG | TCGA-CS-4942 | 1 | 00031327847 | 099686724 | 0 | 096309197 | 0036908068 | 3 | 096261597 | 00148612335 | 0022522787 | ||||||||||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0005265965 | 099473405 | 0 | 09940544 | 00059455987 | 3 | 09439103 | 0023049146 | 003304057 | ||||||||||||
TCGA-LGG | TCGA-CS-4944 | 1 | 009363656 | 09063635 | 0 | 08755211 | 0124478824 | 2 | 034047556 | 033881712 | 03207073 | ||||||||||||
TCGA-LGG | TCGA-CS-5393 | 1 | 009623762 | 09037624 | 0 | 098178816 | 001821182 | 3 | 014111634 | 042021698 | 043866673 | ||||||||||||
TCGA-LGG | TCGA-CS-5395 | 0 | 08502822 | 014971776 | 0 | 09932025 | 00067975316 | 2 | 0052374925 | 018397054 | 076365453 | ||||||||||||
TCGA-LGG | TCGA-CS-5396 | 1 | 099839586 | 00016040892 | 1 | 099967945 | 000032062363 | 3 | 00016345463 | 029090768 | 07074577 | ||||||||||||
TCGA-LGG | TCGA-CS-5397 | 0 | 049304244 | 050695753 | 0 | 08829839 | 0117016025 | 3 | 038702008 | 021211159 | 040086827 | ||||||||||||
TCGA-LGG | TCGA-CS-6186 | 0 | 099913234 | 00008676725 | 0 | 099956185 | 000043818905 | 3 | 00008662089 | 016898473 | 083014905 | ||||||||||||
TCGA-LGG | TCGA-CS-6188 | 0 | 052768165 | 047231838 | 0 | 08584221 | 014157787 | 3 | 019437431 | 047675493 | 03288707 | ||||||||||||
TCGA-LGG | TCGA-CS-6290 | 1 | 09102666 | 008973339 | 0 | 09462997 | 0053700306 | 3 | 0104100704 | 025633416 | 06395651 | ||||||||||||
TCGA-LGG | TCGA-CS-6665 | 1 | 099600047 | 0003999501 | 0 | 099756086 | 00024391294 | 3 | 0011873978 | 001634113 | 097178483 | ||||||||||||
TCGA-LGG | TCGA-CS-6666 | 1 | 021655986 | 07834402 | 0 | 09327296 | 0067270435 | 3 | 017667453 | 036334327 | 045998225 | ||||||||||||
TCGA-LGG | TCGA-CS-6667 | 1 | 012061995 | 087938 | 0 | 095699733 | 0043002643 | 2 | 063733935 | 019323014 | 016943048 | ||||||||||||
TCGA-LGG | TCGA-CS-6668 | 1 | 0076787576 | 09232124 | 1 | 04240933 | 057590663 | 2 | 06810894 | 013706882 | 018184178 | ||||||||||||
TCGA-LGG | TCGA-CS-6669 | 0 | 08488156 | 01511844 | 0 | 094018847 | 005981148 | 2 | 0037862387 | 002352077 | 09386168 | ||||||||||||
TCGA-LGG | TCGA-DU-5849 | 1 | 005773187 | 094226813 | 1 | 08664153 | 013358466 | 2 | 072753835 | 015028271 | 012217898 | ||||||||||||
TCGA-LGG | TCGA-DU-5851 | 1 | 09963994 | 0003600603 | 0 | 099808073 | 00019192374 | 3 | 00060602655 | 012558761 | 08683521 | ||||||||||||
TCGA-LGG | TCGA-DU-5852 | 0 | 09998591 | 000014091856 | 0 | 099954873 | 000045121062 | 3 | 0002267452 | 00038046916 | 099392784 | ||||||||||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0010986943 | 09890131 | 0 | 09549844 | 0045015533 | 2 | 08603989 | 0077804394 | 0061796777 | ||||||||||||
TCGA-LGG | TCGA-DU-5854 | 0 | 09567354 | 0043264627 | 0 | 098768765 | 0012312326 | 3 | 01194655 | 027027336 | 06102612 | ||||||||||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0009312956 | 09906871 | 0 | 046602532 | 053397465 | 3 | 0008289882 | 097042197 | 0021288157 | ||||||||||||
TCGA-LGG | TCGA-DU-5871 | 1 | 005623634 | 09437636 | 0 | 09449439 | 005505607 | 2 | 042517176 | 020180763 | 037302068 | ||||||||||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0062359583 | 09376405 | 0 | 015278916 | 08472108 | 2 | 012133307 | 048199505 | 039667192 | ||||||||||||
TCGA-LGG | TCGA-DU-5874 | 1 | 022858672 | 077141327 | 1 | 06457066 | 03542934 | 2 | 058503634 | 020639434 | 02085693 | ||||||||||||
TCGA-LGG | TCGA-DU-6397 | 1 | 097691274 | 002308724 | 1 | 09908213 | 0009178773 | 3 | 00048094327 | 00412339 | 09539566 | ||||||||||||
TCGA-LGG | TCGA-DU-6399 | 1 | 00023920655 | 099760795 | 0 | 09970073 | 00029926652 | 2 | 098691386 | 0007037292 | 0006048777 | ||||||||||||
TCGA-LGG | TCGA-DU-6400 | 1 | 0030923586 | 09690764 | 1 | 037771282 | 06222872 | 2 | 09710506 | 0015339471 | 001360994 | ||||||||||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0014545513 | 098545444 | 0 | 045332992 | 054667014 | 2 | 0878585 | 006398724 | 005742785 | ||||||||||||
TCGA-LGG | TCGA-DU-6404 | 0 | 08563024 | 014369765 | 0 | 09857318 | 00142681915 | 3 | 0012578745 | 08931047 | 009431658 | ||||||||||||
TCGA-LGG | TCGA-DU-6405 | 0 | 094122344 | 0058776554 | 0 | 09657707 | 0034229323 | 3 | 0015099723 | 0858934 | 012596628 | ||||||||||||
TCGA-LGG | TCGA-DU-6407 | 1 | 00046772743 | 099532276 | 0 | 095787287 | 0042127114 | 2 | 095650303 | 0019410672 | 0024086302 | ||||||||||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0032852467 | 09671475 | 0 | 02978783 | 070212173 | 3 | 046377006 | 04552443 | 008098562 | ||||||||||||
TCGA-LGG | TCGA-DU-6410 | 1 | 084198 | 015801999 | 1 | 09610981 | 0038901985 | 3 | 0029748935 | 0547783 | 042246798 | ||||||||||||
TCGA-LGG | TCGA-DU-6542 | 1 | 099541724 | 0004582765 | 0 | 099690056 | 00030994152 | 3 | 00036504513 | 0033356518 | 0962993 | ||||||||||||
TCGA-LGG | TCGA-DU-7008 | 1 | 00027017966 | 09972982 | 0 | 09924154 | 0007584589 | 2 | 0945233 | 0033200152 | 0021566862 | ||||||||||||
TCGA-LGG | TCGA-DU-7010 | 1 | 09090629 | 0090937115 | 0 | 083999664 | 016000335 | 3 | 0011747591 | 011156695 | 08766855 | ||||||||||||
TCGA-LGG | TCGA-DU-7014 | -1 | 00067384504 | 09932615 | 0 | 09144437 | 008555635 | 2 | 090214694 | 005846623 | 003938676 | ||||||||||||
TCGA-LGG | TCGA-DU-7015 | 1 | 011059116 | 08894088 | 0 | 09457512 | 005424881 | 2 | 04990067 | 023008518 | 027090812 | ||||||||||||
TCGA-LGG | TCGA-DU-7018 | 1 | 06190684 | 038093168 | 1 | 09720721 | 0027927874 | 3 | 002608347 | 03462771 | 06276394 | ||||||||||||
TCGA-LGG | TCGA-DU-7019 | 1 | 006866228 | 09313377 | 0 | 068647516 | 031352484 | 3 | 06280373 | 02546188 | 011734395 | ||||||||||||
TCGA-LGG | TCGA-DU-7294 | 1 | 039513415 | 06048658 | 1 | 04910898 | 05089102 | 2 | 044678423 | 011827048 | 04349453 | ||||||||||||
TCGA-LGG | TCGA-DU-7298 | 1 | 002178117 | 097821885 | 0 | 058896303 | 041103697 | 3 | 04621931 | 040058115 | 013722575 | ||||||||||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0050494254 | 094950575 | 0 | 09805993 | 0019400762 | 3 | 088520575 | 003754964 | 0077244624 | ||||||||||||
TCGA-LGG | TCGA-DU-7300 | 1 | 020334144 | 07966585 | 1 | 021174264 | 078825736 | 3 | 06957292 | 014594184 | 015832895 | ||||||||||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0028517082 | 09714829 | 0 | 07594931 | 024050693 | 2 | 07559878 | 013617343 | 010783881 | ||||||||||||
TCGA-LGG | TCGA-DU-7302 | 1 | 007878401 | 092121595 | 1 | 097414124 | 0025858777 | 3 | 059945434 | 013100924 | 02695364 | ||||||||||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0049359404 | 09506406 | 0 | 09947084 | 0005291605 | 3 | 05746174 | 017312215 | 025226048 | ||||||||||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0774658 | 022534202 | 0 | 09720191 | 0027980946 | 2 | 007909051 | 04979186 | 042299092 | ||||||||||||
TCGA-LGG | TCGA-DU-7309 | 1 | 002068546 | 097931457 | 0 | 091696864 | 008303132 | 3 | 091011685 | 0041825026 | 0048058107 | ||||||||||||
TCGA-LGG | TCGA-DU-8162 | 0 | 019030987 | 08096902 | 0 | 084344435 | 015655571 | 3 | 06724078 | 015660264 | 017098951 | ||||||||||||
TCGA-LGG | TCGA-DU-8164 | 1 | 0026989132 | 09730109 | 1 | 06119184 | 038808158 | 2 | 078654927 | 011851947 | 00949313 | ||||||||||||
TCGA-LGG | TCGA-DU-8165 | 0 | 099918324 | 000081673806 | 0 | 09982692 | 00017308301 | 3 | 00077142627 | 001586733 | 097641844 | ||||||||||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0062617026 | 093738294 | 0 | 052265906 | 047734097 | 2 | 0571523 | 027175376 | 015672325 | ||||||||||||
TCGA-LGG | TCGA-DU-8167 | 1 | 008068282 | 09193171 | 0 | 08626991 | 013730097 | 2 | 07117111 | 014616342 | 014212546 | ||||||||||||
TCGA-LGG | TCGA-DU-8168 | 1 | 04501781 | 05498219 | 1 | 09405718 | 005942822 | 3 | 028535154 | 039651006 | 031813842 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 013576113 | 08642388 | 0 | 098667485 | 0013325148 | 3 | 06805368 | 011191124 | 020755199 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0038810804 | 09611892 | 0 | 094154674 | 005845324 | 2 | 07418394 | 01198958 | 013826479 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 036534345 | 06346565 | 0 | 097664696 | 0023353029 | 2 | 0076500095 | 07058904 | 021760948 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 057493186 | 042506814 | 0 | 08586593 | 014134066 | 3 | 024835269 | 018135522 | 05702921 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 017411166 | 082588834 | 0 | 08903419 | 0109658085 | 2 | 026840523 | 031951824 | 041207647 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 00015382263 | 099846184 | 0 | 09784259 | 0021574067 | 3 | 099424005 | 00014788082 | 0004281163 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 099497885 | 000502115 | 0 | 09904406 | 0009559399 | 3 | 00076062134 | 003340487 | 09589889 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 01338958 | 08661042 | 1 | 010181248 | 08981875 | 2 | 08703488 | 0033631936 | 0096019216 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 007097701 | 092902297 | 1 | 0049773447 | 09502266 | 2 | 08236395 | 0043779366 | 013258114 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 00054852334 | 09945148 | 1 | 00052813343 | 09947187 | 2 | 095740056 | 0030734295 | 0011865048 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 00015218158 | 099847823 | 0 | 09977216 | 00022783307 | 3 | 097611564 | 0011231668 | 0012652792 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 090418625 | 009581377 | 1 | 09320215 | 006797852 | 3 | 015433969 | 006605734 | 0779603 | ||||||||||||
TCGA-LGG | TCGA-EZ-7265A | -1 | 001654544 | 09834546 | -1 | 092290026 | 0077099696 | -1 | 091443384 | 0045349486 | 0040216673 | ||||||||||||
TCGA-LGG | TCGA-FG-5964 | 1 | 095945925 | 004054074 | 1 | 09480585 | 0051941562 | 2 | 0052469887 | 018844457 | 075908554 | ||||||||||||
TCGA-LGG | TCGA-FG-6688 | 0 | 041685596 | 0583144 | 0 | 0400786 | 0599214 | 3 | 032869554 | 028211078 | 03891937 | ||||||||||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0040960647 | 09590394 | 0 | 088871056 | 01112895 | 2 | 078484637 | 01247657 | 009038795 | ||||||||||||
TCGA-LGG | TCGA-FG-6691 | 1 | 00066411127 | 09933589 | 0 | 09705485 | 002945148 | 2 | 082394814 | 01353293 | 004072261 | ||||||||||||
TCGA-LGG | TCGA-FG-6692 | 0 | 099044985 | 0009550158 | 0 | 098370695 | 0016293105 | 3 | 002482948 | 023811981 | 07370507 | ||||||||||||
TCGA-LGG | TCGA-FG-7643 | 0 | 067991304 | 032008696 | 0 | 094600123 | 0053998843 | 2 | 032237333 | 020420441 | 047342223 | ||||||||||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 00037180893 | 09962819 | 0 | 098237246 | 0017627545 | 2 | 09685786 | 0019877713 | 0011543726 | ||||||||||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 0023916386 | 097608364 | 1 | 003330537 | 096669465 | 2 | 016134319 | 0751304 | 0087352775 | ||||||||||||
TCGA-LGG | TCGA-FG-A713 | 1 | 020932822 | 07906717 | 1 | 053740746 | 046259254 | 2 | 068370515 | 013241291 | 018388201 | ||||||||||||
TCGA-LGG | TCGA-HT-7473 | 1 | 026437023 | 073562974 | 0 | 09914391 | 0008560891 | 2 | 009070598 | 05834457 | 032584828 | ||||||||||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0014885316 | 09851147 | 0 | 093397486 | 006602513 | 3 | 09713343 | 0009202645 | 0019462984 | ||||||||||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0078306936 | 09216931 | 0 | 044295275 | 055704725 | 2 | 06683338 | 024550638 | 00861598 | ||||||||||||
TCGA-LGG | TCGA-HT-7616 | 1 | 0994089 | 0005911069 | 1 | 08912444 | 010875558 | 3 | 00015109215 | 00081261955 | 09903628 | ||||||||||||
TCGA-LGG | TCGA-HT-7680 | 0 | 01775255 | 08224745 | 0 | 079779327 | 020220678 | 2 | 06160002 | 021518312 | 016881672 | ||||||||||||
TCGA-LGG | TCGA-HT-7684 | 1 | 099250317 | 00074968883 | 0 | 09977216 | 00022783307 | 3 | 0001585032 | 0011880362 | 09865346 | ||||||||||||
TCGA-LGG | TCGA-HT-7686 | 1 | 043986762 | 05601324 | 0 | 09985134 | 00014866153 | 3 | 08800356 | 0017893802 | 010207067 | ||||||||||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0508178 | 049182203 | 0 | 09986749 | 00013250223 | 3 | 007351807 | 06479417 | 027854022 | ||||||||||||
TCGA-LGG | TCGA-HT-7692 | 1 | 0006764646 | 09932354 | 1 | 00017718028 | 099822825 | 2 | 084003216 | 009709251 | 00628754 | ||||||||||||
TCGA-LGG | TCGA-HT-7693 | 1 | 08835126 | 011648734 | 0 | 098880965 | 0011190402 | 2 | 00389769 | 060259813 | 035842496 | ||||||||||||
TCGA-LGG | TCGA-HT-7694 | 1 | 006299064 | 093700933 | 1 | 06663645 | 03336355 | 3 | 06368097 | 02515797 | 011161056 | ||||||||||||
TCGA-LGG | TCGA-HT-7855 | 1 | 013434944 | 086565053 | 0 | 06805072 | 031949285 | 3 | 03900612 | 035394293 | 02559958 | ||||||||||||
TCGA-LGG | TCGA-HT-7856 | 1 | 0037151825 | 09628482 | 1 | 045108467 | 05489153 | 3 | 0024809493 | 094240344 | 0032787096 | ||||||||||||
TCGA-LGG | TCGA-HT-7860 | 0 | 09996338 | 000036614697 | 0 | 099890125 | 00010987312 | 3 | 00023981468 | 004139189 | 095620996 | ||||||||||||
TCGA-LGG | TCGA-HT-7874 | 1 | 027373514 | 07262649 | 1 | 061277324 | 03872268 | 3 | 030690825 | 04321765 | 026091516 | ||||||||||||
TCGA-LGG | TCGA-HT-7879 | 1 | 006545533 | 09345446 | 0 | 07643643 | 023563562 | 3 | 07316188 | 01349482 | 0133433 | ||||||||||||
TCGA-LGG | TCGA-HT-7882 | 0 | 099826247 | 00017375927 | 0 | 099920684 | 0000793176 | 3 | 00026636408 | 0011237644 | 098609877 | ||||||||||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0045437213 | 09545628 | 0 | 09804874 | 0019512545 | 2 | 067944294 | 020575646 | 011480064 | ||||||||||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0090937115 | 09090629 | 0 | 08061669 | 019383314 | 2 | 069696444 | 017501967 | 012801588 | ||||||||||||
TCGA-LGG | TCGA-HT-8105 | 1 | 09291196 | 0070880495 | 1 | 09865976 | 0013402403 | 3 | 036353382 | 007970196 | 055676425 | ||||||||||||
TCGA-LGG | TCGA-HT-8106 | 1 | 09987081 | 00012918457 | 0 | 099922514 | 00007748164 | 3 | 0019909225 | 0057560045 | 09225307 | ||||||||||||
TCGA-LGG | TCGA-HT-8107 | 0 | 006150854 | 09384914 | 0 | 018944609 | 08105539 | 2 | 071847403 | 015055439 | 013097167 | ||||||||||||
TCGA-LGG | TCGA-HT-8111 | 1 | 062096643 | 037903354 | 0 | 09220272 | 007797278 | 3 | 00015017459 | 090417147 | 009432678 | ||||||||||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0003941571 | 099605846 | 0 | 00025608707 | 099743915 | 2 | 088384247 | 009021307 | 002594447 | ||||||||||||
TCGA-LGG | TCGA-HT-8114 | 1 | 09404078 | 005959219 | 0 | 09970708 | 00029292419 | 3 | 0015292862 | 028022403 | 07044831 | ||||||||||||
TCGA-LGG | TCGA-HT-8563 | 1 | 099999154 | 843094E-06 | 0 | 099999607 | 39515203E-06 | 3 | 32918017E-06 | 031742522 | 068257153 | ||||||||||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 06915494 | 030845058 | 0 | 045883363 | 054116637 | 3 | 012257236 | 02777765 | 059965116 | ||||||||||||
TCGA-LGG | TCGA-HT_A614 | 1 | 08180474 | 018195263 | 0 | 09584989 | 00415011 | 2 | 0067164555 | 0059489973 | 087334543 | ||||||||||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0035779487 | 09642206 | 0 | 07277821 | 0272218 | 2 | 07607039 | 014429174 | 009500429 |
been noted that most current research concerns narrow task-specific methodsthat lack the context between different related tasks which might restrict theperformance of these methods [17]
An important technical limitation when using deep learning methods is thelimited GPU memory which restricts the size of models that can be trained[18] This is a problem especially for clinical data which is often 3D requiringeven more memory than the commonly used 2D networks This further limitsthe size of these models resulting in shallower models and the use of patches ofa scan instead of using the full 3D scan as an input which limits the amount ofcontext these methods can extract from the scans
Here we present a new method that addresses the above problems Ourmethod consists of a single multi-task convolutional neural network (CNN)that can predict the IDH mutation status the 1p19q co-deletion status andthe grade (grade IIIIIIV) of a tumor while also simultaneously segmenting thetumor see Figure 1 To the best of our knowledge this is the first method thatprovides all of this information at the same time allowing clinical experts to de-rive the WHO category from the individually predicted genetic and histologicalfeatures while also allowing them to consider or disregard specific predictionsas they deem fit Exploiting the capabilities of the latest GPUs optimizing ourimplementation to reduce the memory footprint and using distributed multi-GPU training we were able to train a model that uses the full 3D scan as aninput We trained our method using the largest most diverse patient cohortto date with 1508 patients included from 16 different institutes To ensurethe broad applicability of our method we used minimal inclusion criteria onlyrequiring the four most commonly used MRI sequences pre- and post-contrastT1-weighted (T1w) T2-weighted (T2w) and T2-weighted fluid attenuated in-version recovery (T2w-FLAIR) [19 20] No constraints were placed on thepatientsrsquo clinical characteristics such as the tumor grade or the radiologicalcharacteristics of scans such as the scan quality In this way our method couldcapture the heterogeneity that is naturally present in clinical data We testedour method on an independent dataset of 240 patients from 13 different insti-tutes to evaluate the true generalizability of our method Our results show thatwe can predict multiple clinical features of glioma from MRI scans in a diversepatient population
3
Convolutionalneural network
IDH status
Wildtype Mutated
1p19q status
Intact Co-deleted
Grade
II III IV
WHO 2016categorization
MRI scansPreprocessed
scansSegmentation
Figure 1 Overview of our method Pre- and post-contrast T1w T2w and T2w-FLAIR scans are used as an input The scans are registered to an atlas biasfield corrected skull stripped and normalized before being passed through ourconvolutional neural network One branch of the network segments the tumorwhile at the same time the features are combined to predict the IDH status1p19q status and grade of the tumor
4
2 Results
21 Patient characteristics
We included a total of 1748 patients in our study 1508 as a train set and240 as an independent test set The patients in the train set originated fromnine different data collections and 16 different institutes and the test set wascollected from two different data collections and 13 different institutes Table 1provides a full overview of the patient characteristics in the train and test setand Figure 2 shows the inclusion flowchart and the distribution of the patientsover the different data collections in the train set and test set
Table 1 Patient characteristics for the train set and test set
Train set Test setN N
Patients 1508 240IDH status
Mutated 226 150 88 367Wildtype 440 292 129 537Unknown 842 558 23 96
1p19q co-deletion statusCo-deleted 103 68 26 108Intact 337 224 207 863Unknown 1068 708 7 29
GradeII 230 153 47 196III 114 76 59 246IV 830 550 132 550Unknown 334 221 2 08
WHO 2016 categorizationOligodendroglioma 96 64 26 108Astrocytoma IDH wildtype 31 21 22 92Astrocytoma IDH mutated 98 64 57 237GBM IDH wildtype 331 219 106 442GBM IDH mutated 16 11 5 21Unknown 936 621 24 100
SegmentationManual 716 475 240 100Automatic 792 525 0 0
IDH isocitrate dehydrogenase WHO World Health Organization GBMglioblastoma
5
Patient screening
Train set2181 Glioma patients
1241 Erasmus MC491 Haaglanden Medical Center168 BraTS130 REMBRANDT66 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set461 Glioma patients
199 TCGA-LGG262 TCGA-GBM
Patient inclusion
Train set1508 Patients in train set
816 Erasmus MC279 Haaglanden Medical Center168 BraTS109 REMBRANDT51 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set240 Patients in test set
107 TCGA-LGG133 TCGA-GBM
Patient exclusion
Train set673 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
425 Erasmus MC212 Haaglanden Medical Center
0 BraTS21 REMBRANDT15 CPTAC-GBM0 Ivy GAP0 Amsterdam UMC0 Brain-Tumor-Progression0 University Medical Center Utrecht
Test set221 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
92 TCGA-LGG129 TCGA-GBM
Figure 2 Inclusion flowchart of the train set and test set
6
22 Algorithm performance
We used 15 of the train set as a validation set and selected the model pa-rameters that achieved the best performance on this validation set where themodel achieved an area under receiver operating characteristic curve (AUC) of088 for the IDH mutation status prediction an AUC of 076 for the 1p19qco-deletion prediction an AUC of 075 for the grade prediction and a meansegmentation DICE score of 081 The selected model parameters are shown inAppendix F We then trained a model using these parameters and the full trainset and evaluated it on the independent test set
For the genetic and histological feature predictions we achieved an AUC of090 for the IDH mutation status prediction an AUC of 085 for the 1p19qco-deletion prediction and an AUC of 081 for the grade prediction in thetest set The full results are shown in Table 2 with the corresponding receiveroperating characteristic (ROC)-curves in Figure 3 Table 2 also shows the resultsin (clinically relevant) subgroups of patients This shows that we achieved anIDH-AUC of 081 in low grade glioma (LGG) (grade IIIII) an IDH-AUC of064 in high grade glioma (HGG) (grade IV) and a 1p19q-AUC of 073 in LGGWhen only predicting LGG vs HGG instead of predicting the individual gradeswe achieved an AUC of 091 In Appendix A we provide confusion matrices forthe IDH 1p19q and grade predictions as well as a confusion matrix for thefinal WHO 2016 subtype which shows that only one patient was predicted asa non-existing WHO 2016 subtype In Appendix C we provide the individualpredictions and ground truth labels for all patients in the test set to allow forthe calculation of additional metrics
For the automatic segmentation we achieved a mean DICE score of 084 amean Hausdorff distance of 189 mm and a mean volumetric similarity coeffi-cient of 090 Figure 4 shows boxplots of the DICE scores Hausdorff distancesand volumetric similarity coefficients for the different patients in the test set InAppendix B we show five patients that were randomly selected from both theTCGA-LGG and TCGA-GBM data collections to demonstrate the automaticsegmentations made by our method
23 Model interpretability
To provide insight into the behavior of our model we created saliency mapswhich show which parts of the scans contributed the most to the predictionThese saliency maps are shown in Figure 5 for two example patients from thetest set It can be seen that for the LGG the network focused on a bright rim inthe T2w-FLAIR scan whereas for the HGG it focused on the enhancement in thepost-contrast T1w scan To aid further interpretation we provide visualizationsof selected filter outputs in the network in Appendix D which also show thatthe network focuses on the tumor and these filters seem to recognize specificimaging features such as the contrast enhancement and T2w-FLAIR brightness
7
Table 2 Evaluation results of the final model on the test set
Patientgroup
Task AUC Accuracy Sensitivity Specificity
All IDH 090 084 072 0931p19q 085 089 039 095Grade (IIIIIIV) 081 071 NA NAGrade II 091 086 075 089Grade III 069 075 017 094Grade IV 091 082 095 066LGG vs HGG 091 084 072 093
LGG IDH 081 074 073 0771p19q 073 076 039 089
HGG IDH 064 094 040 096
Abbreviations AUC area under receiver operating characteristic curve IDHisocitrate dehydrogenase LGG low grade glioma HGG high grade glioma
Figure 3 Receiver operating characteristic (ROC)-curves of the genetic andhistological features evaluated on the test set The crosses indicate the locationof the decision threshold for the reported accuracy sensitivity and specificity
8
Figure 4 DICE scores Hausdorff distances and volumetric similarity coeffi-cients for all patients in the test set The DICE score is a measure of theoverlap between the ground truth and predicted segmentation (where 1 indi-cates perfect overlap) The Hausdorff distance is a measure of the agreementbetween the boundaries of the ground truth and predicted segmentation (loweris better) The volumetric similarity coefficient is a measure of the agreementin volume (where 1 indicates perfect agreement)
9
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Saliency maps of a low grade glioma patient (TCGA-DU-6400) This is an IDH mu-tated 1p19q co-deleted grade II tumor The network focuses on a rim of brightnessin the T2w-FLAIR scan
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) Saliency maps of a high grade glioma patient (TCGA-06-0238) This is an IDHwildtype grade IV tumor The network focuses on enhancing spots around the necrosison the post-contrast T1w scan
Figure 5 Saliency maps of two patients from the test set showing areas thatare relevant for the prediction
10
24 Model robustness
By not excluding scans from our train set based on radiological characteristicswe were able to make our model robust to low scan quality as can be seen in anexample from the test set in Figure 6 Even though this example scan containedimaging artifacts our method was able to properly segment the tumor (DICEscore of 087) and correctly predict the tumor as an IDH wildtype grade IVtumor
Figure 6 Example of a T2w-FLAIR scan containing imaging artifacts and theautomatic segmentation (red overlay) made by our method It was correctlypredicted as an IDH wildtype grade IV glioma This is patient TCGA-06-5408from the TCGA-GBM collection
Finally we considered two examples of scans that were incorrectly predictedby our method see Figure 7 These two examples were chosen because ournetwork assigned high prediction scores to the wrong classes for these casesFigure 7a shows an example of a grade II IDH mutated 1p19q co-deletedglioma that was predicted as grade IV IDH wildtype by our method Ourmethodrsquos prediction was most likely caused by the hyperintensities in the post-contrast T1w scan being interpreted as contrast enhancement Since these hy-perintensities are also present in the pre-contrast T1w scan they are most likelycalcifications and the radiological appearance of this tumor is indicative of anoligodendroglioma Figure 7b shows an example of a grade IV IDH wildtypeglioma that was predicted as a grade III IDH mutated glioma by our method
11
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) TCGA-DU-6410 from the TCGA-LGG collection The ground truth histopatho-logical analysis indicated this glioma was grade II IDH mutated 1p19q co-deletedbut our method predicted it as a grade IV IDH wildtype
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) TCGA-76-7664 from the TCGA-HGG collection Histopathologically this gliomawas grade IV IDH wildtype but our method predicted it as grade III IDH mutated
Figure 7 Examples of scans that were incorrectly predicted by our method
12
3 Discussion
We have developed a method that can predict the IDH mutation status 1p19qco-deletion status and grade of glioma while simultaneously providing the tu-mor segmentation based on pre-operative MRI scans For the genetic andhistological feature predictions we achieved an AUC of 090 for the IDH mu-tation status prediction an AUC of 085 for the 1p19q co-deletion predictionand an AUC of 081 for the grade prediction in the test set
In an independent test set which contained data from 13 different instituteswe demonstrated that our method predicts these features with good overall per-formance we achieved an AUC of 090 for the IDH mutation status predictionan AUC of 085 for the 1p19q co-deletion prediction and an AUC of 081 forthe grade prediction and a mean whole tumor DICE score of 084 This per-formance on unseen data that was only used during the final evaluation of thealgorithm and that was purposefully not used to guide any decisions regardingthe method design shows the true generalizability of our method Using thelatest GPU capabilities we were able to train a large model which uses thefull 3D scan as input Furthermore by using the largest most diverse patientcohort to date we were able to make our method robust to the heterogeneitythat is naturally present in clinical imaging data such that it generalizes forbroad application in clinical practice
By using a multi-task network our method could learn the context betweendifferent features For example IDH wildtype and 1p19q co-deletion are mu-tually exclusive [21] If two separate methods had been used one to predict theIDH status and one to predict the 1p19q co-deletion status an IDH wildtypeglioma might be predicted to be 1p19q co-deleted which does not stroke withthe clinical reality Since our method learns both of these genetic features si-multaneously it correctly learned not to predict 1p19q co-deletion in tumorsthat were IDH wildtype there was only one patient in which our algorithm pre-dicted a tumor to be both IDH wildtype and 1p19q co-deleted Furthermoreby predicting the genetic and histological features individually instead of onlypredicting the WHO 2016 category it is possible to adopt updated guidelinessuch as cIMPACT-NOW future-proofing our method [22]
Some previous studies also used multi-task networks to predict the geneticand histological features of glioma [23 24 25] Tang et al [23] used a multi-tasknetwork that predicts multiple genetic features as well as the overall survivalof glioblastoma Since their method only works for glioblastoma patients thetumor grade must be known in advance complicating the use of their methodin the pre-operative setting when tumor grade is not yet known Furthermoretheir method requires a tumor segmentation prior to application of their methodwhich is a time-consuming expert task In a study by Xue et al [24] a multi-task network was used with a structure similar to the one proposed in thispaper to segment the tumor and predict the grade (LGG or HGG) and IDHmutation status However they do not predict the 1p19q co-deletion statusneeded for the WHO 2016 categorization Lastly Decuyper et al [25] useda multi-task network that predicts the IDH mutation and 1p19q co-deletion
13
status and the tumor grade (LGG or HGG) Their method requires a tumorsegmentation as input which they obtain from a U-Net that is applied earlierin their pipeline thus their method requires two networks instead of the singlenetwork we use in our method These differences aside the most importantlimitation of each of these studies is the lack of an independent test set forevaluating their results It is now considered essential that an independent testset is used to prevent an overly optimistic estimate of a methodrsquos performance[15 26 27 28] Thus our study improves on this previous work by providinga single network that combines the different tasks being trained on a moreextensive and diverse dataset not requiring a tumor segmentation as an in-put providing all information needed for the WHO 2016 categorization andcrucially by being evaluated in an independent test set
An important genetic feature that is not predicted by our method is theO6-methylguanine-methyltransferase (MGMT) methylation status Althoughthe MGMT methylation status is not part of the WHO 2016 categorizationit is part of clinical management guidelines and is an important prognosticmarker in glioblastoma [4] In the initial stages of this study we attemptedto predict the MGMT methylation status however the performance of thisprediction was poor Furthermore the methylation cutoff level which is usedto determine whether a tumor is MGMT methylated shows a wide varietybetween institutes leading to inconsistent results [29] We therefore opted not toinclude the MGMT prediction at all rather than to provide a poor prediction ofan unsharply defined parameter Although some methods attempted to predictthe MGMT status with varying degrees of success there is still an ongoingdiscussion on the validity of MR imaging features of the MGMT status [23 3031 32 33]
Our method shows good overall performance but there are noticeable per-formance differences between tumor categories For example when our methodpredicts a tumor as an IDH wildtype glioblastoma it is correct almost all of thetime On the other hand it has some difficulty differentiating IDH mutated1p19q co-deleted low-grade glioma from other low-grade glioma The sensitiv-ity for the prediction of grade III glioma was low which might be caused bythe lack of a central pathology review Because of this there were differences inmolecular testing and histological analysis and it is known that distinguishingbetween grade II and grade III has a poor observer reliability [34] Althoughour method can be relevant for certain subgroups our methodrsquos performancestill needs to be improved to ensure relevancy for the full patient population
In future work we aim to increase the performance of our method by includ-ing perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI)since there has been an increasing amount of evidence that these physiologi-cal imaging modalities contain additional information that correlates with thetumorrsquos genetic status and aggressiveness [35 36] They were not included inthis study since PWI and to a lesser extent DWI are not as ingrained in theclinical imaging routine as the structural scans used in this work [19 20] Thusincluding these modalities would limit our methodrsquos clinical applicability andsubstantially reduce the number of patients in the train and test set However
14
PWI and DWI are increasingly becoming more commonplace which will allowincluding these in future research and which might improve performance
In conclusion we have developed a non-invasive method that can predict theIDH mutation status 1p19q co-deletion status and grade of glioma while atthe same time segmenting the tumor based on pre-operative MRI scans withhigh overall performance Although the performance of our method might needto be improved before it will find widespread clinical acceptance we believe thatthis research is an important step forward in the field of radiomics Predict-ing multiple clinical features simultaneously steps away from the conventionalsingle-task methods and is more in line with the clinical practice where multipleclinical features are considered simultaneously and may even be related Fur-thermore by not limiting the patient population used to develop our method toa selection based on clinical or radiological characteristics we alleviate the needfor a priori (expert) knowledge which may not always be available Althoughsteps still have to be taken before radiomics will find its way into the clinicespecially in terms of performance our work provides a crucial step forward byresolving some of the hurdles of clinical implementation now and paving theway for a full transition in the future
4 Methods
41 Patient population
The train set was collected from four in-house datasets and five publicly avail-able datasets In-house datasets were collected from four different institutesErasmus MC (EMC) Haaglanden Medical Center (HMC) Amsterdam UMC(AUMC) [37] and University Medical Center Utrecht (UMCU) Four of the fivepublic datasets were collected from The Cancer Imaging Archive (TCIA) [38]the Repository of Molecular Brain Neoplasia Data (REMBRANDT) collection[39] the Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multi-forme (CPTAC-GBM) collection [40] the Ivy Glioblastoma Atlas Project (IvyGAP) collection [41 42] and the Brain-Tumor-Progression collection [43] Thefifth dataset was the 2019 Brain Tumor Segmentation challenge (BraTS) chal-lenge dataset [44 45 46] from which we excluded the patients that were alsoavailable in the TCGA-LGG and TCGA-GBM collections [47 48]
For the internal datasets from the EMC and the HMC manual segmentationswere available which were made by four different clinical experts For patientswhere segmentations from more than one observer were available we randomlypicked one of the segmentations to use in the train set The segmentationsfrom the AUMC data were made by a single observer of the study by Visseret al [37] From the public datasets only the BraTS dataset and the Brain-Tumor-Progression dataset provided manual segmentations Segmentations ofthe BraTS dataset as provided in the 2019 training and validation set wereused For the Brain-Tumor-Progression dataset the segmentations as providedin the TCIA data collection were used
15
Patients were included if pre-operative pre- and post-contrast T1w T2wand T2w-FLAIR scans were available no further inclusion criteria were setFor example patients were not excluded based on the radiological characteristicsof the scan such as low imaging quality or imaging artifacts or the gliomarsquosclinical characteristics such as the grade If multiple scans of the same contrasttype were available in a single scan session (eg multiple T2w scans) the scanupon which the segmentation was made was selected If no segmentation wasavailable or the segmentation was not made based on that scan contrast thescan with the highest axial resolution was used where a 3D acquisition waspreferred over a 2D acquisition
For the in-house data genetic and histological data were available for theEMC HMC and UMCU dataset which were obtained from analysis of tumortissue after biopsy or resection Genetic and histological data of the publicdatasets were also available for the REMBRANDT CPTAC-GBM and IvyGAP collections Data for the REMBRANDT and CPTAC-GBM collectionswas collected from the clinical data available at the TCIA [40 39] For theIvy GAP collection the genetic and histological data were obtained from theSwedish Institute at httpsivygapswedishorghome
As a test set we used the TCGA-LGG and TCGA-GBM collections from theTCIA [47 48] Genetic and histological labels were obtained from the clinicaldata available at the TCIA Segmentations were used as available from theTCIA based on the 2018 BraTS challenge [45 49 50] The inclusion criteriafor the patients included in the BraTS challenge were the same as our inclusioncriteria the presence of a pre-operative pre- and post-contrast T1w T2w andT2w-FLAIR scan Thus patients from the TCGA-LGG and TCGA-GBM wereincluded if a segmentation from the BraTS challenge was available Howeverfor three patients we found that although they did have manual segmentationsthey did not meet our inclusion requirements TCGA-08-0509 and TCGA-08-0510 from TCGA-GBM because they did not have a pre-contrast T1w scan andTCGA-FG-7634 from TCGA-LGG because there was no post-contrast T1wscan
42 Automatic segmentation in the train set
To present our method with a large diversity in scans we wanted to include asmany patients in the train set as possible from the different datasets Thereforewe performed automatic segmentation in patients that did not have manualsegmentations To this end we used an initial version of our network (presentedin Section 44) without the additional layers that were needed for the predictionof the genetic and histological features This network was initially trained usingall patients in the train set for whom a manual segmentation was availableand this trained network was then applied to all patients for which a manualsegmentation was not available The resulting automatic segmentations wereinspected and if their quality was acceptable they were added to the trainset The network was then trained again using this increased dataset and wasapplied to scans that did not yet have a segmentation of acceptable quality
16
This process was repeated until an acceptable segmentation was available forall patients which constituted our final complete train set
43 Pre-processing
For all datasets except for the BraTS dataset for which the scans were alreadyprovided in NIfTI format the scans were converted from DICOM format toNIfTI format using dcm2niix version v1020190410 [51] We then registeredall scans to the MNI152 T1w and T2w atlases version ICBM 2009a whichhad a resolution of 1x1x1 mm3 and a size of 197x233x189 voxels [52 53] Thescans were affinely registered using Elastix 50 [54 55] The pre- and post-contrast T1w scans were registered to the T1w atlas the T2w and T2w-FLAIRscans were registered to the T2w atlas When a manual segmentation wasavailable for patients from the in-house datasets the registration parametersthat resulted from registering the scan used during the segmentation were usedto transform the segmentation to the atlas In the case of the public datasetswe used the registration parameters of the T2w-FLAIR scans to transform thesegmentations
After the registration all scans were N4 bias field corrected using SimpleITKversion 124 [56] A brain mask was made for the atlas using HD-BET both forthe T1w atlas and the T2w atlas [57] This brain mask was used to skull stripall registered scans and crop them to a bounding box around the brain maskreducing the amount of background present in the scans resulting in a scan sizeof 152x182x145 voxels Subsequently the scans were normalized such that foreach scan the average image intensity was 0 and the standard deviation of theimage intensity was 1 within the brain mask Finally the background outsidethe brain mask was set to the minimum intensity value within the brain mask
Since the segmentation could sometimes be rugged at the edges after regis-tration especially when the segmentations were initially made on low-resolutionscans we smoothed the segmentation using a 3x3x3 median filter (this was onlydone in the train set) For segmentations that contained more than one la-bel eg when the tumor necrosis and enhancement were separately segmentedall labels were collapsed into a single label to obtain a single segmentation ofthe whole tumor The genetic and histological labels and the segmentations ofeach patient were one-hot encoded The four scans ground truth labels andsegmentation of each patient were then used as the input to the network
44 Model
We based the architecture of our model on the U-Net architecture with someadaptations made to allow for a full 3D input and the auxiliary tasks [58] Ournetwork architecture which we have named PrognosAIs Structure-Net or PS-Net for short can be seen in Figure 8
To use the full 3D scan as an input to the network we replaced the firstpooling layer that is usually present in the U-Net with a strided convolutionwith a kernel size of 9x9x9 and a stride of 3x3x3 In the upsampling branch of
17
32
32 64
128
256
512 256
7x8x7256 128
128 64
64 32
32 2
Segmentation
145x182x152
49x61x51
25x31x26
13x16x13
1472
512 2IDH
512 2
1p19q
512 3Grade
Batch normalization Concatenation Convolution amp ReLU3x3x3
Convolution amp Softmax1x1x1
(De)convolution amp ReLU9x9x9
stride 3x3x3
Dense amp ReLU Dense amp Softmax Dropout
Max pooling2x2x2
Up-convolution amp ReLU2x2x2
Global maxpooling
Figure 8 Overview of the PrognosAIs Structure-Net (PS-Net) architecture usedfor our model The numbers below the different layers indicate the number offilters dense units or features at that layer We have also indicated the featuremap size at the different depths of the network
the network the last up-convolution is replaced by a deconvolution with thesame kernel size and stride
At each depth of the network we have added global max-pooling layersdirectly after the dropout layer to obtain imaging features that can be used topredict the genetic and histological features We chose global pooling layers asthey do not introduce any additional parameters that need to be trained thuskeeping the memory required by our model manageable The features from thedifferent depths of the network were concatenated and fed into three differentdense layers one for each of the genetic and histological outputs
l2 kernel regularization was used in all convolutional layers except for thelast convolutional layer used for the output of the segmentation In total thismodel contained 27042473 trainable an 2944 non-trainable parameters
18
45 Model training
Training of the model was done on eight NVidia RTX2080Tirsquos with 11GB ofmemory using TensorFlow 220 [59] To be able to use the full 3D scan asinput to the network without running into memory issues we had to optimizethe memory efficiency of the model training Most importantly we used mixed-precision training which means that most of the variables of the model (such asthe weights) were stored in float16 which requires half the memory of float32which is typically used to store these variables [60] Only the last softmaxactivation layers of each output were stored as float32 We also stored ourpre-processed scans as float16 to further reduce memory usage
However even with these settings we could not use a batch size larger than1 It is known that a larger batch size is preferable as it increases the stabilityof the gradient updates and allows for a better estimation of the normalizationparameters in batch normalization layers [61] Therefore we distributed thetraining over the eight GPUs using the NCCL AllReduce algorithm whichcombines the gradients calculated on each GPU before calculating the updateto the model parameters [62] We also used synchronized batch normalizationlayers which synchronize the updates of their parameters over the distributedmodels In this way our model had a virtual batch size of eight for the gradientupdates and the batch normalization layers parameters
To provide more samples to the algorithm and prevent potential overtrain-ing we applied four types of data augmentation during training croppingrotation brightness shifts and contrast shifts Each augmentation was appliedwith a certain augmentation probability which determined the probability ofthat augmentation type being applied to a specific sample When an image wascropped a random number of voxels between 0 and 20 was cropped from eachdimension and filled with zeros For the random rotation an angle betweenminus30 and 30 degrees was selected from a uniform distribution for each dimen-sion The brightness shift was applied with a delta uniformly drawn between0 and 02 and the contrast shift factor was randomly drawn between 085 and115 We also introduced an augmentation factor which determines how ofteneach sample was parsed as an input sample during a single epoch where eachtime it could be augmented differently
For the IDH 1p19q and grade output we used a masked categorical cross-entropy loss and for the segmentation we used a DICE loss see Appendix E fordetails We used AdamW as an optimizer which has shown improved general-ization performance over Adam by introducing the weight decay parameter as aseparate parameter from the learning rate [63] The learning rate was automat-ically reduced by a factor of 025 if the loss did not improve during the last fiveepochs with a minimum learning rate of 1 middot 10minus11 The model could train for amaximum of 150 epochs and training was stopped early if the average loss overthe last five epochs did not improve Once the model was finished training theweights from the epoch with the lowest loss were restored
19
46 Hyperparameter tuning
Hyperparameters involved in the training of the model needed to be tuned toachieve the best performance We tuned a total of six hyper parameters the l2-norm the dropout rate the augmentation factor the augmentation probabilitythe optimizerrsquos initial learning rate and the optimizerrsquos weight decay A fulloverview of the trained parameters and the values tested for the different settingsis presented in Appendix F
To tune these hyperparameters we split the train set into a hyperparametertraining set (851282 patients of the full train data) and a hyperparametervalidation set (15226 patients of the full train data) Models were trained fordifferent hyperparameter settings via an exhaustive search using the hyperpa-rameter train set and then evaluated on the hyperparameter validation set Nodata augmentation was applied to the hyperparameter validation to ensure thatresults between trained models were comparable The hyperparameters that ledto the lowest overall loss in the hyperparameter validation set were chosen asthe optimal hyperparameters We trained the final model using these optimalhyperparameters and the full train set
47 Post-processing
The predictions of the network were post-processed to obtain the final predictedlabels and segmentations for the samples Since softmax activations were usedfor the genetic and histological outputs a prediction between 0 and 1 was out-putted for each class where the individual predictions summed to 1 The finalpredicted label was then considered as the class with the highest prediction scoreFor the prediction of LGG (grade IIIII) vs HGG (grade IV) the predictionscores of grade II and grade III were combined to obtain the prediction scorefor LGG the prediction score of grade IV was used as the prediction score forHGG If a segmentation contained multiple unconnected components we onlyretained the largest component to obtain a single whole tumor segmentation
48 Model evaluation
The performance of the final trained model was evaluated on the independenttest set comparing the predicted labels with the ground truth labels For thegenetic and histological features we evaluated the AUC the accuracy the sen-sitivity and the specificity using scikit-learn version 0231 for details see Ap-pendix G [64] We evaluated these metrics on the full test set and in subcate-gories relevant to the WHO 2016 guidelines We evaluated the IDH performanceseparately in the LGG (grade IIIII) and HGG (grade IV) subgroups the 1p19qperformance in LGG and we also evaluated the performance of distinguishingbetween LGG and HGG instead of predicting the individual grades
To evaluate the performance of the segmentation we calculated the DICEscores Hausdorff distances and volumetric similarity coefficient comparing theautomatic segmentation of our method and the manual ground truth segmen-
20
tations for all patients in the test set These metrics were calculated usingthe EvaluateSegmentation toolbox version 20170425 [65] for details see Ap-pendix G
To prevent an overly optimistic estimation of our modelrsquos predictive valuewe only evaluated our model on the test set once all hyperparameters werechosen and the final model was trained In this way the performance in thetest set did not influence decisions made during the development of the modelpreventing possible overfitting by fine-tuning to the test set
To gain insight into the model we made saliency maps that show whichparts of the scan contribute the most to the prediction of the CNN [66] Saliencymaps were made using tf-keras-vis 052 changing the activation function of alloutput layers from softmax to linear activations using SmoothGrad to reducethe noisiness of the saliency maps [66]
Another way to gain insight into the networkrsquos behavior is to visualize thefilter outputs of the convolutional layers as they can give some idea as to whatoperations the network applies to the scans We visualized the filter outputs ofthe last convolutional layers in the downsample and upsample path at the firstdepth (at an image size of 49x61x51) of our network These filter outputs werevisualized by passing a sample through the network and showing the convolu-tional layersrsquo outputs replacing the ReLU activation with linear activations
49 Data availability
An overview of the patients included from the public datasets used in the train-ing and testing of the algorithm and their ground truth label is available inAppendix H The data from the public datasets are available in TCIA un-der DOIs 107937K9TCIA2015588OZUZB 107937k9tcia20183rje41q1107937K9TCIA2016XLwaN6nL and 107937K9TCIA201815quzvnb Datafrom the BraTS are available at httpbraintumorsegmentationorg Datafrom the in-house datasets are not publicly available due to participant privacyand consent
410 Code availability
The code used in this paper is available on GitHub under an Apache 2 license athttpsgithubcomSvdvoortPrognosAIs_glioma This code includes thefull pipeline from registration of the patients to the final post-processing of thepredictions The trained model is also available on GitHub along with code toapply it to new patients
21
Appendices
A Confusion matrices
Tables 3 4 and 5 show the confusion matrices for the IDH 1p19q and gradepredictions and Table 6 shows the confusion matrix for the WHO 2016 subtypes
Table 4 shows that the algorithm mainly has difficulty recognizing 1p19qco-deleted tumors which are mostly predicted as 1p19q intact Table 5 showsthat most of the incorrectly predicted grade III tumors are predicted as gradeIV tumors
Table 6 shows that our algorithm often incorrectly predicts IDH-wildtypeastrocytoma as IDH-wildtype glioblastoma The latest cIMPACT-NOW guide-lines propose a new categorization in which IDH-wildtype astrocytoma thatshow either TERT promoter methylation or EFGR gene amplification or chro-mosome 7 gainchromsome 10 loss are classified as IDH-wildtype glioblastoma[22] This new categorization is proposed since the survival of patients withthose IDH-wildtype astrocytoma is similar to the survival of patients with IDH-wildtype glioblastoma [22] From the 13 IDH-wildtype astrocytoma that werewrongly predicted as IDH-wildtype glioblastoma 12 would actually be cate-gorized as IDH-wildtype glioblastoma under this new categorization Thusalthough our method wrongly predicted the WHO 2016 subtype it might ac-tually have picked up on imaging features related to the aggressiveness of thetumor which might lead to a better categorization
Table 3 Confusion matrix of the IDH predictions
Predicted
Wildtype Mutated
Actu
al
Wildtype 120 9
Mutated 25 63
Table 4 Confusion matrix of the 1p19q predictions
Predicted
Intact Co-deleted
Actu
al
Intact 197 10
Co-deleted 16 10
22
Table 5 Confusion matrix of the grade predictions
Predicted
Grade II Grade III Grade IV
Actu
al Grade II 35 6 6
Grade III 19 10 30
Grade IV 2 5 125
Table 6 Confusion matrix of the WHO 2016 predictions The rsquootherrsquo categoryindicates patients that were predicted as a non-existing WHO 2016 subtype forexample IDH wildtype 1p19q co-deleted tumors Only one patient (TCGA-HT-A5RC) was predicted as a non-existing category It was predicted as anIDH wildtype 1p19q co-deleted grade IV tumor
Predicted
Oligodendrogliom
a
IDH-m
utated
astrocytoma
IDH-w
ildtype
astrocytoma
IDH-m
utated
glioblastoma
IDH-w
ildtype
glioblastoma
Other
Actu
al
Oligodendroglioma 10 8 1 0 7 0
IDH-mutatedastrocytoma 6 34 4 3 10 0
IDH-wildtypeastrocytoma 1 2 3 2 13 1
IDH-mutatedglioblastoma 0 1 0 0 3 0
IDH-wildtypeglioblastoma 0 3 3 1 96 0
Oligodendroglioma are IDH-mutated 1p19q co-deleted grade IIIII giomaIDH-mutated astrocytoma are IDH-mutated 1p19q intact grade IIIII gliomaIDH-wildtype astrocytoma are IDH-wildtype 1p19q intact grade IIIII gliomaIDH-mutated glioblastoma are IDH-mutated grade IV gliomaIDH-wildtype glioblastoma are IDH-wildtype grade IV glioma
23
B Segmentation examples
To demonstrate the automatic segmentations made by our method we randomlyselected five patients from both the TCGA-LGG and the TCGA-GBM datasetThe scans and segmentations of the five patients from the TCGA-LGG datasetand the TCGA-GBM dataset are shown in Figures 9 and 10 respectively TheDICE score Hausdorff distance and volumetric similarity coefficient for thesepatients are given in Table 7 The method seems to mostly focus on the hyper-intensities of the T2w-FLAIR scan Despite the registrations issues that can beseen for the T2w scan in Figure 10d the tumor was still properly segmenteddemonstrating the robustness of our method
Patient DICE HD (mm) VSC
TCGA-LGG
TCGA-DU-7301 089 103 095TCGA-FG-5964 080 58 082TCGA-FG-A713 073 78 088TCGA-HT-7475 087 149 090TCGA-HT-8106 088 112 099
TCGA-GBM
TCGA-02-0037 082 226 099TCGA-08-0353 091 130 098TCGA-12-1094 090 73 093TCGA-14-3477 090 165 099TCGA-19-5951 073 197 073
Table 7 The DICE score Hausdorff distance (HD) and volumetric similaritycoefficient (VSC) for the randomly selected patients from the TCGA-LGG andTCGA-GBM data collections
24
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-DU-7301 from the TCGA-LGG data collection
(b) Patient TCGA-FG-5964 from the TCGA-LGG data collection
(c) Patient TCGA-FG-A713 from the TCGA-LGG data collection
(d) Patient TCGA-HT-7475 from the TCGA-LGG data collection
(e) Patient TCGA-HT-8106 from the TCGA-LGG data collection
Figure 9 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-LGG data collection
25
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-02-0037 from the TCGA-GBM data collection
(b) Patient TCGA-08-0353 from the TCGA-GBM data collection
(c) Patient TCGA-12-1094 from the TCGA-GBM data collection
(d) Patient TCGA-14-3477 from the TCGA-GBM data collection
(e) Patient TCGA-19-5951 from the TCGA-GBM data collection
Figure 10 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-GBM data collection
26
C Prediction results in the test set
27
D Filter output visualizations
Figures 11 and 12 show the output of the convolution filters for the same LGGpatient as shown in Figure 5a and Figures 13 and 14 show the output of theconvolution filters for the same HGG patient as shown in Figure 5b Figures 11and 13 show the outputs of the last convolution layer in the downsample pathat the feature size of 49x61x51 (the fourth convolutional layer in the network)Figures 12 and 14 show the outputs of the last convolution layer in the upsamplepath at the feature size of 49x61x51 (the nineteenth convolutional layer in thenetwork)
Comparing Figure 11 to Figure 12 and Figure 13 to Figure 14 we can seethat the convolutional layers in the upsample path do not keep a lot of detailfor the healthy part of the brain as this region seems blurred However withinthe tumor different regions can still be distinguished The different parts ofthe tumor from the scans can also be seen such as the contrast-enhancing partand the high signal intensity on the T2w-FLAIR For the grade IV glioma inFigure 14 some filters such as filter 26 also seem to focus on the necrotic partof the tumor
28
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 11 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
29
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 12 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
30
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 13 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-06-0238 This is an IDH wildtype grade IV glioma
31
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 14 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-06-0238 This is an IDH wildtype grade IV glioma
32
E Training losses
During the training of the network we used masked categorical cross-entropy lossfor the IDH 1p19q and grade outputs The normal categorical cross-entropyloss is defined as
LCEbatch = minus 1
Nbatch
sumj
sumiisinC
yij log (yij) (1)
where LCEbatch is the total cross-entropy loss over a batch yij is the ground truth
label of sample j for class i yij is the prediction score for sample j for class iC is the set of classes and Nbatch is the number of samples in the batch Here itis assumed that the ground truth labels are one-hot-encoded thus yij is either0 or 1 for each class In our case the ground truth is not known for all sampleswhich can be incorporated in Equation (1) by setting yij to 0 for all classesfor a sample for which the ground truth is not known That sample wouldthen not contribute to the overall loss and would not contribute to the gradientupdate However this can skew the total loss over a batch since the loss is stillaveraged over the total number of samples in a batch regardless of whether theground truth is known resulting in a lower loss for batches that contained moresamples with unknown ground truth Therefore we used a masked categoricalcross-entropy loss
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
yij log (yij) (2)
where
microbatchj =
Nbatchsumij yij
sumi
yij (3)
is the batch weight for sample j In this way the total batch loss is only averagedover the samples that actually have a ground truth
Since there was an imbalance between the number of ground truth samplesfor each class we used class weights to compensate for this imbalance Thusthe loss becomes
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
microclassi yij log (yij) (4)
where
microclassi =
N
Ni |C|(5)
is the class weight for class i N is the total number of samples with knownground truth Ni is the number of samples of class i and |C| is the number ofclasses By determining the class weight in this way we ensured that
microclassi Ni =
N
|C|= constant (6)
33
Thus each class would have the same contribution to the overall loss Theseclass weights were (individually) determined for the IDH output the 1p19qoutput and the grade output
For the segmentation output we used the DICE loss
LDICEbatch =
sumj
1minus 2 middotsumvoxels
k yjk middot yjksumvoxelsk yjk + yjk
(7)
where yjk is the ground truth label in voxel k of sample j and yjk is theprediction score outputted for voxel k of sample j
The total loss that was optimized for the model was a weighted sum of thefour individual losses
Ltotal =summ
micromLm (8)
with
microm =1
Xm (9)
where Lm is the loss for output m microm is the loss weight for loss m (either theIDH 1p19q grade or segmentation loss) and Xm is the number of sampleswith known ground truth for output m In this way we could counteract theeffect of certain outputs having more known labels than other outputs
34
F Parameter tuning
Table 8 Hyperparameters that were tuned and the values that were testedValues in bold show the selected values used in the final model
Tuning parameter Tested values
Dropout rate 015 02 025 030 035 040l2-norm 00001 000001 0000001Learning rate 001 0001 00001 000001 00000001Weight decay 0001 00001 000001Augmentation factor 1 2 3Augmentation probability 025 030 035 040 045
35
G Evaluation metrics
We calculated the AUC accuracy sensitivity and specificity metrics for thegenetic and histological features for the definitions of these metrics see [67]
For the IDH and 1p19q co-deletion outputs the IDH mutated and the1p19q co-deleted samples were regarded as the positive class respectively Sincethe grade was a multi-class problem no single positive class could be determinedFor the prediction of the individual grades that grade was seen as the positiveclass and all other grades as the negative class (eg in the case of the gradeIII prediction grade III was regarded as the positive class and grade II and IVwere regarded as the negative class) For the LGG vs HGG prediction LGGwas considered as the positive class and HGG as the negative class For theevaluation of these metrics for the genetic and histological features only thesubjects with known ground truth were taken into account
The overall AUC for the grade was a multi-class AUC determined in a one-vs-one approach comparing each class against the others in this way thismetric was insensitive to class imbalance [68] A multi-class accuracy was usedto determine the overall accuracy for the grade predictions [67]
To evaluate the performance of the automated segmentation we evaluatedthe DICE score the Hausdorff distance and the volumetric similarity coefficientThe DICE score is a measure of overlap between two segmentations where avalue of 1 indicates perfect overlap and the Hausdorff distance is a measureof the closeness of the borders of the segmentations The volumetric similaritycoefficient is a measure of the agreement between the volumes of two segmen-tations without taking account the actual location of the tumor where a valueof 1 indicates perfect agreement See [65] for the definitions of these metrics
36
H Ground truth labels of patients included frompublic datasets
Acknowledgments
Sebastian van der Voort and Fatih Incekara acknowledge funding by the DutchCancer Society (KWF project number EMCR 2015-7859)
Data used in this publication were generated by the National Cancer Insti-tute Clinical Proteomic Tumor Analysis Consortium (CPTAC)
The results published here are in whole or part based upon data generatedby the TCGA Research Network httpcancergenomenihgov
Author contributions
SRvdV FI WJN MS and SK contrived the study and designed theexperiments FI MMJW JWS RNT GJL PCDWH RSEAJPEV MJvdB and MS included patients in the different studiesSRvdV FI MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV MJvdB and MS collected the dataSRvdV carried out the experiments SRvdV FI MS and SK inter-preted the results SRvdV FI MJvdB MS and SK created the initialdraft of the paper MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV WJN and MJvdB revised the paper
References
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[2] Hendrikus J Dubbink Peggy N Atmodimedjo Johan M Kros Pim JFrench Marc Sanson Ahmed Idbaih Pieter Wesseling Roelien EntingWim Spliet Cees Tijssen Winand N M Dinjens Thierry Gorlia andMartin J van den Bent Molecular classification of anaplastic oligoden-droglioma using next-generation sequencing a report of the prospectiverandomized EORTC brain tumor group 26951 phase III trial Neuro-Oncology 18(3)388ndash400 March 2016 ISSN 1522-8517 URL https
doiorg101093neuoncnov182
[3] Jeanette E Eckel-Passow Daniel H Lachance Annette M Molinaro Kyle MWalsh Paul A Decker Hugues Sicotte Melike Pekmezci Terri Rice Matt LKosel Ivan V Smirnov Gobinda Sarkar Alissa A Caron Thomas M
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Kollmeyer Corinne E Praska Anisha R Chada Chandralekha Halder He-len M Hansen Lucie S McCoy Paige M Bracci Roxanne Marshall ShichunZheng Gerald F Reis Alexander R Pico Brian P OrsquoNeill Jan C BucknerCaterina Giannini Jason T Huse Arie Perry Tarik Tihan Mitchell SBerger Susan M Chang Michael D Prados Joseph Wiemels John KWiencke Margaret R Wrensch and Robert B Jenkins Glioma groupsbased on 1p19q IDH and TERT promoter mutations in tumors NewEngland Journal of Medicine 372(26)2499ndash2508 June 2015 ISSN 0028-4793 URL httpsdoiorg101056NEJMoa1407279
[4] David N Louis Arie Perry Guido Reifenberger Andreas von Deimling Do-minique Figarella-Branger Webster K Cavenee Hiroko Ohgaki Otmar DWiestler Paul Kleihues and David W Ellison The 2016 world health orga-nization classification of tumors of the central nervous system a summaryActa Neuropathologica 131(6)803ndash820 June 2016 ISSN 0001-6322 URLhttpsdoiorg101007s00401-016-1545-1
[5] Ching-Chang Chen Peng-Wei Hsu Tai-Wei Erich Wu Shih-Tseng LeeChen-Nen Chang Kuo-chen Wei Chih-Cheng Chuang Chieh-Tsai WuTai-Ngar Lui Yung-Hsin Hsu Tzu-Kang Lin Sai-Cheung Lee and Yin-Cheng Huang Stereotactic brain biopsy Single center retrospective anal-ysis of complications Clinical Neurology and Neurosurgery 111(10)835ndash839 December 2009 ISSN 0303-8467 URL httpsdoiorg101016
jclineuro200908013
[6] Robert J Jackson Gregory N Fuller Dima Abi-Said Frederick F LangZiya L Gokaslan Wei Ming Shi David M Wildrick and Raymond SawayaLimitations of stereotactic biopsy in the initial management of gliomasNeuro-Oncology 3(3)193ndash200 July 2001 ISSN 1522-8517 URL https
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[7] M Zhou J Scott B Chaudhury L Hall D Goldgof KW Yeom M IvY Ou J Kalpathy-Cramer S Napel R Gillies O Gevaert and R GatenbyRadiomics in brain tumor Image assessment quantitative feature de-scriptors and machine-learning approaches American Journal of Neu-roradiology 39(2)208ndash216 February 2018 ISSN 0195-6108 URL https
doiorg103174ajnrA5391
[8] Wenya Linda Bi Ahmed Hosny Matthew B Schabath Maryellen LGiger Nicolai J Birkbak Alireza Mehrtash Tavis Allison Omar ArnaoutChristopher Abbosh Ian F Dunn Raymond H Mak Rulla M TamimiClare M Tempany Charles Swanton Udo Hoffmann Lawrence H SchwartzRobert J Gillies Raymond Y Huang and Hugo J W L Aerts Artificialintelligence in cancer imaging Clinical challenges and applications CAA Cancer Journal for Clinicians 69(2)caac21552 February 2019 ISSN0007-9235 URL httpsdoiorg103322caac21552
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[9] Ahmad Chaddad Michael Jonathan Kucharczyk Paul Daniel SihamSabri Bertrand J Jean-Claude Tamim Niazi and Bassam AbdulkarimRadiomics in glioblastoma Current status and challenges facing clinicalimplementation Frontiers in Oncology 9374ndash374 May 2019 ISSN 2234-943X URL httpsdoiorg103389fonc201900374
[10] Marion Smits Imaging of oligodendroglioma The British Journal of Ra-diology 89(1060)20150857 April 2016 ISSN 0007-1285 URL https
doiorg101259bjr20150857
[11] Rachel L Delfanti David E Piccioni Jason Handwerker Naeim BahramiAnithaPriya Krishnan Roshan Karunamuni Jona A Hattangadi-GluthTyler M Seibert Ashwin Srikant Karra A Jones Vivian S Snyder An-ders M Dale Nathan S White Carrie R McDonald and Nikdokht FaridImaging correlates for the 2016 update on WHO classification of gradeIIIII gliomas implications for IDH 1p19q and ATRX status Journal ofNeuro-Oncology 135(3)601ndash609 December 2017 ISSN 0167-594X URLhttpsdoiorg101007s11060-017-2613-7
[12] Sonal Gore Tanay Chougule Jayant Jagtap Jitender Saini and MadhuraIngalhalikar A review of radiomics and deep predictive modeling in gliomacharacterization Academic Radiology July 2020 ISSN 1076-6332 URLhttpsdoiorg101016jacra202006016
[13] Hugo J W L Aerts Emmanuel Rios Velazquez Ralph T H LeijenaarChintan Parmar Patrick Grossmann Sara Carvalho Johan BussinkRene Monshouwer Benjamin Haibe-Kains Derek Rietveld Frank HoebersMichelle M Rietbergen C Rene Leemans Andre Dekker John Quacken-bush Robert J Gillies and Philippe Lambin Decoding tumour pheno-type by noninvasive imaging using a quantitative radiomics approach Na-ture Communications 5(1)4006 September 2014 ISSN 2041-1723 URLhttpsdoiorg101038ncomms5006
[14] Sarah Chihati and Djamel Gaceb A review of recent progress in deeplearning-based methods for MRI brain tumor segmentation In 202011th International Conference on Information and Communication Sys-tems (ICICS) pages 149ndash154 Institute of Electrical and Electronics Engi-neers (IEEE) April 2020 ISBN 9781728162270 URL httpsdoiorg
101109icics494692020239550
[15] Robert J Gillies Paul E Kinahan and Hedvig Hricak Radiomics Imagesare more than pictures they are data Radiology 278(2)563ndash577 Febru-ary 2016 ISSN 0033-8419 URL httpsdoiorg101148radiol
2015151169
[16] James H Thrall Xiang Li Quanzheng Li Cinthia Cruz Synho Do KeithDreyer and James Brink Artificial intelligence and machine learning in ra-diology Opportunities challenges pitfalls and criteria for success Journal
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of the American College of Radiology 15(3 Part B)504ndash508 March 2018ISSN 1546-1440 URL httpsdoiorg101016jjacr201712026
[17] Ahmed Hosny Chintan Parmar John Quackenbush Lawrence H Schwartzand Hugo J W L Aerts Artificial intelligence in radiology Nature ReviewsCancer 18(8)500ndash510 August 2018 ISSN 1474-175X URL https
doiorg101038s41568-018-0016-5
[18] Okan Kopuklu Neslihan Kose Ahmet Gunduz and Gerhard Rigoll Re-source efficient 3D convolutional neural networks In 2019 IEEECVFInternational Conference on Computer Vision Workshop (ICCVW) Insti-tute of Electrical and Electronics Engineers (IEEE) October 2019 ISBN9781728150239 URL httpsdoiorg101109iccvw201900240
[19] S C Thust S Heiland A Falini H R Jager A D Waldman P C SundgrenC Godi V K Katsaros A Ramos N Bargallo M W Vernooij T YousryM Bendszus and M Smits Glioma imaging in europe A survey of 220centres and recommendations for best clinical practice European Radiology28(8)3306ndash3317 August 2018 ISSN 0938-7994 URL httpsdoiorg
101007s00330-018-5314-5
[20] Christian F Freyschlag Sandro M Krieg Johannes Kerschbaumer DanielPinggera Marie-Therese Forster Dominik Cordier Marco Rossi GabrieleMiceli Alexandre Roux Andres Reyes Silvio Sarubbo Anja Smits JoannaSierpowska Pierre A Robe Geert-Jan Rutten Thomas Santarius TomaszMatys Marc Zanello Fabien Almairac Lydiane Mondot Asgeir S JakolaMaria Zetterling Adria Rofes Gord von Campe Remy Guillevin DanieleBagatto Vincent Lubrano Marion Rapp John Goodden Philip C De WittHamer Johan Pallud Lorenzo Bello Claudius Thome Hugues Duffauand Emmanuel Mandonnet Imaging practice in low-grade gliomas amongeuropean specialized centers and proposal for a minimum core of imagingJournal of Neuro-Oncology 139(3)699ndash711 September 2018 ISSN 0167-594X URL httpsdoiorg101007s11060-018-2916-3
[21] M Labussiere A Idbaih X-W Wang Y Marie B Boisselier C FaletS Paris J Laffaire C Carpentier E Criniere F Ducray S El HallaniK Mokhtari K Hoang-Xuan J-Y Delattre and M Sanson All the 1p19qcodeleted gliomas are mutated on IDH1 or IDH2 Neurology 74(23)1886ndash1890 June 2010 ISSN 0028-3878 URL httpsdoiorg101212WNL
0b013e3181e1cf3a
[22] David N Louis Pieter Wesseling Kenneth Aldape Daniel J Brat DavidCapper Ian A Cree Charles Eberhart Dominique Figarella-BrangerMaryam Fouladi Gregory N Fuller Caterina Giannini Christine Haber-ler Cynthia Hawkins Takashi Komori Johan M Kros HK Ng Brent AOrr Sung-Hye Park Werner Paulus Arie Perry Torsten Pietsch GuidoReifenberger Marc Rosenblum Brian Rous Felix Sahm Chitra SarkarDavid A Solomon Uri Tabori Martin J Bent Andreas Deimling Michael
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Weller Valerie A White and David W Ellison cIMPACT-NOW up-date 6 new entity and diagnostic principle recommendations of thecIMPACT-Utrecht meeting on future CNS tumor classification and grad-ing Brain Pathology 30(4)844ndash856 July 2020 ISSN 1015-6305 URLhttpsdoiorg101111bpa12832
[23] Zhenyu Tang Yuyun Xu Zhicheng Jiao Junfeng Lu Lei Jin Abudumi-jiti Aibaidula Jinsong Wu Qian Wang Han Zhang and Dinggang ShenPre-operative overall survival time prediction for glioblastoma patients us-ing deep learning on both imaging phenotype and genotype In Ding-gang Shen Tianming Liu Terry M Peters Lawrence H Staib CarolineEssert Sean Zhou Pew-Thian Yap and Ali Khan editors Lecture Notesin Computer Science pages 415ndash422 Springer Science and Business Me-dia LLC 2019 ISBN 9783030322380 URL httpsdoiorg101007
978-3-030-32239-7_46
[24] Zhiyuan Xue Bowen Xin Dingqian Wang and Xiuying Wang Radiomics-enhanced multi-task neural network for non-invasive glioma subtypingand segmentation In Hassan Mohy-ud Din and Saima Rathore editorsRadiomics and Radiogenomics in Neuro-oncology pages 81ndash90 SpringerScience and Business Media LLC 2020 ISBN 9783030401238 URLhttpsdoiorg101007978-3-030-40124-5_9
[25] Milan Decuyper Stijn Bonte Karel Deblaere and Roel Van Holen Au-tomated MRI based pipeline for glioma segmentation and prediction ofgrade IDH mutation and 1p19q co-deletion 2020 Preprint at https
arxivorgabs200511965
[26] Stefania Rizzo Francesca Botta Sara Raimondi Daniela Origgi Cris-tiana Fanciullo Alessio Giuseppe Morganti and Massimo Bellomi Ra-diomics the facts and the challenges of image analysis European Ra-diology Experimental 2(1)36 December 2018 ISSN 2509-9280 URLhttpsdoiorg101186s41747-018-0068-z
[27] Philipp Lohmann Norbert Galldiks Martin Kocher Alexander HeinzelChristian P Filss Carina Stegmayr Felix M Mottaghy Gereon R FinkN Jon Shah and Karl-Josef Langen Radiomics in neuro-oncology Basicsworkflow and applications Methods June 2020 ISSN 1046-2023 URLhttpsdoiorg101016jymeth202006003
[28] Stephen S F Yip and Hugo J W L Aerts Applications and limitationsof radiomics Physics in Medicine and Biology 61(13)R150ndashR166 July2016 ISSN 0031-9155 URL httpsdoiorg1010880031-915561
13r150
[29] Annika Malmstrom Ma lgorzata Lysiak Bjarne Winther Kristensen Eliz-abeth Hovey Roger Henriksson and Peter Soderkvist Do we really knowwho has an MGMT methylated glioma Results of an international survey
41
regarding use of MGMT analyses for glioma Neuro-Oncology Practice 7(1)68ndash76 09 2019 ISSN 2054-2577 URL httpsdoiorg101093
nopnpz039
[30] Takahiro Sasaki Manabu Kinoshita Koji Fujita Junya Fukai NobuhideHayashi Yuji Uematsu Yoshiko Okita Masahiro Nonaka Shusuke Mo-riuchi Takehiro Uda Naohiro Tsuyuguchi Hideyuki Arita Kanji MoriKenichi Ishibashi Koji Takano Namiko Nishida Tomoko Shofuda EmaYoshioka Daisuke Kanematsu Yoshinori Kodama Masayuki ManoNaoyuki Nakao and Yonehiro Kanemura Radiomics and MGMT pro-moter methylation for prognostication of newly diagnosed glioblastomaScientific Reports 9(1)14435 December 2019 ISSN 2045-2322 URLhttpsdoiorg101038s41598-019-50849-y
[31] A Gupta A Prager RJ Young W Shi AMP Omuro and JJ GraberDiffusion-weighted MR imaging and MGMT methylation status in glioblas-toma A reappraisal of the role of preoperative quantitative ADC measure-ments American Journal of Neuroradiology 34(1)E10ndashE11 January 2013ISSN 0195-6108 URL httpsdoiorg103174ajnrA3467
[32] JA Carrillo A Lai PL Nghiemphu HJ Kim HS Phillips S KharbandaP Moftakhar S Lalaezari W Yong BM Ellingson TF Cloughesy andWB Pope Relationship between tumor enhancement edema IDH1 muta-tional status MGMT promoter methylation and survival in glioblastomaAmerican Journal of Neuroradiology 33(7)1349ndash1355 August 2012 ISSN0195-6108 URL httpsdoiorg103174ajnrA2950
[33] Vilde Elisabeth Mikkelsen Hong Yan Dai Anne Line Stensjoslashen Erik Mag-nus Berntsen Oslashyvind Salvesen Ole Solheim and Sverre Helge TorpMGMT promoter methylation status is not related to histological or radio-logical features in IDH wild-type glioblastomas Journal of Neuropathologyamp Experimental Neurology 79(8)855ndash862 August 2020 ISSN 0022-3069URL httpsdoiorg101093jnennlaa060
[34] Martin J van den Bent Interobserver variation of the histopathologicaldiagnosis in clinical trials on glioma a clinicianrsquos perspective Acta Neu-ropathologica 120(3)297ndash304 September 2010 ISSN 1432-0533 URLhttpsdoiorg101007s00401-010-0725-7
[35] Ji Eun Park Ho Sung Kim Youngheun Jo Roh-Eul Yoo Seung Hong ChoiSoo Jung Nam and Jeong Hoon Kim Radiomics prognostication modelin glioblastoma using diffusion- and perfusion-weighted MRI ScientificReports 10(1)4250 December 2020 ISSN 2045-2322 URL httpsdoi
org101038s41598-020-61178-w
[36] Minjae Kim So Yeong Jung Ji Eun Park Yeongheun Jo Seo YoungPark Soo Jung Nam Jeong Hoon Kim and Ho Sung Kim Diffusion-and perfusion-weighted MRI radiomics model may predict isocitrate dehy-drogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade
42
glioma European Radiology 30(4)2142ndash2151 April 2020 ISSN 0938-7994URL httpsdoiorg101007s00330-019-06548-3
[37] M Visser DMJ Muller RJM van Duijn M Smits N Verburg EJ HendriksRJA Nabuurs JCJ Bot RS Eijgelaar M Witte MB van Herk F BarkhofPC de WittHamer and JC de Munck Inter-rater agreement in glioma seg-mentations on longitudinal MRI NeuroImage Clinical 22101727 2019ISSN 2213-1582 URL httpsdoiorg101016jnicl2019101727
[38] Kenneth Clark Bruce Vendt Kirk Smith John Freymann Justin KirbyPaul Koppel Stephen Moore Stanley Phillips David Maffitt MichaelPringle Lawrence Tarbox and Fred Prior The cancer imaging archive(TCIA) Maintaining and operating a public information repository Jour-nal of Digital Imaging 26(6)1045ndash1057 December 2013 ISSN 0897-1889URL httpsdoiorg101007s10278-013-9622-7
[39] Lisa Scarpace Adam E Flanders Rajan Jain Tom Mikkelsen and David WAndrews Data from REMBRANDT The Cancer Imaging Archive 2015URL httpsdoiorg107937K9TCIA2015588OZUZB
[40] National Cancer Institute Clinical Proteomic Tumor Analysis Consortium(CPTAC) Radiology data from the clinical proteomic tumor analysisconsortium glioblastoma multiforme CPTAC-GBM collection The CancerImaging Archive 2018 URL httpsdoiorg107937k9tcia2018
3rje41q1
[41] Nameeta Shah Xu Feng Michael Lankerovich Ralph B Puchalski andBart Keogh Data from Ivy GAP The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016XLwaN6nL
[42] Ralph B Puchalski Nameeta Shah Jeremy Miller Rachel Dalley Steve RNomura Jae-Guen Yoon Kimberly A Smith Michael Lankerovich DarrenBertagnolli Kris Bickley Andrew F Boe Krissy Brouner Stephanie But-ler Shiella Caldejon Mike Chapin Suvro Datta Nick Dee Tsega DestaTim Dolbeare Nadezhda Dotson Amanda Ebbert David Feng Xu FengMichael Fisher Garrett Gee Jeff Goldy Lindsey Gourley Benjamin WGregor Guangyu Gu Nika Hejazinia John Hohmann Parvinder HothiRobert Howard Kevin Joines Ali Kriedberg Leonard Kuan Chris LauFelix Lee Hwahyung Lee Tracy Lemon Fuhui Long Naveed Mastan ErikaMott Chantal Murthy Kiet Ngo Eric Olson Melissa Reding Zack RileyDavid Rosen David Sandman Nadiya Shapovalova Clifford R Slaughter-beck Andrew Sodt Graham Stockdale Aaron Szafer Wayne WakemanPaul E Wohnoutka Steven J White Don Marsh Robert C RostomilyLydia Ng Chinh Dang Allan Jones Bart Keogh Haley R GittlemanJill S Barnholtz-Sloan Patrick J Cimino Megha S Uppin C Dirk KeeneFarrokh R Farrokhi Justin D Lathia Michael E Berens Antonio IavaroneAmy Bernard Ed Lein John W Phillips Steven W Rostad Charles Cobbs
43
Michael J Hawrylycz and Greg D Foltz An anatomic transcriptional at-las of human glioblastoma Science 360(6389)660ndash663 May 2018 ISSN0036-8075 URL httpsdoiorg101126scienceaaf2666
[43] Kathleen Schmainda and Melissa Prah Data from Brain-Tumor-Progression The Cancer Imaging Archive 2018 URL httpsdoiorg
107937K9TCIA201815quzvnb
[44] B H Menze A Jakab S Bauer J Kalpathy-Cramer K FarahaniJ Kirby Y Burren N Porz J Slotboom R Wiest L Lanczi E GerstnerM Weber T Arbel B B Avants N Ayache P Buendia D L CollinsN Cordier J J Corso A Criminisi T Das H Delingette C Demi-ralp C R Durst M Dojat S Doyle J Festa F Forbes E GeremiaB Glocker P Golland X Guo A Hamamci K M IftekharuddinR Jena N M John E Konukoglu D Lashkari J A Mariz R MeierS Pereira D Precup S J Price T R Raviv S M S Reza M RyanD Sarikaya L Schwartz H Shin J Shotton C A Silva N SousaN K Subbanna G Szekely T J Taylor O M Thomas N J Tusti-son G Unal F Vasseur M Wintermark D H Ye L Zhao B ZhaoD Zikic M Prastawa M Reyes and K Van Leemput The mul-timodal brain tumor image segmentation benchmark (BRATS) IEEETransactions on Medical Imaging 34(10)1993ndash2024 2015 URL https
doiorg101109TMI20142377694
[45] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin S Kirby John B Freymann Keyvan Farahani and ChristosDavatzikos Advancing the cancer genome atlas glioma MRI collectionswith expert segmentation labels and radiomic features Scientific Data 4(1)170117 December 2017 ISSN 2052-4463 URL httpsdoiorg10
1038sdata2017117
[46] Spyridon Bakas Mauricio Reyes Andras Jakab Stefan Bauer MarkusRempfler Alessandro Crimi Russell Takeshi Shinohara Christoph BergerSung Min Ha Martin Rozycki Marcel Prastawa Esther Alberts JanaLipkova John Freymann Justin Kirby Michel Bilello Hassan Fathallah-Shaykh Roland Wiest Jan Kirschke Benedikt Wiestler Rivka ColenAikaterini Kotrotsou Pamela Lamontagne Daniel Marcus MikhailMilchenko Arash Nazeri Marc-Andre Weber Abhishek Mahajan UjjwalBaid Elizabeth Gerstner Dongjin Kwon Gagan Acharya Manu Agar-wal Mahbubul Alam Alberto Albiol Antonio Albiol Francisco J AlbiolVarghese Alex Nigel Allinson Pedro H A Amorim Abhijit AmrutkarGanesh Anand Simon Andermatt Tal Arbel Pablo Arbelaez Aaron Av-ery Muneeza Azmat Pranjal B W Bai Subhashis Banerjee Bill BarthThomas Batchelder Kayhan Batmanghelich Enzo Battistella AndrewBeers Mikhail Belyaev Martin Bendszus Eze Benson Jose Bernal Ha-landur Nagaraja Bharath George Biros Sotirios Bisdas James BrownMariano Cabezas Shilei Cao Jorge M Cardoso Eric N Carver Adria
44
Casamitjana Laura Silvana Castillo Marcel Cata Philippe Cattin Al-bert Cerigues Vinicius S Chagas Siddhartha Chandra Yi-Ju ChangShiyu Chang Ken Chang Joseph Chazalon Shengcong Chen Wei ChenJefferson W Chen Zhaolin Chen Kun Cheng Ahana Roy ChoudhuryRoger Chylla Albert Clerigues Steven Colleman Ramiro German Ro-driguez Colmeiro Marc Combalia Anthony Costa Xiaomeng Cui Zhen-zhen Dai Lutao Dai Laura Alexandra Daza Eric Deutsch ChangxingDing Chao Dong Shidu Dong Wojciech Dudzik Zach Eaton-Rosen GaryEgan Guilherme Escudero Theo Estienne Richard Everson JonathanFabrizio Yong Fan Longwei Fang Xue Feng Enzo Ferrante Lucas Fi-don Martin Fischer Andrew P French Naomi Fridman Huan Fu DavidFuentes Yaozong Gao Evan Gates David Gering Amir Gholami WilliGierke Ben Glocker Mingming Gong Sandra Gonzalez-Villa T Gros-ges Yuanfang Guan Sheng Guo Sudeep Gupta Woo-Sup Han Il SongHan Konstantin Harmuth Huiguang He Aura Hernandez-Sabate EvelynHerrmann Naveen Himthani Winston Hsu Cheyu Hsu Xiaojun Hu Xi-aobin Hu Yan Hu Yifan Hu Rui Hua Teng-Yi Huang Weilin HuangSabine Van Huffel Quan Huo Vivek HV Khan M Iftekharuddin FabianIsensee Mobarakol Islam Aaron S Jackson Sachin R Jambawalikar An-drew Jesson Weijian Jian Peter Jin V Jeya Maria Jose Alain JungoB Kainz Konstantinos Kamnitsas Po-Yu Kao Ayush Karnawat ThomasKellermeier Adel Kermi Kurt Keutzer Mohamed Tarek Khadir Mahen-dra Khened Philipp Kickingereder Geena Kim Nik King Haley KnappUrspeter Knecht Lisa Kohli Deren Kong Xiangmao Kong Simon Kop-pers Avinash Kori Ganapathy Krishnamurthi Egor Krivov Piyush Ku-mar Kaisar Kushibar Dmitrii Lachinov Tryphon Lambrou Joon LeeChengen Lee Yuehchou Lee M Lee Szidonia Lefkovits Laszlo LefkovitsJames Levitt Tengfei Li Hongwei Li Wenqi Li Hongyang Li XiaochuanLi Yuexiang Li Heng Li Zhenye Li Xiaoyu Li Zeju Li XiaoGang LiWenqi Li Zheng-Shen Lin Fengming Lin Pietro Lio Chang Liu Bo-qiang Liu Xiang Liu Mingyuan Liu Ju Liu Luyan Liu Xavier LladoMarc Moreno Lopez Pablo Ribalta Lorenzo Zhentai Lu Lin Luo ZhigangLuo Jun Ma Kai Ma Thomas Mackie Anant Madabushi Issam Mah-moudi Klaus H Maier-Hein Pradipta Maji CP Mammen Andreas MangB S Manjunath Michal Marcinkiewicz S McDonagh Stephen McKennaRichard McKinley Miriam Mehl Sachin Mehta Raghav Mehta RaphaelMeier Christoph Meinel Dorit Merhof Craig Meyer Robert Miller Sush-mita Mitra Aliasgar Moiyadi David Molina-Garcia Miguel A B Mon-teiro Grzegorz Mrukwa Andriy Myronenko Jakub Nalepa Thuyen NgoDong Nie Holly Ning Chen Niu Nicholas K Nuechterlein Eric Oer-mann Arlindo Oliveira Diego D C Oliveira Arnau Oliver AlexanderF I Osman Yu-Nian Ou Sebastien Ourselin Nikos Paragios Moo SungPark Brad Paschke J Gregory Pauloski Kamlesh Pawar Nick PawlowskiLinmin Pei Suting Peng Silvio M Pereira Julian Perez-Beteta Vic-tor M Perez-Garcia Simon Pezold Bao Pham Ashish Phophalia GemmaPiella G N Pillai Marie Piraud Maxim Pisov Anmol Popli Michael P
45
Pound Reza Pourreza Prateek Prasanna Vesna Prkovska Tony P Prid-more Santi Puch Elodie Puybareau Buyue Qian Xu Qiao MartinRajchl Swapnil Rane Michael Rebsamen Hongliang Ren Xuhua RenKarthik Revanuru Mina Rezaei Oliver Rippel Luis Carlos Rivera Char-lotte Robert Bruce Rosen Daniel Rueckert Mohammed Safwan MostafaSalem Joaquim Salvi Irina Sanchez Irina Sanchez Heitor M Santos Em-mett Sartor Dawid Schellingerhout Klaudius Scheufele Matthew R ScottArtur A Scussel Sara Sedlar Juan Pablo Serrano-Rubio N Jon ShahNameetha Shah Mazhar Shaikh B Uma Shankar Zeina Shboul HaipengShen Dinggang Shen Linlin Shen Haocheng Shen Varun Shenoy FengShi Hyung Eun Shin Hai Shu Diana Sima M Sinclair Orjan SmedbyJames M Snyder Mohammadreza Soltaninejad Guidong Song MehulSoni Jean Stawiaski Shashank Subramanian Li Sun Roger Sun JiaweiSun Kay Sun Yu Sun Guoxia Sun Shuang Sun Yannick R Suter Las-zlo Szilagyi Sanjay Talbar Dacheng Tao Dacheng Tao Zhongzhao TengSiddhesh Thakur Meenakshi H Thakur Sameer Tharakan Pallavi Ti-wari Guillaume Tochon Tuan Tran Yuhsiang M Tsai Kuan-Lun TsengTran Anh Tuan Vadim Turlapov Nicholas Tustison Maria VakalopoulouSergi Valverde Rami Vanguri Evgeny Vasiliev Jonathan Ventura LuisVera Tom Vercauteren C A Verrastro Lasitha Vidyaratne Veronica Vi-laplana Ajeet Vivekanandan Guotai Wang Qian Wang Chiatse J WangWeichung Wang Duo Wang Ruixuan Wang Yuanyuan Wang ChunliangWang Guotai Wang Ning Wen Xin Wen Leon Weninger Wolfgang WickShaocheng Wu Qiang Wu Yihong Wu Yong Xia Yanwu Xu XiaowenXu Peiyuan Xu Tsai-Ling Yang Xiaoping Yang Hao-Yu Yang JunlinYang Haojin Yang Guang Yang Hongdou Yao Xujiong Ye ChangchangYin Brett Young-Moxon Jinhua Yu Xiangyu Yue Songtao Zhang AngelaZhang Kun Zhang Xuejie Zhang Lichi Zhang Xiaoyue Zhang YazhuoZhang Lei Zhang Jianguo Zhang Xiang Zhang Tianhao Zhang SichengZhao Yu Zhao Xiaomei Zhao Liang Zhao Yefeng Zheng Liming ZhongChenhong Zhou Xiaobing Zhou Fan Zhou Hongtu Zhu Jin Zhu YingZhuge Weiwei Zong Jayashree Kalpathy-Cramer Keyvan Farahani Chris-tos Davatzikos Koen van Leemput and Bjoern Menze Identifying the bestmachine learning algorithms for brain tumor segmentation progression as-sessment and overall survival prediction in the BRATS challenge 2018Preprint at httpsarxivorgabs181102629
[47] Nancy Pedano Adam E Flanders Lisa Scarpace Tom Mikkelsen Jen-nifer M Eschbacher Beth Hermes Victor Sisneros Jill Barnholtz-Sloanand Quinn Ostrom Radiology data from the cancer genome atlas lowgrade glioma [TCGA-LGG] collection The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016L4LTD3TK
[48] Lisa Scarpace Tom Mikkelsen Soonmee Cha Sujaya Rao SangeetaTekchandani David Gutman Joel H Saltz Bradley J Erickson NancyPedano Adam E Flanders Jill Barnholtz-Sloan Quinn Ostrom DanielBarboriak and Laura J Pierce Radiology data from the cancer genome
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atlas glioblastoma multiforme [TCGA-GBM] collection The Cancer Imag-ing Archive 2016 URL httpsdoiorg107937K9TCIA2016
RNYFUYE9
[49] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin Kirby John Freymann Keyvan Farahani and ChristosDavatzikos Segmentation labels and radiomic features for the pre-operativescans of the TCGA-LGG collection [data set] The Cancer Imaging Archive2017 URL httpsdoiorg107937K9TCIA2017GJQ7R0EF
[50] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello Mar-tin Rozycki Justin Kirby John Freymann Keyvan Farahani and Chris-tos Davatzikos Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [data set] The CancerImaging Archive 2017 URL httpsdoiorg107937K9TCIA2017
KLXWJJ1Q
[51] Xiangrui Li Paul S Morgan John Ashburner Jolinda Smith and Christo-pher Rorden The first step for neuroimaging data analysis DICOM toNIfTI conversion Journal of Neuroscience Methods 26447ndash56 May 2016ISSN 0165-0270 URL httpsdoiorg101016jjneumeth201603
001
[52] Vladimir Fonov Alan C Evans Kelly Botteron C Robert Almli Robert CMcKinstry and D Louis Collins Unbiased average age-appropriate at-lases for pediatric studies NeuroImage 54(1)313ndash327 January 2011ISSN 1053-8119 URL httpsdoiorg101016jneuroimage2010
07033
[53] VS Fonov AC Evans RC McKinstry CR Almli and DL Collins Unbiasednonlinear average age-appropriate brain templates from birth to adulthoodNeuroImage 47S102 July 2009 ISSN 1053-8119 URL httpsdoi
org101016S1053-8119(09)70884-5 Organization for Human BrainMapping 2009 Annual Meeting
[54] S Klein M Staring K Murphy MA Viergever and J Pluim elastix Atoolbox for intensity-based medical image registration IEEE Transactionson Medical Imaging 29(1)196ndash205 January 2010 ISSN 0278-0062 URLhttpsdoiorg101109TMI20092035616
[55] Denis Shamonin Fast parallel image registration on CPU and GPU fordiagnostic classification of alzheimerrsquos disease Frontiers in Neuroinfor-matics 750 2013 ISSN 1662-5196 URL httpsdoiorg103389
fninf201300050
[56] Bradley C Lowekamp David T Chen Luis Ibanez and Daniel Blezek Thedesign of SimpleITK Frontiers in Neuroinformatics 745 2013 ISSN1662-5196 URL httpsdoiorg103389fninf201300045
47
[57] Fabian Isensee Marianne Schell Irada Pflueger Gianluca Brugnara DavidBonekamp Ulf Neuberger Antje Wick Heinz-Peter Schlemmer SabineHeiland Wolfgang Wick Martin Bendszus Klaus H Maier-Hein andPhilipp Kickingereder Automated brain extraction of multisequence MRIusing artificial neural networks Human Brain Mapping 40(17)4952ndash4964December 2019 ISSN 1065-9471 URL httpsdoiorg101002hbm
24750
[58] Olaf Ronneberger Invited talk U-net convolutional networks for biomed-ical image segmentation In geb Fritzsche K Maier-Hein geb Lehmann TDeserno Heinz Handels and Thomas Tolxdorff editors Bildverarbeitungfur die Medizin 2017 Informatik aktuell pages 3ndash3 Springer Scienceand Business Media LLC 2017 ISBN 9783662543443 URL https
doiorg101007978-3-662-54345-0_3
[59] Martın Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy DavisJeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving MichaelIsard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry MooreDerek G Murray Benoit Steiner Paul Tucker Vijay Vasudevan PeteWarden Martin Wicke Yuan Yu and Xiaoqiang Zheng Tensor-Flow A system for large-scale machine learning In 12th USENIXSymposium on Operating Systems Design and Implementation (OSDI16) pages 265ndash283 USENIX Association November 2016 ISBN978-1-931971-33-1 URL httpswwwusenixorgconferenceosdi16
technical-sessionspresentationabadi
[60] Dipankar Das Naveen Mellempudi Dheevatsa Mudigere Dhiraj KalamkarSasikanth Avancha Kunal Banerjee Srinivas Sridharan KarthikVaidyanathan Bharat Kaul Evangelos Georganas Alexander HeineckePradeep Dubey Jesus Corbal Nikita Shustrov Roma Dubtsov EvaristFomenko and Vadim Pirogov Mixed precision training of convolutionalneural networks using integer operations In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=H135uzZ0-
[61] Samuel L Smith Pieter-Jan Kindermans and Quoc V Le Donrsquot decaythe learning rate increase the batch size In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=B1Yy1BxCZ
[62] Cliff Woolley NCCL accelarated multi-GPU collective communicationsURL httpsimagesnvidiacomeventssc15pdfsNCCL-Woolley
pdf Accessed on 2020-09-30
[63] Ilya Loshchilov and Frank Hutter Decoupled weight decay regularization2017 Preprint at httpsarxivorgabs171105101
48
[64] F Pedregosa G Varoquaux A Gramfort V Michel B Thirion O GriselM Blondel P Prettenhofer R Weiss V Dubourg J Vanderplas A Pas-sos D Cournapeau M Brucher M Perrot and E Duchesnay Scikit-learn Machine learning in python Journal of Machine Learning Re-search 122825ndash2830 2011 URL httpswwwjmlrorgpapersv12
pedregosa11ahtml
[65] Abdel Aziz Taha and Allan Hanbury Metrics for evaluating 3d medicalimage segmentation analysis selection and tool BMC Medical Imaging15(1)29 August 2015 ISSN 1471-2342 URL httpsdoiorg101186
s12880-015-0068-x
[66] Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viegas and MartinWattenberg SmoothGrad removing noise by adding noise 2017 Preprintat httpsarxivorgabs170603825
[67] Alaa Tharwat Classification assessment methods Applied Computing andInformatics 2018 ISSN 2210-8327 URL httpsdoiorg101016j
aci201808003
[68] David J Hand and Robert J Till A simple generalisation of the areaunder the ROC curve for multiple class classification problems MachineLearning 45(2)171ndash186 November 2001 ISSN 1573-0565 URL https
doiorg101023A1010920819831
49
Data Collection | Patient | IDH_mutated | 1p19q_codeleted | Grade | |||||
BTumorP | PGBM-001 | -1 | -1 | -1 | |||||
BTumorP | PGBM-002 | -1 | -1 | -1 | |||||
BTumorP | PGBM-003 | -1 | -1 | -1 | |||||
BTumorP | PGBM-004 | -1 | -1 | -1 | |||||
BTumorP | PGBM-005 | -1 | -1 | -1 | |||||
BTumorP | PGBM-006 | -1 | -1 | -1 | |||||
BTumorP | PGBM-007 | -1 | -1 | -1 | |||||
BTumorP | PGBM-008 | -1 | -1 | -1 | |||||
BTumorP | PGBM-009 | -1 | -1 | -1 | |||||
BTumorP | PGBM-010 | -1 | -1 | -1 | |||||
BTumorP | PGBM-011 | -1 | -1 | -1 | |||||
BTumorP | PGBM-012 | -1 | -1 | -1 | |||||
BTumorP | PGBM-013 | -1 | -1 | -1 | |||||
BTumorP | PGBM-014 | -1 | -1 | -1 | |||||
BTumorP | PGBM-015 | -1 | -1 | -1 | |||||
BTumorP | PGBM-016 | -1 | -1 | -1 | |||||
BTumorP | PGBM-017 | -1 | -1 | -1 | |||||
BTumorP | PGBM-018 | -1 | -1 | -1 | |||||
BTumorP | PGBM-019 | -1 | -1 | -1 | |||||
BTumorP | PGBM-020 | -1 | -1 | -1 | |||||
BraTS | 2013_0 | -1 | -1 | -1 | |||||
BraTS | 2013_10 | -1 | -1 | -1 | |||||
BraTS | 2013_11 | -1 | -1 | -1 | |||||
BraTS | 2013_12 | -1 | -1 | -1 | |||||
BraTS | 2013_13 | -1 | -1 | -1 | |||||
BraTS | 2013_14 | -1 | -1 | -1 | |||||
BraTS | 2013_15 | -1 | -1 | -1 | |||||
BraTS | 2013_16 | -1 | -1 | -1 | |||||
BraTS | 2013_17 | -1 | -1 | -1 | |||||
BraTS | 2013_18 | -1 | -1 | -1 | |||||
BraTS | 2013_19 | -1 | -1 | -1 | |||||
BraTS | 2013_1 | -1 | -1 | -1 | |||||
BraTS | 2013_20 | -1 | -1 | -1 | |||||
BraTS | 2013_21 | -1 | -1 | -1 | |||||
BraTS | 2013_22 | -1 | -1 | -1 | |||||
BraTS | 2013_23 | -1 | -1 | -1 | |||||
BraTS | 2013_24 | -1 | -1 | -1 | |||||
BraTS | 2013_25 | -1 | -1 | -1 | |||||
BraTS | 2013_26 | -1 | -1 | -1 | |||||
BraTS | 2013_27 | -1 | -1 | -1 | |||||
BraTS | 2013_28 | -1 | -1 | -1 | |||||
BraTS | 2013_29 | -1 | -1 | -1 | |||||
BraTS | 2013_2 | -1 | -1 | -1 | |||||
BraTS | 2013_3 | -1 | -1 | -1 | |||||
BraTS | 2013_4 | -1 | -1 | -1 | |||||
BraTS | 2013_5 | -1 | -1 | -1 | |||||
BraTS | 2013_6 | -1 | -1 | -1 | |||||
BraTS | 2013_7 | -1 | -1 | -1 | |||||
BraTS | 2013_8 | -1 | -1 | -1 | |||||
BraTS | 2013_9 | -1 | -1 | -1 | |||||
BraTS | CBICA_AAB | -1 | -1 | -1 | |||||
BraTS | CBICA_AAG | -1 | -1 | -1 | |||||
BraTS | CBICA_AAL | -1 | -1 | -1 | |||||
BraTS | CBICA_AAP | -1 | -1 | -1 | |||||
BraTS | CBICA_ABB | -1 | -1 | -1 | |||||
BraTS | CBICA_ABE | -1 | -1 | -1 | |||||
BraTS | CBICA_ABM | -1 | -1 | -1 | |||||
BraTS | CBICA_ABN | -1 | -1 | -1 | |||||
BraTS | CBICA_ABO | -1 | -1 | -1 | |||||
BraTS | CBICA_ABY | -1 | -1 | -1 | |||||
BraTS | CBICA_ALN | -1 | -1 | -1 | |||||
BraTS | CBICA_ALU | -1 | -1 | -1 | |||||
BraTS | CBICA_ALX | -1 | -1 | -1 | |||||
BraTS | CBICA_AME | -1 | -1 | -1 | |||||
BraTS | CBICA_AMH | -1 | -1 | -1 | |||||
BraTS | CBICA_ANG | -1 | -1 | -1 | |||||
BraTS | CBICA_ANI | -1 | -1 | -1 | |||||
BraTS | CBICA_ANP | -1 | -1 | -1 | |||||
BraTS | CBICA_ANV | -1 | -1 | -1 | |||||
BraTS | CBICA_ANZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AOC | -1 | -1 | -1 | |||||
BraTS | CBICA_AOD | -1 | -1 | -1 | |||||
BraTS | CBICA_AOH | -1 | -1 | -1 | |||||
BraTS | CBICA_AOO | -1 | -1 | -1 | |||||
BraTS | CBICA_AOP | -1 | -1 | -1 | |||||
BraTS | CBICA_AOS | -1 | -1 | -1 | |||||
BraTS | CBICA_AOZ | -1 | -1 | -1 | |||||
BraTS | CBICA_APK | -1 | -1 | -1 | |||||
BraTS | CBICA_APR | -1 | -1 | -1 | |||||
BraTS | CBICA_APY | -1 | -1 | -1 | |||||
BraTS | CBICA_APZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQA | -1 | -1 | -1 | |||||
BraTS | CBICA_AQD | -1 | -1 | -1 | |||||
BraTS | CBICA_AQG | -1 | -1 | -1 | |||||
BraTS | CBICA_AQJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQN | -1 | -1 | -1 | |||||
BraTS | CBICA_AQO | -1 | -1 | -1 | |||||
BraTS | CBICA_AQP | -1 | -1 | -1 | |||||
BraTS | CBICA_AQQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQR | -1 | -1 | -1 | |||||
BraTS | CBICA_AQT | -1 | -1 | -1 | |||||
BraTS | CBICA_AQU | -1 | -1 | -1 | |||||
BraTS | CBICA_AQV | -1 | -1 | -1 | |||||
BraTS | CBICA_AQY | -1 | -1 | -1 | |||||
BraTS | CBICA_AQZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ARF | -1 | -1 | -1 | |||||
BraTS | CBICA_ARW | -1 | -1 | -1 | |||||
BraTS | CBICA_ARZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ASA | -1 | -1 | -1 | |||||
BraTS | CBICA_ASE | -1 | -1 | -1 | |||||
BraTS | CBICA_ASF | -1 | -1 | -1 | |||||
BraTS | CBICA_ASG | -1 | -1 | -1 | |||||
BraTS | CBICA_ASH | -1 | -1 | -1 | |||||
BraTS | CBICA_ASK | -1 | -1 | -1 | |||||
BraTS | CBICA_ASN | -1 | -1 | -1 | |||||
BraTS | CBICA_ASO | -1 | -1 | -1 | |||||
BraTS | CBICA_ASR | -1 | -1 | -1 | |||||
BraTS | CBICA_ASU | -1 | -1 | -1 | |||||
BraTS | CBICA_ASV | -1 | -1 | -1 | |||||
BraTS | CBICA_ASW | -1 | -1 | -1 | |||||
BraTS | CBICA_ASY | -1 | -1 | -1 | |||||
BraTS | CBICA_ATB | -1 | -1 | -1 | |||||
BraTS | CBICA_ATD | -1 | -1 | -1 | |||||
BraTS | CBICA_ATF | -1 | -1 | -1 | |||||
BraTS | CBICA_ATN | -1 | -1 | -1 | |||||
BraTS | CBICA_ATP | -1 | -1 | -1 | |||||
BraTS | CBICA_ATV | -1 | -1 | -1 | |||||
BraTS | CBICA_ATX | -1 | -1 | -1 | |||||
BraTS | CBICA_AUA | -1 | -1 | -1 | |||||
BraTS | CBICA_AUN | -1 | -1 | -1 | |||||
BraTS | CBICA_AUQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AUR | -1 | -1 | -1 | |||||
BraTS | CBICA_AUW | -1 | -1 | -1 | |||||
BraTS | CBICA_AUX | -1 | -1 | -1 | |||||
BraTS | CBICA_AVB | -1 | -1 | -1 | |||||
BraTS | CBICA_AVF | -1 | -1 | -1 | |||||
BraTS | CBICA_AVG | -1 | -1 | -1 | |||||
BraTS | CBICA_AVJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AVT | -1 | -1 | -1 | |||||
BraTS | CBICA_AVV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWG | -1 | -1 | -1 | |||||
BraTS | CBICA_AWH | -1 | -1 | -1 | |||||
BraTS | CBICA_AWI | -1 | -1 | -1 | |||||
BraTS | CBICA_AWV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWX | -1 | -1 | -1 | |||||
BraTS | CBICA_AXJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXL | -1 | -1 | -1 | |||||
BraTS | CBICA_AXM | -1 | -1 | -1 | |||||
BraTS | CBICA_AXN | -1 | -1 | -1 | |||||
BraTS | CBICA_AXO | -1 | -1 | -1 | |||||
BraTS | CBICA_AXQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXW | -1 | -1 | -1 | |||||
BraTS | CBICA_AYA | -1 | -1 | -1 | |||||
BraTS | CBICA_AYC | -1 | -1 | -1 | |||||
BraTS | CBICA_AYG | -1 | -1 | -1 | |||||
BraTS | CBICA_AYI | -1 | -1 | -1 | |||||
BraTS | CBICA_AYU | -1 | -1 | -1 | |||||
BraTS | CBICA_AYW | -1 | -1 | -1 | |||||
BraTS | CBICA_AZD | -1 | -1 | -1 | |||||
BraTS | CBICA_AZH | -1 | -1 | -1 | |||||
BraTS | CBICA_BAN | -1 | -1 | -1 | |||||
BraTS | CBICA_BAP | -1 | -1 | -1 | |||||
BraTS | CBICA_BAX | -1 | -1 | -1 | |||||
BraTS | CBICA_BBG | -1 | -1 | -1 | |||||
BraTS | CBICA_BCF | -1 | -1 | -1 | |||||
BraTS | CBICA_BCL | -1 | -1 | -1 | |||||
BraTS | CBICA_BDK | -1 | -1 | -1 | |||||
BraTS | CBICA_BEM | -1 | -1 | -1 | |||||
BraTS | CBICA_BFB | -1 | -1 | -1 | |||||
BraTS | CBICA_BFP | -1 | -1 | -1 | |||||
BraTS | CBICA_BGE | -1 | -1 | -1 | |||||
BraTS | CBICA_BGG | -1 | -1 | -1 | |||||
BraTS | CBICA_BGN | -1 | -1 | -1 | |||||
BraTS | CBICA_BGO | -1 | -1 | -1 | |||||
BraTS | CBICA_BGR | -1 | -1 | -1 | |||||
BraTS | CBICA_BGT | -1 | -1 | -1 | |||||
BraTS | CBICA_BGW | -1 | -1 | -1 | |||||
BraTS | CBICA_BGX | -1 | -1 | -1 | |||||
BraTS | CBICA_BHB | -1 | -1 | -1 | |||||
BraTS | CBICA_BHK | -1 | -1 | -1 | |||||
BraTS | CBICA_BHM | -1 | -1 | -1 | |||||
BraTS | CBICA_BHQ | -1 | -1 | -1 | |||||
BraTS | CBICA_BHV | -1 | -1 | -1 | |||||
BraTS | CBICA_BHZ | -1 | -1 | -1 | |||||
BraTS | CBICA_BIC | -1 | -1 | -1 | |||||
BraTS | CBICA_BJY | -1 | -1 | -1 | |||||
BraTS | CBICA_BKV | -1 | -1 | -1 | |||||
BraTS | CBICA_BLJ | -1 | -1 | -1 | |||||
BraTS | CBICA_BNR | -1 | -1 | -1 | |||||
BraTS | TMC_6290 | -1 | -1 | -1 | |||||
BraTS | TMC_6643 | -1 | -1 | -1 | |||||
BraTS | TMC_9043 | -1 | -1 | -1 | |||||
BraTS | TMC_11964 | -1 | -1 | -1 | |||||
BraTS | TMC_12866 | -1 | -1 | -1 | |||||
BraTS | TMC_15477 | -1 | -1 | -1 | |||||
BraTS | TMC_21360 | -1 | -1 | -1 | |||||
BraTS | TMC_27374 | -1 | -1 | -1 | |||||
BraTS | TMC_30014 | -1 | -1 | -1 | |||||
CPTAC-GBM | C3L-00016 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00019 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00265 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00278 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00349 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00424 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00429 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00506 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00528 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00591 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00631 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00636 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00671 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00674 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00677 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01045 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01046 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01142 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01156 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01327 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02041 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02465 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02504 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02704 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02706 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02707 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02708 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03260 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03266 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03727 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03728 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03747 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03748 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-04084 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00661 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00662 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00663 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00665 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01192 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01196 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01505 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01849 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01851 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01852 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02255 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02256 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02286 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03001 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03003 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03755 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-04686 | -1 | -1 | 4 | |||||
IvyGAP | W10 | 1 | 1 | 4 | |||||
IvyGAP | W11 | 0 | 0 | 4 | |||||
IvyGAP | W12 | 0 | 0 | 4 | |||||
IvyGAP | W13 | 0 | 0 | 4 | |||||
IvyGAP | W16 | 0 | 0 | 4 | |||||
IvyGAP | W18 | 0 | 0 | 4 | |||||
IvyGAP | W19 | 0 | 0 | 4 | |||||
IvyGAP | W1 | 0 | 0 | 4 | |||||
IvyGAP | W20 | 0 | 0 | 4 | |||||
IvyGAP | W21 | 0 | 0 | 4 | |||||
IvyGAP | W22 | 0 | 0 | -1 | |||||
IvyGAP | W26 | 0 | -1 | 4 | |||||
IvyGAP | W29 | 0 | 0 | 4 | |||||
IvyGAP | W2 | 0 | 1 | 4 | |||||
IvyGAP | W30 | 0 | 0 | 4 | |||||
IvyGAP | W31 | 1 | 1 | 4 | |||||
IvyGAP | W32 | 0 | 0 | 4 | |||||
IvyGAP | W33 | 0 | 0 | 4 | |||||
IvyGAP | W34 | 0 | 0 | 4 | |||||
IvyGAP | W35 | 1 | 0 | 3 | |||||
IvyGAP | W36 | 0 | 0 | 4 | |||||
IvyGAP | W38 | 0 | 0 | 4 | |||||
IvyGAP | W39 | 0 | 0 | 4 | |||||
IvyGAP | W3 | 1 | 0 | 4 | |||||
IvyGAP | W40 | 0 | 0 | 4 | |||||
IvyGAP | W42 | 0 | -1 | 4 | |||||
IvyGAP | W43 | 0 | -1 | 4 | |||||
IvyGAP | W45 | -1 | -1 | 4 | |||||
IvyGAP | W48 | 0 | -1 | 4 | |||||
IvyGAP | W4 | 1 | 0 | 4 | |||||
IvyGAP | W50 | 0 | -1 | 3 | |||||
IvyGAP | W53 | 1 | -1 | 4 | |||||
IvyGAP | W54 | 0 | -1 | 4 | |||||
IvyGAP | W55 | 0 | -1 | 4 | |||||
IvyGAP | W5 | 0 | 0 | 4 | |||||
IvyGAP | W6 | 0 | 0 | 4 | |||||
IvyGAP | W7 | 0 | 0 | 4 | |||||
IvyGAP | W8 | 0 | 0 | 4 | |||||
IvyGAP | W9 | 0 | 0 | 4 | |||||
REMBRANDT | 900-00-5299 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5303 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5308 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5316 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5317 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5332 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5339 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5341 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5342 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5346 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5380 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5381 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5382 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5385 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5396 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5404 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5412 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5414 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5458 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5459 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5462 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5468 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5476 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5477 | -1 | -1 | 2 | |||||
REMBRANDT | HF0763 | -1 | -1 | -1 | |||||
REMBRANDT | HF0828 | -1 | -1 | 3 | |||||
REMBRANDT | HF0835 | -1 | -1 | 2 | |||||
REMBRANDT | HF0855 | -1 | -1 | 2 | |||||
REMBRANDT | HF0868 | -1 | -1 | -1 | |||||
REMBRANDT | HF0883 | -1 | -1 | -1 | |||||
REMBRANDT | HF0899 | -1 | -1 | 2 | |||||
REMBRANDT | HF0920 | -1 | -1 | 2 | |||||
REMBRANDT | HF0931 | -1 | -1 | 2 | |||||
REMBRANDT | HF0953 | -1 | -1 | 2 | |||||
REMBRANDT | HF0960 | -1 | -1 | 2 | |||||
REMBRANDT | HF0966 | -1 | -1 | 3 | |||||
REMBRANDT | HF0986 | -1 | -1 | 4 | |||||
REMBRANDT | HF0990 | -1 | -1 | 4 | |||||
REMBRANDT | HF1000 | -1 | -1 | 2 | |||||
REMBRANDT | HF1058 | -1 | -1 | 4 | |||||
REMBRANDT | HF1059 | -1 | -1 | 3 | |||||
REMBRANDT | HF1071 | -1 | -1 | 4 | |||||
REMBRANDT | HF1077 | -1 | -1 | 4 | |||||
REMBRANDT | HF1078 | -1 | -1 | 4 | |||||
REMBRANDT | HF1097 | -1 | -1 | 4 | |||||
REMBRANDT | HF1113 | -1 | -1 | -1 | |||||
REMBRANDT | HF1122 | -1 | -1 | 4 | |||||
REMBRANDT | HF1136 | -1 | -1 | 3 | |||||
REMBRANDT | HF1139 | -1 | -1 | 4 | |||||
REMBRANDT | HF1150 | -1 | -1 | 3 | |||||
REMBRANDT | HF1156 | -1 | -1 | 2 | |||||
REMBRANDT | HF1167 | -1 | -1 | 2 | |||||
REMBRANDT | HF1185 | -1 | -1 | 3 | |||||
REMBRANDT | HF1191 | -1 | -1 | 4 | |||||
REMBRANDT | HF1199 | -1 | -1 | -1 | |||||
REMBRANDT | HF1219 | -1 | -1 | 3 | |||||
REMBRANDT | HF1227 | -1 | -1 | 2 | |||||
REMBRANDT | HF1232 | -1 | -1 | 3 | |||||
REMBRANDT | HF1235 | -1 | -1 | 2 | |||||
REMBRANDT | HF1242 | -1 | -1 | 3 | |||||
REMBRANDT | HF1246 | -1 | -1 | 2 | |||||
REMBRANDT | HF1264 | -1 | -1 | 2 | |||||
REMBRANDT | HF1269 | -1 | -1 | 4 | |||||
REMBRANDT | HF1280 | -1 | -1 | 3 | |||||
REMBRANDT | HF1292 | -1 | -1 | 4 | |||||
REMBRANDT | HF1293 | -1 | -1 | -1 | |||||
REMBRANDT | HF1297 | -1 | -1 | 4 | |||||
REMBRANDT | HF1300 | -1 | -1 | -1 | |||||
REMBRANDT | HF1307 | -1 | -1 | -1 | |||||
REMBRANDT | HF1316 | -1 | -1 | 2 | |||||
REMBRANDT | HF1318 | -1 | -1 | -1 | |||||
REMBRANDT | HF1325 | -1 | -1 | 2 | |||||
REMBRANDT | HF1331 | -1 | -1 | -1 | |||||
REMBRANDT | HF1334 | -1 | -1 | 2 | |||||
REMBRANDT | HF1344 | -1 | -1 | 2 | |||||
REMBRANDT | HF1345 | -1 | -1 | 2 | |||||
REMBRANDT | HF1357 | -1 | -1 | 3 | |||||
REMBRANDT | HF1381 | -1 | -1 | 2 | |||||
REMBRANDT | HF1397 | -1 | -1 | 4 | |||||
REMBRANDT | HF1398 | -1 | -1 | 3 | |||||
REMBRANDT | HF1407 | -1 | -1 | 2 | |||||
REMBRANDT | HF1409 | -1 | -1 | 3 | |||||
REMBRANDT | HF1420 | -1 | -1 | -1 | |||||
REMBRANDT | HF1429 | -1 | -1 | -1 | |||||
REMBRANDT | HF1433 | -1 | -1 | 2 | |||||
REMBRANDT | HF1437 | -1 | -1 | -1 | |||||
REMBRANDT | HF1442 | -1 | -1 | 2 | |||||
REMBRANDT | HF1458 | -1 | -1 | 3 | |||||
REMBRANDT | HF1463 | -1 | -1 | 2 | |||||
REMBRANDT | HF1489 | -1 | -1 | 2 | |||||
REMBRANDT | HF1490 | -1 | -1 | 3 | |||||
REMBRANDT | HF1493 | -1 | -1 | -1 | |||||
REMBRANDT | HF1510 | -1 | -1 | -1 | |||||
REMBRANDT | HF1511 | -1 | -1 | 2 | |||||
REMBRANDT | HF1517 | -1 | -1 | 4 | |||||
REMBRANDT | HF1538 | -1 | -1 | 4 | |||||
REMBRANDT | HF1551 | -1 | -1 | 2 | |||||
REMBRANDT | HF1553 | -1 | -1 | 2 | |||||
REMBRANDT | HF1560 | -1 | -1 | 4 | |||||
REMBRANDT | HF1568 | -1 | -1 | 2 | |||||
REMBRANDT | HF1587 | -1 | -1 | 3 | |||||
REMBRANDT | HF1588 | -1 | -1 | 2 | |||||
REMBRANDT | HF1606 | -1 | -1 | 2 | |||||
REMBRANDT | HF1613 | -1 | -1 | 3 | |||||
REMBRANDT | HF1628 | -1 | -1 | 4 | |||||
REMBRANDT | HF1652 | -1 | -1 | -1 | |||||
REMBRANDT | HF1677 | -1 | -1 | 2 | |||||
REMBRANDT | HF1702 | -1 | -1 | 3 | |||||
REMBRANDT | HF1708 | -1 | -1 | 2 | |||||
TCGA-GBM | TCGA-02-0003 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0006 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0009 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0011 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0027 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0033 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0034 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0037 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0046 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0047 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0048 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0054 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0059 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0060 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0064 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0068 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0069 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0070 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0075 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0085 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0086 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0087 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0102 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0106 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0116 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0119 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0122 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0128 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0130 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0132 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0133 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0137 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0138 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0139 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0142 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0145 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0149 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0154 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0158 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0162 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0164 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0166 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0168 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0175 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0176 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0177 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0179 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0182 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0184 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0185 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0187 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0188 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0189 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0190 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0192 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0213 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0238 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0240 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0241 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0644 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0646 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0648 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0649 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1084 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1802 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-2570 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5408 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5412 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5413 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5417 | 1 | -1 | 4 | |||||
TCGA-GBM | TCGA-06-6389 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0350 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0352 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0353 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0354 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0355 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0356 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0357 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0358 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0359 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0360 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0385 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0389 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0392 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0512 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0520 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0521 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0522 | -1 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0524 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0529 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0616 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0776 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1093 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1094 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1098 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1598 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1601 | 0 | -1 | -1 | |||||
TCGA-GBM | TCGA-12-1602 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-3650 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-0789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1456 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1794 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1825 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-3477 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-0963 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2624 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2631 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5951 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5954 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5958 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5960 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1834 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1838 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-2526 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4932 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-76-4934 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4935 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6191 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6193 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6280 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6282 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6285 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6656 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6657 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6661 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6662 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6663 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6664 | 0 | 0 | 4 | |||||
TCGA-LGG | TCGA-CS-4941 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4942 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4944 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5393 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-5395 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5396 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-CS-5397 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6186 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6188 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6290 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6665 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6666 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6667 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-6668 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-CS-6669 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5849 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-5851 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5852 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5854 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5871 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5874 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6397 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6399 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6400 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6404 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6405 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6407 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6410 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6542 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7008 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7010 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7014 | -1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7015 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7018 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7019 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7294 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-7298 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7300 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7302 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7309 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8162 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8164 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-8165 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8167 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8168 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-EZ-7265A | -1 | -1 | -1 | |||||
TCGA-LGG | TCGA-FG-5964 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-6688 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6691 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6692 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-7643 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-A713 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7473 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7616 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7680 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7684 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7686 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7692 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7693 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7694 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7856 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7860 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7874 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7879 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7882 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8105 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-8106 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8107 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8111 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8114 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8563 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A614 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0 | 2 |
Data_collection | Patient | IDH_mutated | Prediction_score_IDH_wildtype | Prediction_score_IDH_mutated | 1p19q_codeleted | Prediction_score_1p19q_codeleted | Prediction_score_1p19q_intact | Grade | Prediction_score_grade_2 | Prediction_score_grade_3 | Prediction_score_grade_4 | ||||||||||||
TCGA-GBM | TCGA-02-0003 | 0 | 099998915 | 10867886E-05 | 0 | 099996686 | 3308471E-05 | 4 | 7377526E-05 | 000074111245 | 099918514 | ||||||||||||
TCGA-GBM | TCGA-02-0006 | 0 | 042321962 | 05767803 | 0 | 068791837 | 031208166 | 4 | 060229343 | 026596427 | 013174225 | ||||||||||||
TCGA-GBM | TCGA-02-0009 | 0 | 099306935 | 0006930672 | 0 | 09906961 | 0009303949 | 4 | 0056565534 | 010282235 | 08406121 | ||||||||||||
TCGA-GBM | TCGA-02-0011 | 0 | 013531776 | 08646823 | 0 | 085318035 | 01468197 | 4 | 0015055533 | 092510724 | 005983725 | ||||||||||||
TCGA-GBM | TCGA-02-0027 | 0 | 09997279 | 000027212297 | 0 | 09986827 | 00013172914 | 4 | 00016104137 | 00038575265 | 0994532 | ||||||||||||
TCGA-GBM | TCGA-02-0033 | 0 | 099974436 | 000025564007 | 0 | 099940693 | 0000593021 | 4 | 00020670628 | 0003761288 | 09941717 | ||||||||||||
TCGA-GBM | TCGA-02-0034 | 0 | 091404164 | 008595832 | 0 | 089209336 | 01079066 | 4 | 00116944825 | 0061110377 | 092719513 | ||||||||||||
TCGA-GBM | TCGA-02-0037 | 0 | 09999577 | 42315594E-05 | 0 | 099992716 | 72827526E-05 | 4 | 82080274E-05 | 0009249337 | 09906686 | ||||||||||||
TCGA-GBM | TCGA-02-0046 | 0 | 0999129 | 00008710656 | 0 | 09989637 | 00010362669 | 4 | 0004290756 | 0022799779 | 097290945 | ||||||||||||
TCGA-GBM | TCGA-02-0047 | 0 | 099991703 | 83008505E-05 | 0 | 09999292 | 70863265E-05 | 4 | 000016252015 | 0040118434 | 095971906 | ||||||||||||
TCGA-GBM | TCGA-02-0048 | 0 | 09998785 | 000012148175 | 0 | 099959475 | 000040527192 | 4 | 00002215901 | 000039696065 | 09993814 | ||||||||||||
TCGA-GBM | TCGA-02-0054 | 0 | 09999831 | 1689829E-05 | 0 | 09999442 | 5583975E-05 | 4 | 00010063206 | 0060579527 | 093841416 | ||||||||||||
TCGA-GBM | TCGA-02-0059 | -1 | 09993749 | 000062511285 | 0 | 09996424 | 00003576683 | 4 | 00007046657 | 0010920537 | 09883748 | ||||||||||||
TCGA-GBM | TCGA-02-0060 | 0 | 07197039 | 028029615 | 0 | 09016612 | 009833879 | 4 | 017739706 | 03728545 | 04497484 | ||||||||||||
TCGA-GBM | TCGA-02-0064 | 0 | 09999083 | 9170197E-05 | 0 | 09995073 | 000049264234 | 4 | 000043781495 | 00028024286 | 099675983 | ||||||||||||
TCGA-GBM | TCGA-02-0068 | 0 | 099187535 | 0008124709 | 0 | 099528164 | 00047183693 | 4 | 00030539853 | 059695286 | 039999318 | ||||||||||||
TCGA-GBM | TCGA-02-0069 | 0 | 09890871 | 0010912909 | 0 | 099704784 | 0002952148 | 4 | 00057067247 | 0061368063 | 09329252 | ||||||||||||
TCGA-GBM | TCGA-02-0070 | 0 | 09940659 | 00059340666 | 0 | 0957794 | 0042206 | 4 | 0008216515 | 003556913 | 09562143 | ||||||||||||
TCGA-GBM | TCGA-02-0075 | 0 | 099933076 | 00006693099 | 0 | 099735296 | 00026470982 | 4 | 000044697264 | 00035736929 | 09959793 | ||||||||||||
TCGA-GBM | TCGA-02-0085 | 0 | 099114406 | 0008855922 | 0 | 09756698 | 002433019 | 4 | 00065203947 | 0035171553 | 095830804 | ||||||||||||
TCGA-GBM | TCGA-02-0086 | 0 | 099965334 | 000034666777 | 0 | 0998698 | 00013019645 | 4 | 000032699382 | 00018025768 | 099787045 | ||||||||||||
TCGA-GBM | TCGA-02-0087 | -1 | 09974885 | 00025114634 | 0 | 09990638 | 000093628286 | 4 | 0007505083 | 0008562708 | 098393226 | ||||||||||||
TCGA-GBM | TCGA-02-0102 | 0 | 09797647 | 0020235319 | 0 | 098292196 | 0017078074 | 4 | 003512482 | 03901857 | 05746895 | ||||||||||||
TCGA-GBM | TCGA-02-0106 | -1 | 099993694 | 6302759E-05 | 0 | 099980897 | 000019110431 | 4 | 60797247E-05 | 00008735659 | 09990657 | ||||||||||||
TCGA-GBM | TCGA-02-0116 | 0 | 09999778 | 22125667E-05 | 0 | 09996886 | 00003113695 | 4 | 000015498884 | 000051770627 | 09993273 | ||||||||||||
TCGA-GBM | TCGA-06-0119 | 0 | 09999362 | 63770494E-05 | 0 | 09999355 | 6452215E-05 | 4 | 000013225728 | 00028902534 | 099697745 | ||||||||||||
TCGA-GBM | TCGA-06-0122 | 0 | 09915298 | 0008470196 | 0 | 09859093 | 00140907345 | 4 | 00121390615 | 027333176 | 071452916 | ||||||||||||
TCGA-GBM | TCGA-06-0128 | 1 | 099988174 | 000011820537 | 0 | 099980634 | 000019373452 | 4 | 000016409029 | 0007865882 | 099197 | ||||||||||||
TCGA-GBM | TCGA-06-0130 | 0 | 099998784 | 12123987E-05 | 0 | 09999323 | 6775062E-05 | 4 | 80872844E-05 | 00026260202 | 099729306 | ||||||||||||
TCGA-GBM | TCGA-06-0132 | 0 | 09998566 | 000014341719 | 0 | 099988496 | 000011501736 | 4 | 000072843547 | 0005115947 | 099415565 | ||||||||||||
TCGA-GBM | TCGA-06-0133 | 0 | 097782 | 002218004 | 0 | 0993807 | 00061929906 | 4 | 0026753133 | 004659919 | 09266477 | ||||||||||||
TCGA-GBM | TCGA-06-0137 | 0 | 096448904 | 003551094 | 0 | 099403125 | 0005968731 | 4 | 000649511 | 038909483 | 060441005 | ||||||||||||
TCGA-GBM | TCGA-06-0138 | 0 | 09977743 | 00022256707 | 0 | 099736834 | 0002631674 | 4 | 00032954598 | 0011606657 | 09850979 | ||||||||||||
TCGA-GBM | TCGA-06-0139 | 0 | 09992649 | 00007350447 | 0 | 099898964 | 00010103129 | 4 | 00021781863 | 00069256434 | 099089617 | ||||||||||||
TCGA-GBM | TCGA-06-0142 | 0 | 099909425 | 00009057334 | 0 | 09985896 | 00014103584 | 4 | 0002598974 | 0046451908 | 09509491 | ||||||||||||
TCGA-GBM | TCGA-06-0145 | 0 | 099964654 | 000035350278 | 0 | 0999652 | 000034802416 | 4 | 00009068022 | 0021991275 | 0977102 | ||||||||||||
TCGA-GBM | TCGA-06-0149 | -1 | 09992161 | 00007839425 | 0 | 09981067 | 00018932257 | 4 | 00057726577 | 0013888515 | 09803388 | ||||||||||||
TCGA-GBM | TCGA-06-0154 | 0 | 099968064 | 000031937403 | 0 | 0999729 | 000027106237 | 4 | 000041507537 | 023430935 | 07652756 | ||||||||||||
TCGA-GBM | TCGA-06-0158 | 0 | 09999199 | 8014118E-05 | 0 | 099992514 | 74846226E-05 | 4 | 00026547876 | 020762624 | 0789719 | ||||||||||||
TCGA-GBM | TCGA-06-0162 | -1 | 099964297 | 00003569706 | 0 | 09997459 | 0000254147 | 4 | 000033955855 | 004318936 | 09564711 | ||||||||||||
TCGA-GBM | TCGA-06-0164 | -1 | 09983991 | 00016009645 | 0 | 09873262 | 0012673735 | 4 | 00016517473 | 00048346478 | 09935136 | ||||||||||||
TCGA-GBM | TCGA-06-0166 | 0 | 099991715 | 82846556E-05 | 0 | 0999554 | 00004459562 | 4 | 000013499439 | 0011635037 | 098823 | ||||||||||||
TCGA-GBM | TCGA-06-0168 | 0 | 09975561 | 00024438864 | 0 | 09964825 | 00035174883 | 4 | 0004766434 | 010448053 | 089075303 | ||||||||||||
TCGA-GBM | TCGA-06-0175 | -1 | 09996252 | 000037482675 | 0 | 09988098 | 00011902251 | 4 | 00026097735 | 004992068 | 094746953 | ||||||||||||
TCGA-GBM | TCGA-06-0176 | 0 | 099550986 | 00044901576 | 0 | 09998872 | 000011279297 | 4 | 0032868527 | 036690876 | 06002227 | ||||||||||||
TCGA-GBM | TCGA-06-0177 | -1 | 081774735 | 018225263 | 0 | 09946464 | 00053536464 | 4 | 0026683953 | 013013016 | 08431859 | ||||||||||||
TCGA-GBM | TCGA-06-0179 | -1 | 09997508 | 000024923254 | 0 | 09989778 | 00010222099 | 4 | 0002628482 | 0004127114 | 099324447 | ||||||||||||
TCGA-GBM | TCGA-06-0182 | -1 | 099999547 | 45838406E-06 | 0 | 099998736 | 12656287E-05 | 4 | 00002591103 | 000018499703 | 09995559 | ||||||||||||
TCGA-GBM | TCGA-06-0184 | 0 | 09935369 | 00064631375 | 0 | 099458355 | 00054164114 | 4 | 0023110552 | 0017436244 | 09594532 | ||||||||||||
TCGA-GBM | TCGA-06-0185 | 0 | 09999337 | 66310655E-05 | 0 | 099986255 | 000013738607 | 4 | 7657532E-05 | 0016089642 | 098383385 | ||||||||||||
TCGA-GBM | TCGA-06-0187 | 0 | 09991689 | 00008312097 | 0 | 099700147 | 00029984985 | 4 | 00020616595 | 0033111423 | 096482694 | ||||||||||||
TCGA-GBM | TCGA-06-0188 | 0 | 09883802 | 0011619771 | 0 | 09826743 | 0017325714 | 4 | 0013776424 | 0112841725 | 087338185 | ||||||||||||
TCGA-GBM | TCGA-06-0189 | 0 | 099906737 | 0000932636 | 0 | 09983865 | 00016135005 | 4 | 00022760795 | 00106745735 | 09870494 | ||||||||||||
TCGA-GBM | TCGA-06-0190 | 0 | 099954176 | 000045831292 | 0 | 09967013 | 00032986512 | 4 | 000040555766 | 0001246768 | 099834764 | ||||||||||||
TCGA-GBM | TCGA-06-0192 | 0 | 09997876 | 00002123566 | 0 | 09992735 | 00007264875 | 4 | 00004505576 | 00014473333 | 09981021 | ||||||||||||
TCGA-GBM | TCGA-06-0213 | 0 | 099986935 | 00001305845 | 0 | 099971646 | 000028351307 | 4 | 8755587E-05 | 00013480412 | 09985644 | ||||||||||||
TCGA-GBM | TCGA-06-0238 | 0 | 09999982 | 17603431E-06 | 0 | 09999894 | 10616134E-05 | 4 | 8076515E-05 | 56053756E-05 | 099986315 | ||||||||||||
TCGA-GBM | TCGA-06-0240 | 0 | 09989956 | 00010044163 | 0 | 099948466 | 00005152657 | 4 | 00016040986 | 021931975 | 077907616 | ||||||||||||
TCGA-GBM | TCGA-06-0241 | 0 | 099959785 | 000040211933 | 0 | 099910825 | 00008917038 | 4 | 00023411359 | 0007850656 | 098980826 | ||||||||||||
TCGA-GBM | TCGA-06-0644 | 0 | 09871044 | 0012895588 | 0 | 09859228 | 00140771745 | 4 | 0013671214 | 009819665 | 088813215 | ||||||||||||
TCGA-GBM | TCGA-06-0646 | 0 | 099959 | 00004100472 | 0 | 099936503 | 000063495064 | 4 | 00019223108 | 0040443853 | 095763385 | ||||||||||||
TCGA-GBM | TCGA-06-0648 | 0 | 09999709 | 29083441E-05 | 0 | 099982435 | 000017571273 | 4 | 000077678583 | 000038868992 | 099883455 | ||||||||||||
TCGA-GBM | TCGA-06-0649 | 0 | 09997805 | 000021952427 | 0 | 099951684 | 000048311835 | 4 | 0042641632 | 00058432207 | 095151514 | ||||||||||||
TCGA-GBM | TCGA-06-1084 | 0 | 099985826 | 000014174655 | 0 | 099968565 | 00003144242 | 4 | 00002676724 | 020492287 | 079480946 | ||||||||||||
TCGA-GBM | TCGA-06-1802 | -1 | 09991928 | 00008072305 | 0 | 09956176 | 0004382337 | 4 | 000043478087 | 00019495043 | 09976157 | ||||||||||||
TCGA-GBM | TCGA-06-2570 | 1 | 096841115 | 0031588882 | 0 | 09842457 | 0015754245 | 4 | 0015369608 | 0030956635 | 09536738 | ||||||||||||
TCGA-GBM | TCGA-06-5408 | 0 | 099857306 | 00014269598 | 0 | 09962638 | 00037362208 | 4 | 00027690146 | 0016195394 | 098103565 | ||||||||||||
TCGA-GBM | TCGA-06-5412 | 0 | 099366105 | 0006338921 | 0 | 099193794 | 0008061992 | 4 | 0011476759 | 006606435 | 09224589 | ||||||||||||
TCGA-GBM | TCGA-06-5413 | 0 | 09994105 | 000058955856 | 0 | 09983026 | 00016974095 | 4 | 00027100197 | 0021083053 | 097620696 | ||||||||||||
TCGA-GBM | TCGA-06-5417 | 1 | 01521267 | 08478733 | -1 | 03064492 | 06935508 | 4 | 013736826 | 037757674 | 048505494 | ||||||||||||
TCGA-GBM | TCGA-06-6389 | 1 | 099987435 | 000012558252 | 0 | 09997017 | 000029827762 | 4 | 00014020519 | 00020044278 | 099659353 | ||||||||||||
TCGA-GBM | TCGA-08-0350 | 0 | 019229275 | 08077072 | 0 | 0033211168 | 09667888 | 4 | 0051619414 | 022280572 | 072557485 | ||||||||||||
TCGA-GBM | TCGA-08-0352 | 0 | 099997497 | 25071595E-05 | 0 | 099992514 | 74846226E-05 | 4 | 000024192198 | 000048111935 | 099927694 | ||||||||||||
TCGA-GBM | TCGA-08-0353 | 0 | 09901496 | 0009850325 | 0 | 09967775 | 0003222484 | 4 | 00053748637 | 0004291497 | 09903336 | ||||||||||||
TCGA-GBM | TCGA-08-0354 | 0 | 076413894 | 023586108 | 0 | 07554566 | 024454337 | 4 | 008784444 | 02004897 | 071166587 | ||||||||||||
TCGA-GBM | TCGA-08-0355 | 0 | 09998349 | 000016506859 | 0 | 099984336 | 000015659066 | 4 | 000076689845 | 0023648744 | 09755844 | ||||||||||||
TCGA-GBM | TCGA-08-0356 | 0 | 097673583 | 0023264103 | 0 | 097773504 | 0022264915 | 4 | 001175834 | 0031075679 | 095716596 | ||||||||||||
TCGA-GBM | TCGA-08-0357 | 0 | 099509466 | 0004905406 | 0 | 099300176 | 00069982093 | 4 | 0005191745 | 0038681854 | 095612645 | ||||||||||||
TCGA-GBM | TCGA-08-0358 | 0 | 099999785 | 2199356E-06 | 0 | 099999034 | 9628425E-06 | 4 | 6113315E-06 | 00011283219 | 09988656 | ||||||||||||
TCGA-GBM | TCGA-08-0359 | 0 | 097885466 | 0021145396 | 0 | 09956006 | 00043994132 | 4 | 0009885523 | 0066605434 | 092350906 | ||||||||||||
TCGA-GBM | TCGA-08-0360 | 0 | 09922444 | 00077555366 | -1 | 09948704 | 00051296344 | 4 | 0013318472 | 003317344 | 095350814 | ||||||||||||
TCGA-GBM | TCGA-08-0385 | 0 | 099605453 | 00039454065 | -1 | 099686414 | 0003135836 | 4 | 00050293226 | 0029977333 | 096499336 | ||||||||||||
TCGA-GBM | TCGA-08-0389 | 0 | 099964714 | 000035281325 | 0 | 09991272 | 000087276706 | 4 | 00017554013 | 00024730961 | 099577147 | ||||||||||||
TCGA-GBM | TCGA-08-0390 | 0 | 099945146 | 000054847915 | 0 | 099936 | 00006399274 | 4 | 00036811908 | 00050958768 | 0991223 | ||||||||||||
TCGA-GBM | TCGA-08-0392 | 0 | 099962366 | 000037629317 | 0 | 09993575 | 00006424303 | 4 | 000036593352 | 0010291994 | 09893421 | ||||||||||||
TCGA-GBM | TCGA-08-0512 | -1 | 09982893 | 00017106998 | 0 | 099193794 | 0008061992 | 4 | 00016200381 | 00027773918 | 09956026 | ||||||||||||
TCGA-GBM | TCGA-08-0520 | -1 | 099603915 | 00039607873 | 0 | 09981933 | 00018066854 | 4 | 00007140295 | 0019064669 | 09802213 | ||||||||||||
TCGA-GBM | TCGA-08-0521 | -1 | 09975274 | 0002472623 | 0 | 099490017 | 00050998176 | 4 | 0001514669 | 0020103427 | 09783819 | ||||||||||||
TCGA-GBM | TCGA-08-0522 | -1 | 099960107 | 000039899128 | -1 | 09992053 | 00007947255 | 4 | 0000269389 | 0006173321 | 09935573 | ||||||||||||
TCGA-GBM | TCGA-08-0524 | -1 | 09964619 | 0003538086 | 0 | 099620515 | 0003794834 | 4 | 000019140428 | 0010096702 | 09897119 | ||||||||||||
TCGA-GBM | TCGA-08-0529 | -1 | 09996567 | 000034329997 | 0 | 099952066 | 00004793605 | 4 | 000032077235 | 0035970636 | 09637086 | ||||||||||||
TCGA-GBM | TCGA-12-0616 | 0 | 098521465 | 0014785408 | 0 | 098704207 | 001295789 | 4 | 001592791 | 012875569 | 08553164 | ||||||||||||
TCGA-GBM | TCGA-12-0776 | -1 | 099899167 | 00010083434 | 0 | 09987031 | 00012968953 | 4 | 0019219175 | 00637484 | 09170324 | ||||||||||||
TCGA-GBM | TCGA-12-0829 | 0 | 099913067 | 00008693674 | 0 | 099821776 | 00017821962 | 4 | 00021031094 | 0055067167 | 09428297 | ||||||||||||
TCGA-GBM | TCGA-12-1093 | 0 | 099992585 | 7411892E-05 | 0 | 09999448 | 5518923E-05 | 4 | 000046803855 | 0012115157 | 098741674 | ||||||||||||
TCGA-GBM | TCGA-12-1094 | -1 | 09980045 | 00019955388 | 0 | 09866105 | 0013389497 | 4 | 00053194338 | 001599471 | 097868586 | ||||||||||||
TCGA-GBM | TCGA-12-1098 | -1 | 09998406 | 000015936712 | 0 | 09977216 | 00022783307 | 4 | 000010218692 | 0035607774 | 09642901 | ||||||||||||
TCGA-GBM | TCGA-12-1598 | 0 | 096309197 | 0036908068 | 0 | 097933435 | 0020665688 | 4 | 0012952217 | 052912676 | 045792103 | ||||||||||||
TCGA-GBM | TCGA-12-1601 | 0 | 09875683 | 0012431651 | -1 | 0991891 | 0008108984 | -1 | 00118053425 | 0105477065 | 088271755 | ||||||||||||
TCGA-GBM | TCGA-12-1602 | 0 | 099830914 | 00016908031 | 0 | 099858415 | 00014158705 | 4 | 0008427611 | 0025996923 | 09655755 | ||||||||||||
TCGA-GBM | TCGA-12-3650 | 0 | 09761519 | 0023848088 | 0 | 097467697 | 0025323058 | 4 | 0010450666 | 043705726 | 05524921 | ||||||||||||
TCGA-GBM | TCGA-14-0789 | 0 | 099856466 | 00014353332 | 0 | 099666256 | 00033374047 | 4 | 0001406897 | 0008273975 | 099031913 | ||||||||||||
TCGA-GBM | TCGA-14-1456 | 1 | 006299064 | 093700933 | 0 | 08656222 | 013437784 | 4 | 016490369 | 047177824 | 036331803 | ||||||||||||
TCGA-GBM | TCGA-14-1794 | 0 | 08579393 | 014206071 | 0 | 09850429 | 0014957087 | 4 | 0023009384 | 009868736 | 08783033 | ||||||||||||
TCGA-GBM | TCGA-14-1825 | 0 | 099960107 | 000039899128 | 0 | 099968123 | 00003187511 | 4 | 0008552247 | 0010156045 | 09812918 | ||||||||||||
TCGA-GBM | TCGA-14-1829 | 0 | 090690076 | 009309922 | 0 | 09907856 | 0009214366 | 4 | 0008461936 | 0102735735 | 088880235 | ||||||||||||
TCGA-GBM | TCGA-14-3477 | 0 | 099796116 | 0002038787 | 0 | 09990728 | 00009271923 | 4 | 00032272525 | 0021644868 | 09751279 | ||||||||||||
TCGA-GBM | TCGA-19-0963 | -1 | 099876726 | 00012327607 | 0 | 09983612 | 00016388679 | 4 | 00031698826 | 013153598 | 086529416 | ||||||||||||
TCGA-GBM | TCGA-19-1390 | 0 | 099913234 | 00008676725 | 0 | 099703634 | 00029636684 | 4 | 00015592943 | 0026028048 | 097241265 | ||||||||||||
TCGA-GBM | TCGA-19-1789 | 0 | 09809491 | 00190509 | 0 | 09915216 | 0008478402 | 4 | 0038703684 | 014341596 | 08178804 | ||||||||||||
TCGA-GBM | TCGA-19-2624 | 0 | 07535573 | 024644265 | 0 | 09816127 | 0018387254 | 4 | 012311598 | 012769651 | 07491875 | ||||||||||||
TCGA-GBM | TCGA-19-2631 | 0 | 099860877 | 0001391234 | 0 | 09981178 | 00018821858 | 4 | 00009839778 | 001843531 | 09805807 | ||||||||||||
TCGA-GBM | TCGA-19-5951 | 0 | 09999031 | 9685608E-05 | 0 | 099977034 | 000022960825 | 4 | 00020246736 | 0004014765 | 09939606 | ||||||||||||
TCGA-GBM | TCGA-19-5954 | 0 | 099456257 | 00054374957 | 0 | 09968273 | 00031726828 | 4 | 00073725334 | 006310084 | 09295266 | ||||||||||||
TCGA-GBM | TCGA-19-5958 | 0 | 099999475 | 5234907E-06 | 0 | 0999941 | 58978338E-05 | 4 | 35422294E-05 | 86819025E-05 | 09998777 | ||||||||||||
TCGA-GBM | TCGA-19-5960 | 0 | 09683962 | 003160382 | 0 | 09013577 | 009864227 | 4 | 0011394806 | 018114014 | 08074651 | ||||||||||||
TCGA-GBM | TCGA-27-1834 | 0 | 099998164 | 18342893E-05 | 0 | 09999685 | 31446623E-05 | 4 | 8611921E-05 | 000031686216 | 0999597 | ||||||||||||
TCGA-GBM | TCGA-27-1838 | 0 | 09993625 | 000063743413 | 0 | 09940428 | 0005957154 | 4 | 00006736379 | 0007191195 | 099213517 | ||||||||||||
TCGA-GBM | TCGA-27-2526 | 0 | 099983776 | 000016219281 | 0 | 09996898 | 000031015594 | 4 | 000016658282 | 00006323714 | 09992011 | ||||||||||||
TCGA-GBM | TCGA-76-4932 | 0 | 09867389 | 0013261103 | -1 | 09949397 | 0005060332 | 4 | 00007321126 | 0003016794 | 099625117 | ||||||||||||
TCGA-GBM | TCGA-76-4934 | 0 | 099318933 | 0006810731 | 0 | 09995073 | 000049264234 | 4 | 00061555947 | 00070025027 | 098684186 | ||||||||||||
TCGA-GBM | TCGA-76-4935 | 0 | 074562997 | 025437003 | 0 | 098242307 | 001757688 | 4 | 076535034 | 006437644 | 017027317 | ||||||||||||
TCGA-GBM | TCGA-76-6191 | 0 | 09981067 | 00018932257 | 0 | 09970879 | 00029121784 | 4 | 00044340584 | 00096095055 | 098595643 | ||||||||||||
TCGA-GBM | TCGA-76-6193 | 0 | 09966168 | 0003383191 | 0 | 099850464 | 00014953383 | 4 | 00037061477 | 007873953 | 09175543 | ||||||||||||
TCGA-GBM | TCGA-76-6280 | 0 | 099948776 | 00005122569 | 0 | 099908185 | 000091819017 | 4 | 00001475792 | 000846075 | 099139166 | ||||||||||||
TCGA-GBM | TCGA-76-6282 | 0 | 0995906 | 0004093958 | 0 | 099861956 | 00013804223 | 4 | 00006694951 | 0009437619 | 09898929 | ||||||||||||
TCGA-GBM | TCGA-76-6285 | 0 | 099949074 | 00005092657 | 0 | 09971661 | 00028338495 | 4 | 00031175872 | 004005614 | 095682627 | ||||||||||||
TCGA-GBM | TCGA-76-6656 | 0 | 09996917 | 000030834455 | 0 | 09983897 | 00016103574 | 4 | 002648366 | 00017969633 | 09717193 | ||||||||||||
TCGA-GBM | TCGA-76-6657 | 0 | 099987245 | 000012755992 | 0 | 099951494 | 00004850083 | 4 | 000096620515 | 0005599633 | 09934342 | ||||||||||||
TCGA-GBM | TCGA-76-6661 | 0 | 093211424 | 006788577 | 0 | 09640178 | 0035982177 | 4 | 003490037 | 0026863772 | 09382358 | ||||||||||||
TCGA-GBM | TCGA-76-6662 | 0 | 096425414 | 0035745807 | 0 | 09963924 | 0003607617 | 4 | 002845819 | 002544755 | 09460942 | ||||||||||||
TCGA-GBM | TCGA-76-6663 | 0 | 088664144 | 0113358565 | 0 | 09984207 | 00015792594 | 4 | 0010206689 | 043740335 | 05523899 | ||||||||||||
TCGA-GBM | TCGA-76-6664 | 0 | 011047115 | 08895289 | 0 | 09559813 | 004401865 | 4 | 00049677677 | 08806894 | 011434281 | ||||||||||||
TCGA-LGG | TCGA-CS-4941 | 0 | 088931274 | 011068726 | 0 | 087037706 | 012962292 | 3 | 002865127 | 0048591908 | 092275685 | ||||||||||||
TCGA-LGG | TCGA-CS-4942 | 1 | 00031327847 | 099686724 | 0 | 096309197 | 0036908068 | 3 | 096261597 | 00148612335 | 0022522787 | ||||||||||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0005265965 | 099473405 | 0 | 09940544 | 00059455987 | 3 | 09439103 | 0023049146 | 003304057 | ||||||||||||
TCGA-LGG | TCGA-CS-4944 | 1 | 009363656 | 09063635 | 0 | 08755211 | 0124478824 | 2 | 034047556 | 033881712 | 03207073 | ||||||||||||
TCGA-LGG | TCGA-CS-5393 | 1 | 009623762 | 09037624 | 0 | 098178816 | 001821182 | 3 | 014111634 | 042021698 | 043866673 | ||||||||||||
TCGA-LGG | TCGA-CS-5395 | 0 | 08502822 | 014971776 | 0 | 09932025 | 00067975316 | 2 | 0052374925 | 018397054 | 076365453 | ||||||||||||
TCGA-LGG | TCGA-CS-5396 | 1 | 099839586 | 00016040892 | 1 | 099967945 | 000032062363 | 3 | 00016345463 | 029090768 | 07074577 | ||||||||||||
TCGA-LGG | TCGA-CS-5397 | 0 | 049304244 | 050695753 | 0 | 08829839 | 0117016025 | 3 | 038702008 | 021211159 | 040086827 | ||||||||||||
TCGA-LGG | TCGA-CS-6186 | 0 | 099913234 | 00008676725 | 0 | 099956185 | 000043818905 | 3 | 00008662089 | 016898473 | 083014905 | ||||||||||||
TCGA-LGG | TCGA-CS-6188 | 0 | 052768165 | 047231838 | 0 | 08584221 | 014157787 | 3 | 019437431 | 047675493 | 03288707 | ||||||||||||
TCGA-LGG | TCGA-CS-6290 | 1 | 09102666 | 008973339 | 0 | 09462997 | 0053700306 | 3 | 0104100704 | 025633416 | 06395651 | ||||||||||||
TCGA-LGG | TCGA-CS-6665 | 1 | 099600047 | 0003999501 | 0 | 099756086 | 00024391294 | 3 | 0011873978 | 001634113 | 097178483 | ||||||||||||
TCGA-LGG | TCGA-CS-6666 | 1 | 021655986 | 07834402 | 0 | 09327296 | 0067270435 | 3 | 017667453 | 036334327 | 045998225 | ||||||||||||
TCGA-LGG | TCGA-CS-6667 | 1 | 012061995 | 087938 | 0 | 095699733 | 0043002643 | 2 | 063733935 | 019323014 | 016943048 | ||||||||||||
TCGA-LGG | TCGA-CS-6668 | 1 | 0076787576 | 09232124 | 1 | 04240933 | 057590663 | 2 | 06810894 | 013706882 | 018184178 | ||||||||||||
TCGA-LGG | TCGA-CS-6669 | 0 | 08488156 | 01511844 | 0 | 094018847 | 005981148 | 2 | 0037862387 | 002352077 | 09386168 | ||||||||||||
TCGA-LGG | TCGA-DU-5849 | 1 | 005773187 | 094226813 | 1 | 08664153 | 013358466 | 2 | 072753835 | 015028271 | 012217898 | ||||||||||||
TCGA-LGG | TCGA-DU-5851 | 1 | 09963994 | 0003600603 | 0 | 099808073 | 00019192374 | 3 | 00060602655 | 012558761 | 08683521 | ||||||||||||
TCGA-LGG | TCGA-DU-5852 | 0 | 09998591 | 000014091856 | 0 | 099954873 | 000045121062 | 3 | 0002267452 | 00038046916 | 099392784 | ||||||||||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0010986943 | 09890131 | 0 | 09549844 | 0045015533 | 2 | 08603989 | 0077804394 | 0061796777 | ||||||||||||
TCGA-LGG | TCGA-DU-5854 | 0 | 09567354 | 0043264627 | 0 | 098768765 | 0012312326 | 3 | 01194655 | 027027336 | 06102612 | ||||||||||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0009312956 | 09906871 | 0 | 046602532 | 053397465 | 3 | 0008289882 | 097042197 | 0021288157 | ||||||||||||
TCGA-LGG | TCGA-DU-5871 | 1 | 005623634 | 09437636 | 0 | 09449439 | 005505607 | 2 | 042517176 | 020180763 | 037302068 | ||||||||||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0062359583 | 09376405 | 0 | 015278916 | 08472108 | 2 | 012133307 | 048199505 | 039667192 | ||||||||||||
TCGA-LGG | TCGA-DU-5874 | 1 | 022858672 | 077141327 | 1 | 06457066 | 03542934 | 2 | 058503634 | 020639434 | 02085693 | ||||||||||||
TCGA-LGG | TCGA-DU-6397 | 1 | 097691274 | 002308724 | 1 | 09908213 | 0009178773 | 3 | 00048094327 | 00412339 | 09539566 | ||||||||||||
TCGA-LGG | TCGA-DU-6399 | 1 | 00023920655 | 099760795 | 0 | 09970073 | 00029926652 | 2 | 098691386 | 0007037292 | 0006048777 | ||||||||||||
TCGA-LGG | TCGA-DU-6400 | 1 | 0030923586 | 09690764 | 1 | 037771282 | 06222872 | 2 | 09710506 | 0015339471 | 001360994 | ||||||||||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0014545513 | 098545444 | 0 | 045332992 | 054667014 | 2 | 0878585 | 006398724 | 005742785 | ||||||||||||
TCGA-LGG | TCGA-DU-6404 | 0 | 08563024 | 014369765 | 0 | 09857318 | 00142681915 | 3 | 0012578745 | 08931047 | 009431658 | ||||||||||||
TCGA-LGG | TCGA-DU-6405 | 0 | 094122344 | 0058776554 | 0 | 09657707 | 0034229323 | 3 | 0015099723 | 0858934 | 012596628 | ||||||||||||
TCGA-LGG | TCGA-DU-6407 | 1 | 00046772743 | 099532276 | 0 | 095787287 | 0042127114 | 2 | 095650303 | 0019410672 | 0024086302 | ||||||||||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0032852467 | 09671475 | 0 | 02978783 | 070212173 | 3 | 046377006 | 04552443 | 008098562 | ||||||||||||
TCGA-LGG | TCGA-DU-6410 | 1 | 084198 | 015801999 | 1 | 09610981 | 0038901985 | 3 | 0029748935 | 0547783 | 042246798 | ||||||||||||
TCGA-LGG | TCGA-DU-6542 | 1 | 099541724 | 0004582765 | 0 | 099690056 | 00030994152 | 3 | 00036504513 | 0033356518 | 0962993 | ||||||||||||
TCGA-LGG | TCGA-DU-7008 | 1 | 00027017966 | 09972982 | 0 | 09924154 | 0007584589 | 2 | 0945233 | 0033200152 | 0021566862 | ||||||||||||
TCGA-LGG | TCGA-DU-7010 | 1 | 09090629 | 0090937115 | 0 | 083999664 | 016000335 | 3 | 0011747591 | 011156695 | 08766855 | ||||||||||||
TCGA-LGG | TCGA-DU-7014 | -1 | 00067384504 | 09932615 | 0 | 09144437 | 008555635 | 2 | 090214694 | 005846623 | 003938676 | ||||||||||||
TCGA-LGG | TCGA-DU-7015 | 1 | 011059116 | 08894088 | 0 | 09457512 | 005424881 | 2 | 04990067 | 023008518 | 027090812 | ||||||||||||
TCGA-LGG | TCGA-DU-7018 | 1 | 06190684 | 038093168 | 1 | 09720721 | 0027927874 | 3 | 002608347 | 03462771 | 06276394 | ||||||||||||
TCGA-LGG | TCGA-DU-7019 | 1 | 006866228 | 09313377 | 0 | 068647516 | 031352484 | 3 | 06280373 | 02546188 | 011734395 | ||||||||||||
TCGA-LGG | TCGA-DU-7294 | 1 | 039513415 | 06048658 | 1 | 04910898 | 05089102 | 2 | 044678423 | 011827048 | 04349453 | ||||||||||||
TCGA-LGG | TCGA-DU-7298 | 1 | 002178117 | 097821885 | 0 | 058896303 | 041103697 | 3 | 04621931 | 040058115 | 013722575 | ||||||||||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0050494254 | 094950575 | 0 | 09805993 | 0019400762 | 3 | 088520575 | 003754964 | 0077244624 | ||||||||||||
TCGA-LGG | TCGA-DU-7300 | 1 | 020334144 | 07966585 | 1 | 021174264 | 078825736 | 3 | 06957292 | 014594184 | 015832895 | ||||||||||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0028517082 | 09714829 | 0 | 07594931 | 024050693 | 2 | 07559878 | 013617343 | 010783881 | ||||||||||||
TCGA-LGG | TCGA-DU-7302 | 1 | 007878401 | 092121595 | 1 | 097414124 | 0025858777 | 3 | 059945434 | 013100924 | 02695364 | ||||||||||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0049359404 | 09506406 | 0 | 09947084 | 0005291605 | 3 | 05746174 | 017312215 | 025226048 | ||||||||||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0774658 | 022534202 | 0 | 09720191 | 0027980946 | 2 | 007909051 | 04979186 | 042299092 | ||||||||||||
TCGA-LGG | TCGA-DU-7309 | 1 | 002068546 | 097931457 | 0 | 091696864 | 008303132 | 3 | 091011685 | 0041825026 | 0048058107 | ||||||||||||
TCGA-LGG | TCGA-DU-8162 | 0 | 019030987 | 08096902 | 0 | 084344435 | 015655571 | 3 | 06724078 | 015660264 | 017098951 | ||||||||||||
TCGA-LGG | TCGA-DU-8164 | 1 | 0026989132 | 09730109 | 1 | 06119184 | 038808158 | 2 | 078654927 | 011851947 | 00949313 | ||||||||||||
TCGA-LGG | TCGA-DU-8165 | 0 | 099918324 | 000081673806 | 0 | 09982692 | 00017308301 | 3 | 00077142627 | 001586733 | 097641844 | ||||||||||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0062617026 | 093738294 | 0 | 052265906 | 047734097 | 2 | 0571523 | 027175376 | 015672325 | ||||||||||||
TCGA-LGG | TCGA-DU-8167 | 1 | 008068282 | 09193171 | 0 | 08626991 | 013730097 | 2 | 07117111 | 014616342 | 014212546 | ||||||||||||
TCGA-LGG | TCGA-DU-8168 | 1 | 04501781 | 05498219 | 1 | 09405718 | 005942822 | 3 | 028535154 | 039651006 | 031813842 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 013576113 | 08642388 | 0 | 098667485 | 0013325148 | 3 | 06805368 | 011191124 | 020755199 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0038810804 | 09611892 | 0 | 094154674 | 005845324 | 2 | 07418394 | 01198958 | 013826479 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 036534345 | 06346565 | 0 | 097664696 | 0023353029 | 2 | 0076500095 | 07058904 | 021760948 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 057493186 | 042506814 | 0 | 08586593 | 014134066 | 3 | 024835269 | 018135522 | 05702921 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 017411166 | 082588834 | 0 | 08903419 | 0109658085 | 2 | 026840523 | 031951824 | 041207647 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 00015382263 | 099846184 | 0 | 09784259 | 0021574067 | 3 | 099424005 | 00014788082 | 0004281163 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 099497885 | 000502115 | 0 | 09904406 | 0009559399 | 3 | 00076062134 | 003340487 | 09589889 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 01338958 | 08661042 | 1 | 010181248 | 08981875 | 2 | 08703488 | 0033631936 | 0096019216 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 007097701 | 092902297 | 1 | 0049773447 | 09502266 | 2 | 08236395 | 0043779366 | 013258114 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 00054852334 | 09945148 | 1 | 00052813343 | 09947187 | 2 | 095740056 | 0030734295 | 0011865048 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 00015218158 | 099847823 | 0 | 09977216 | 00022783307 | 3 | 097611564 | 0011231668 | 0012652792 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 090418625 | 009581377 | 1 | 09320215 | 006797852 | 3 | 015433969 | 006605734 | 0779603 | ||||||||||||
TCGA-LGG | TCGA-EZ-7265A | -1 | 001654544 | 09834546 | -1 | 092290026 | 0077099696 | -1 | 091443384 | 0045349486 | 0040216673 | ||||||||||||
TCGA-LGG | TCGA-FG-5964 | 1 | 095945925 | 004054074 | 1 | 09480585 | 0051941562 | 2 | 0052469887 | 018844457 | 075908554 | ||||||||||||
TCGA-LGG | TCGA-FG-6688 | 0 | 041685596 | 0583144 | 0 | 0400786 | 0599214 | 3 | 032869554 | 028211078 | 03891937 | ||||||||||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0040960647 | 09590394 | 0 | 088871056 | 01112895 | 2 | 078484637 | 01247657 | 009038795 | ||||||||||||
TCGA-LGG | TCGA-FG-6691 | 1 | 00066411127 | 09933589 | 0 | 09705485 | 002945148 | 2 | 082394814 | 01353293 | 004072261 | ||||||||||||
TCGA-LGG | TCGA-FG-6692 | 0 | 099044985 | 0009550158 | 0 | 098370695 | 0016293105 | 3 | 002482948 | 023811981 | 07370507 | ||||||||||||
TCGA-LGG | TCGA-FG-7643 | 0 | 067991304 | 032008696 | 0 | 094600123 | 0053998843 | 2 | 032237333 | 020420441 | 047342223 | ||||||||||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 00037180893 | 09962819 | 0 | 098237246 | 0017627545 | 2 | 09685786 | 0019877713 | 0011543726 | ||||||||||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 0023916386 | 097608364 | 1 | 003330537 | 096669465 | 2 | 016134319 | 0751304 | 0087352775 | ||||||||||||
TCGA-LGG | TCGA-FG-A713 | 1 | 020932822 | 07906717 | 1 | 053740746 | 046259254 | 2 | 068370515 | 013241291 | 018388201 | ||||||||||||
TCGA-LGG | TCGA-HT-7473 | 1 | 026437023 | 073562974 | 0 | 09914391 | 0008560891 | 2 | 009070598 | 05834457 | 032584828 | ||||||||||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0014885316 | 09851147 | 0 | 093397486 | 006602513 | 3 | 09713343 | 0009202645 | 0019462984 | ||||||||||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0078306936 | 09216931 | 0 | 044295275 | 055704725 | 2 | 06683338 | 024550638 | 00861598 | ||||||||||||
TCGA-LGG | TCGA-HT-7616 | 1 | 0994089 | 0005911069 | 1 | 08912444 | 010875558 | 3 | 00015109215 | 00081261955 | 09903628 | ||||||||||||
TCGA-LGG | TCGA-HT-7680 | 0 | 01775255 | 08224745 | 0 | 079779327 | 020220678 | 2 | 06160002 | 021518312 | 016881672 | ||||||||||||
TCGA-LGG | TCGA-HT-7684 | 1 | 099250317 | 00074968883 | 0 | 09977216 | 00022783307 | 3 | 0001585032 | 0011880362 | 09865346 | ||||||||||||
TCGA-LGG | TCGA-HT-7686 | 1 | 043986762 | 05601324 | 0 | 09985134 | 00014866153 | 3 | 08800356 | 0017893802 | 010207067 | ||||||||||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0508178 | 049182203 | 0 | 09986749 | 00013250223 | 3 | 007351807 | 06479417 | 027854022 | ||||||||||||
TCGA-LGG | TCGA-HT-7692 | 1 | 0006764646 | 09932354 | 1 | 00017718028 | 099822825 | 2 | 084003216 | 009709251 | 00628754 | ||||||||||||
TCGA-LGG | TCGA-HT-7693 | 1 | 08835126 | 011648734 | 0 | 098880965 | 0011190402 | 2 | 00389769 | 060259813 | 035842496 | ||||||||||||
TCGA-LGG | TCGA-HT-7694 | 1 | 006299064 | 093700933 | 1 | 06663645 | 03336355 | 3 | 06368097 | 02515797 | 011161056 | ||||||||||||
TCGA-LGG | TCGA-HT-7855 | 1 | 013434944 | 086565053 | 0 | 06805072 | 031949285 | 3 | 03900612 | 035394293 | 02559958 | ||||||||||||
TCGA-LGG | TCGA-HT-7856 | 1 | 0037151825 | 09628482 | 1 | 045108467 | 05489153 | 3 | 0024809493 | 094240344 | 0032787096 | ||||||||||||
TCGA-LGG | TCGA-HT-7860 | 0 | 09996338 | 000036614697 | 0 | 099890125 | 00010987312 | 3 | 00023981468 | 004139189 | 095620996 | ||||||||||||
TCGA-LGG | TCGA-HT-7874 | 1 | 027373514 | 07262649 | 1 | 061277324 | 03872268 | 3 | 030690825 | 04321765 | 026091516 | ||||||||||||
TCGA-LGG | TCGA-HT-7879 | 1 | 006545533 | 09345446 | 0 | 07643643 | 023563562 | 3 | 07316188 | 01349482 | 0133433 | ||||||||||||
TCGA-LGG | TCGA-HT-7882 | 0 | 099826247 | 00017375927 | 0 | 099920684 | 0000793176 | 3 | 00026636408 | 0011237644 | 098609877 | ||||||||||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0045437213 | 09545628 | 0 | 09804874 | 0019512545 | 2 | 067944294 | 020575646 | 011480064 | ||||||||||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0090937115 | 09090629 | 0 | 08061669 | 019383314 | 2 | 069696444 | 017501967 | 012801588 | ||||||||||||
TCGA-LGG | TCGA-HT-8105 | 1 | 09291196 | 0070880495 | 1 | 09865976 | 0013402403 | 3 | 036353382 | 007970196 | 055676425 | ||||||||||||
TCGA-LGG | TCGA-HT-8106 | 1 | 09987081 | 00012918457 | 0 | 099922514 | 00007748164 | 3 | 0019909225 | 0057560045 | 09225307 | ||||||||||||
TCGA-LGG | TCGA-HT-8107 | 0 | 006150854 | 09384914 | 0 | 018944609 | 08105539 | 2 | 071847403 | 015055439 | 013097167 | ||||||||||||
TCGA-LGG | TCGA-HT-8111 | 1 | 062096643 | 037903354 | 0 | 09220272 | 007797278 | 3 | 00015017459 | 090417147 | 009432678 | ||||||||||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0003941571 | 099605846 | 0 | 00025608707 | 099743915 | 2 | 088384247 | 009021307 | 002594447 | ||||||||||||
TCGA-LGG | TCGA-HT-8114 | 1 | 09404078 | 005959219 | 0 | 09970708 | 00029292419 | 3 | 0015292862 | 028022403 | 07044831 | ||||||||||||
TCGA-LGG | TCGA-HT-8563 | 1 | 099999154 | 843094E-06 | 0 | 099999607 | 39515203E-06 | 3 | 32918017E-06 | 031742522 | 068257153 | ||||||||||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 06915494 | 030845058 | 0 | 045883363 | 054116637 | 3 | 012257236 | 02777765 | 059965116 | ||||||||||||
TCGA-LGG | TCGA-HT_A614 | 1 | 08180474 | 018195263 | 0 | 09584989 | 00415011 | 2 | 0067164555 | 0059489973 | 087334543 | ||||||||||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0035779487 | 09642206 | 0 | 07277821 | 0272218 | 2 | 07607039 | 014429174 | 009500429 |
Convolutionalneural network
IDH status
Wildtype Mutated
1p19q status
Intact Co-deleted
Grade
II III IV
WHO 2016categorization
MRI scansPreprocessed
scansSegmentation
Figure 1 Overview of our method Pre- and post-contrast T1w T2w and T2w-FLAIR scans are used as an input The scans are registered to an atlas biasfield corrected skull stripped and normalized before being passed through ourconvolutional neural network One branch of the network segments the tumorwhile at the same time the features are combined to predict the IDH status1p19q status and grade of the tumor
4
2 Results
21 Patient characteristics
We included a total of 1748 patients in our study 1508 as a train set and240 as an independent test set The patients in the train set originated fromnine different data collections and 16 different institutes and the test set wascollected from two different data collections and 13 different institutes Table 1provides a full overview of the patient characteristics in the train and test setand Figure 2 shows the inclusion flowchart and the distribution of the patientsover the different data collections in the train set and test set
Table 1 Patient characteristics for the train set and test set
Train set Test setN N
Patients 1508 240IDH status
Mutated 226 150 88 367Wildtype 440 292 129 537Unknown 842 558 23 96
1p19q co-deletion statusCo-deleted 103 68 26 108Intact 337 224 207 863Unknown 1068 708 7 29
GradeII 230 153 47 196III 114 76 59 246IV 830 550 132 550Unknown 334 221 2 08
WHO 2016 categorizationOligodendroglioma 96 64 26 108Astrocytoma IDH wildtype 31 21 22 92Astrocytoma IDH mutated 98 64 57 237GBM IDH wildtype 331 219 106 442GBM IDH mutated 16 11 5 21Unknown 936 621 24 100
SegmentationManual 716 475 240 100Automatic 792 525 0 0
IDH isocitrate dehydrogenase WHO World Health Organization GBMglioblastoma
5
Patient screening
Train set2181 Glioma patients
1241 Erasmus MC491 Haaglanden Medical Center168 BraTS130 REMBRANDT66 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set461 Glioma patients
199 TCGA-LGG262 TCGA-GBM
Patient inclusion
Train set1508 Patients in train set
816 Erasmus MC279 Haaglanden Medical Center168 BraTS109 REMBRANDT51 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set240 Patients in test set
107 TCGA-LGG133 TCGA-GBM
Patient exclusion
Train set673 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
425 Erasmus MC212 Haaglanden Medical Center
0 BraTS21 REMBRANDT15 CPTAC-GBM0 Ivy GAP0 Amsterdam UMC0 Brain-Tumor-Progression0 University Medical Center Utrecht
Test set221 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
92 TCGA-LGG129 TCGA-GBM
Figure 2 Inclusion flowchart of the train set and test set
6
22 Algorithm performance
We used 15 of the train set as a validation set and selected the model pa-rameters that achieved the best performance on this validation set where themodel achieved an area under receiver operating characteristic curve (AUC) of088 for the IDH mutation status prediction an AUC of 076 for the 1p19qco-deletion prediction an AUC of 075 for the grade prediction and a meansegmentation DICE score of 081 The selected model parameters are shown inAppendix F We then trained a model using these parameters and the full trainset and evaluated it on the independent test set
For the genetic and histological feature predictions we achieved an AUC of090 for the IDH mutation status prediction an AUC of 085 for the 1p19qco-deletion prediction and an AUC of 081 for the grade prediction in thetest set The full results are shown in Table 2 with the corresponding receiveroperating characteristic (ROC)-curves in Figure 3 Table 2 also shows the resultsin (clinically relevant) subgroups of patients This shows that we achieved anIDH-AUC of 081 in low grade glioma (LGG) (grade IIIII) an IDH-AUC of064 in high grade glioma (HGG) (grade IV) and a 1p19q-AUC of 073 in LGGWhen only predicting LGG vs HGG instead of predicting the individual gradeswe achieved an AUC of 091 In Appendix A we provide confusion matrices forthe IDH 1p19q and grade predictions as well as a confusion matrix for thefinal WHO 2016 subtype which shows that only one patient was predicted asa non-existing WHO 2016 subtype In Appendix C we provide the individualpredictions and ground truth labels for all patients in the test set to allow forthe calculation of additional metrics
For the automatic segmentation we achieved a mean DICE score of 084 amean Hausdorff distance of 189 mm and a mean volumetric similarity coeffi-cient of 090 Figure 4 shows boxplots of the DICE scores Hausdorff distancesand volumetric similarity coefficients for the different patients in the test set InAppendix B we show five patients that were randomly selected from both theTCGA-LGG and TCGA-GBM data collections to demonstrate the automaticsegmentations made by our method
23 Model interpretability
To provide insight into the behavior of our model we created saliency mapswhich show which parts of the scans contributed the most to the predictionThese saliency maps are shown in Figure 5 for two example patients from thetest set It can be seen that for the LGG the network focused on a bright rim inthe T2w-FLAIR scan whereas for the HGG it focused on the enhancement in thepost-contrast T1w scan To aid further interpretation we provide visualizationsof selected filter outputs in the network in Appendix D which also show thatthe network focuses on the tumor and these filters seem to recognize specificimaging features such as the contrast enhancement and T2w-FLAIR brightness
7
Table 2 Evaluation results of the final model on the test set
Patientgroup
Task AUC Accuracy Sensitivity Specificity
All IDH 090 084 072 0931p19q 085 089 039 095Grade (IIIIIIV) 081 071 NA NAGrade II 091 086 075 089Grade III 069 075 017 094Grade IV 091 082 095 066LGG vs HGG 091 084 072 093
LGG IDH 081 074 073 0771p19q 073 076 039 089
HGG IDH 064 094 040 096
Abbreviations AUC area under receiver operating characteristic curve IDHisocitrate dehydrogenase LGG low grade glioma HGG high grade glioma
Figure 3 Receiver operating characteristic (ROC)-curves of the genetic andhistological features evaluated on the test set The crosses indicate the locationof the decision threshold for the reported accuracy sensitivity and specificity
8
Figure 4 DICE scores Hausdorff distances and volumetric similarity coeffi-cients for all patients in the test set The DICE score is a measure of theoverlap between the ground truth and predicted segmentation (where 1 indi-cates perfect overlap) The Hausdorff distance is a measure of the agreementbetween the boundaries of the ground truth and predicted segmentation (loweris better) The volumetric similarity coefficient is a measure of the agreementin volume (where 1 indicates perfect agreement)
9
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Saliency maps of a low grade glioma patient (TCGA-DU-6400) This is an IDH mu-tated 1p19q co-deleted grade II tumor The network focuses on a rim of brightnessin the T2w-FLAIR scan
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) Saliency maps of a high grade glioma patient (TCGA-06-0238) This is an IDHwildtype grade IV tumor The network focuses on enhancing spots around the necrosison the post-contrast T1w scan
Figure 5 Saliency maps of two patients from the test set showing areas thatare relevant for the prediction
10
24 Model robustness
By not excluding scans from our train set based on radiological characteristicswe were able to make our model robust to low scan quality as can be seen in anexample from the test set in Figure 6 Even though this example scan containedimaging artifacts our method was able to properly segment the tumor (DICEscore of 087) and correctly predict the tumor as an IDH wildtype grade IVtumor
Figure 6 Example of a T2w-FLAIR scan containing imaging artifacts and theautomatic segmentation (red overlay) made by our method It was correctlypredicted as an IDH wildtype grade IV glioma This is patient TCGA-06-5408from the TCGA-GBM collection
Finally we considered two examples of scans that were incorrectly predictedby our method see Figure 7 These two examples were chosen because ournetwork assigned high prediction scores to the wrong classes for these casesFigure 7a shows an example of a grade II IDH mutated 1p19q co-deletedglioma that was predicted as grade IV IDH wildtype by our method Ourmethodrsquos prediction was most likely caused by the hyperintensities in the post-contrast T1w scan being interpreted as contrast enhancement Since these hy-perintensities are also present in the pre-contrast T1w scan they are most likelycalcifications and the radiological appearance of this tumor is indicative of anoligodendroglioma Figure 7b shows an example of a grade IV IDH wildtypeglioma that was predicted as a grade III IDH mutated glioma by our method
11
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) TCGA-DU-6410 from the TCGA-LGG collection The ground truth histopatho-logical analysis indicated this glioma was grade II IDH mutated 1p19q co-deletedbut our method predicted it as a grade IV IDH wildtype
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) TCGA-76-7664 from the TCGA-HGG collection Histopathologically this gliomawas grade IV IDH wildtype but our method predicted it as grade III IDH mutated
Figure 7 Examples of scans that were incorrectly predicted by our method
12
3 Discussion
We have developed a method that can predict the IDH mutation status 1p19qco-deletion status and grade of glioma while simultaneously providing the tu-mor segmentation based on pre-operative MRI scans For the genetic andhistological feature predictions we achieved an AUC of 090 for the IDH mu-tation status prediction an AUC of 085 for the 1p19q co-deletion predictionand an AUC of 081 for the grade prediction in the test set
In an independent test set which contained data from 13 different instituteswe demonstrated that our method predicts these features with good overall per-formance we achieved an AUC of 090 for the IDH mutation status predictionan AUC of 085 for the 1p19q co-deletion prediction and an AUC of 081 forthe grade prediction and a mean whole tumor DICE score of 084 This per-formance on unseen data that was only used during the final evaluation of thealgorithm and that was purposefully not used to guide any decisions regardingthe method design shows the true generalizability of our method Using thelatest GPU capabilities we were able to train a large model which uses thefull 3D scan as input Furthermore by using the largest most diverse patientcohort to date we were able to make our method robust to the heterogeneitythat is naturally present in clinical imaging data such that it generalizes forbroad application in clinical practice
By using a multi-task network our method could learn the context betweendifferent features For example IDH wildtype and 1p19q co-deletion are mu-tually exclusive [21] If two separate methods had been used one to predict theIDH status and one to predict the 1p19q co-deletion status an IDH wildtypeglioma might be predicted to be 1p19q co-deleted which does not stroke withthe clinical reality Since our method learns both of these genetic features si-multaneously it correctly learned not to predict 1p19q co-deletion in tumorsthat were IDH wildtype there was only one patient in which our algorithm pre-dicted a tumor to be both IDH wildtype and 1p19q co-deleted Furthermoreby predicting the genetic and histological features individually instead of onlypredicting the WHO 2016 category it is possible to adopt updated guidelinessuch as cIMPACT-NOW future-proofing our method [22]
Some previous studies also used multi-task networks to predict the geneticand histological features of glioma [23 24 25] Tang et al [23] used a multi-tasknetwork that predicts multiple genetic features as well as the overall survivalof glioblastoma Since their method only works for glioblastoma patients thetumor grade must be known in advance complicating the use of their methodin the pre-operative setting when tumor grade is not yet known Furthermoretheir method requires a tumor segmentation prior to application of their methodwhich is a time-consuming expert task In a study by Xue et al [24] a multi-task network was used with a structure similar to the one proposed in thispaper to segment the tumor and predict the grade (LGG or HGG) and IDHmutation status However they do not predict the 1p19q co-deletion statusneeded for the WHO 2016 categorization Lastly Decuyper et al [25] useda multi-task network that predicts the IDH mutation and 1p19q co-deletion
13
status and the tumor grade (LGG or HGG) Their method requires a tumorsegmentation as input which they obtain from a U-Net that is applied earlierin their pipeline thus their method requires two networks instead of the singlenetwork we use in our method These differences aside the most importantlimitation of each of these studies is the lack of an independent test set forevaluating their results It is now considered essential that an independent testset is used to prevent an overly optimistic estimate of a methodrsquos performance[15 26 27 28] Thus our study improves on this previous work by providinga single network that combines the different tasks being trained on a moreextensive and diverse dataset not requiring a tumor segmentation as an in-put providing all information needed for the WHO 2016 categorization andcrucially by being evaluated in an independent test set
An important genetic feature that is not predicted by our method is theO6-methylguanine-methyltransferase (MGMT) methylation status Althoughthe MGMT methylation status is not part of the WHO 2016 categorizationit is part of clinical management guidelines and is an important prognosticmarker in glioblastoma [4] In the initial stages of this study we attemptedto predict the MGMT methylation status however the performance of thisprediction was poor Furthermore the methylation cutoff level which is usedto determine whether a tumor is MGMT methylated shows a wide varietybetween institutes leading to inconsistent results [29] We therefore opted not toinclude the MGMT prediction at all rather than to provide a poor prediction ofan unsharply defined parameter Although some methods attempted to predictthe MGMT status with varying degrees of success there is still an ongoingdiscussion on the validity of MR imaging features of the MGMT status [23 3031 32 33]
Our method shows good overall performance but there are noticeable per-formance differences between tumor categories For example when our methodpredicts a tumor as an IDH wildtype glioblastoma it is correct almost all of thetime On the other hand it has some difficulty differentiating IDH mutated1p19q co-deleted low-grade glioma from other low-grade glioma The sensitiv-ity for the prediction of grade III glioma was low which might be caused bythe lack of a central pathology review Because of this there were differences inmolecular testing and histological analysis and it is known that distinguishingbetween grade II and grade III has a poor observer reliability [34] Althoughour method can be relevant for certain subgroups our methodrsquos performancestill needs to be improved to ensure relevancy for the full patient population
In future work we aim to increase the performance of our method by includ-ing perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI)since there has been an increasing amount of evidence that these physiologi-cal imaging modalities contain additional information that correlates with thetumorrsquos genetic status and aggressiveness [35 36] They were not included inthis study since PWI and to a lesser extent DWI are not as ingrained in theclinical imaging routine as the structural scans used in this work [19 20] Thusincluding these modalities would limit our methodrsquos clinical applicability andsubstantially reduce the number of patients in the train and test set However
14
PWI and DWI are increasingly becoming more commonplace which will allowincluding these in future research and which might improve performance
In conclusion we have developed a non-invasive method that can predict theIDH mutation status 1p19q co-deletion status and grade of glioma while atthe same time segmenting the tumor based on pre-operative MRI scans withhigh overall performance Although the performance of our method might needto be improved before it will find widespread clinical acceptance we believe thatthis research is an important step forward in the field of radiomics Predict-ing multiple clinical features simultaneously steps away from the conventionalsingle-task methods and is more in line with the clinical practice where multipleclinical features are considered simultaneously and may even be related Fur-thermore by not limiting the patient population used to develop our method toa selection based on clinical or radiological characteristics we alleviate the needfor a priori (expert) knowledge which may not always be available Althoughsteps still have to be taken before radiomics will find its way into the clinicespecially in terms of performance our work provides a crucial step forward byresolving some of the hurdles of clinical implementation now and paving theway for a full transition in the future
4 Methods
41 Patient population
The train set was collected from four in-house datasets and five publicly avail-able datasets In-house datasets were collected from four different institutesErasmus MC (EMC) Haaglanden Medical Center (HMC) Amsterdam UMC(AUMC) [37] and University Medical Center Utrecht (UMCU) Four of the fivepublic datasets were collected from The Cancer Imaging Archive (TCIA) [38]the Repository of Molecular Brain Neoplasia Data (REMBRANDT) collection[39] the Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multi-forme (CPTAC-GBM) collection [40] the Ivy Glioblastoma Atlas Project (IvyGAP) collection [41 42] and the Brain-Tumor-Progression collection [43] Thefifth dataset was the 2019 Brain Tumor Segmentation challenge (BraTS) chal-lenge dataset [44 45 46] from which we excluded the patients that were alsoavailable in the TCGA-LGG and TCGA-GBM collections [47 48]
For the internal datasets from the EMC and the HMC manual segmentationswere available which were made by four different clinical experts For patientswhere segmentations from more than one observer were available we randomlypicked one of the segmentations to use in the train set The segmentationsfrom the AUMC data were made by a single observer of the study by Visseret al [37] From the public datasets only the BraTS dataset and the Brain-Tumor-Progression dataset provided manual segmentations Segmentations ofthe BraTS dataset as provided in the 2019 training and validation set wereused For the Brain-Tumor-Progression dataset the segmentations as providedin the TCIA data collection were used
15
Patients were included if pre-operative pre- and post-contrast T1w T2wand T2w-FLAIR scans were available no further inclusion criteria were setFor example patients were not excluded based on the radiological characteristicsof the scan such as low imaging quality or imaging artifacts or the gliomarsquosclinical characteristics such as the grade If multiple scans of the same contrasttype were available in a single scan session (eg multiple T2w scans) the scanupon which the segmentation was made was selected If no segmentation wasavailable or the segmentation was not made based on that scan contrast thescan with the highest axial resolution was used where a 3D acquisition waspreferred over a 2D acquisition
For the in-house data genetic and histological data were available for theEMC HMC and UMCU dataset which were obtained from analysis of tumortissue after biopsy or resection Genetic and histological data of the publicdatasets were also available for the REMBRANDT CPTAC-GBM and IvyGAP collections Data for the REMBRANDT and CPTAC-GBM collectionswas collected from the clinical data available at the TCIA [40 39] For theIvy GAP collection the genetic and histological data were obtained from theSwedish Institute at httpsivygapswedishorghome
As a test set we used the TCGA-LGG and TCGA-GBM collections from theTCIA [47 48] Genetic and histological labels were obtained from the clinicaldata available at the TCIA Segmentations were used as available from theTCIA based on the 2018 BraTS challenge [45 49 50] The inclusion criteriafor the patients included in the BraTS challenge were the same as our inclusioncriteria the presence of a pre-operative pre- and post-contrast T1w T2w andT2w-FLAIR scan Thus patients from the TCGA-LGG and TCGA-GBM wereincluded if a segmentation from the BraTS challenge was available Howeverfor three patients we found that although they did have manual segmentationsthey did not meet our inclusion requirements TCGA-08-0509 and TCGA-08-0510 from TCGA-GBM because they did not have a pre-contrast T1w scan andTCGA-FG-7634 from TCGA-LGG because there was no post-contrast T1wscan
42 Automatic segmentation in the train set
To present our method with a large diversity in scans we wanted to include asmany patients in the train set as possible from the different datasets Thereforewe performed automatic segmentation in patients that did not have manualsegmentations To this end we used an initial version of our network (presentedin Section 44) without the additional layers that were needed for the predictionof the genetic and histological features This network was initially trained usingall patients in the train set for whom a manual segmentation was availableand this trained network was then applied to all patients for which a manualsegmentation was not available The resulting automatic segmentations wereinspected and if their quality was acceptable they were added to the trainset The network was then trained again using this increased dataset and wasapplied to scans that did not yet have a segmentation of acceptable quality
16
This process was repeated until an acceptable segmentation was available forall patients which constituted our final complete train set
43 Pre-processing
For all datasets except for the BraTS dataset for which the scans were alreadyprovided in NIfTI format the scans were converted from DICOM format toNIfTI format using dcm2niix version v1020190410 [51] We then registeredall scans to the MNI152 T1w and T2w atlases version ICBM 2009a whichhad a resolution of 1x1x1 mm3 and a size of 197x233x189 voxels [52 53] Thescans were affinely registered using Elastix 50 [54 55] The pre- and post-contrast T1w scans were registered to the T1w atlas the T2w and T2w-FLAIRscans were registered to the T2w atlas When a manual segmentation wasavailable for patients from the in-house datasets the registration parametersthat resulted from registering the scan used during the segmentation were usedto transform the segmentation to the atlas In the case of the public datasetswe used the registration parameters of the T2w-FLAIR scans to transform thesegmentations
After the registration all scans were N4 bias field corrected using SimpleITKversion 124 [56] A brain mask was made for the atlas using HD-BET both forthe T1w atlas and the T2w atlas [57] This brain mask was used to skull stripall registered scans and crop them to a bounding box around the brain maskreducing the amount of background present in the scans resulting in a scan sizeof 152x182x145 voxels Subsequently the scans were normalized such that foreach scan the average image intensity was 0 and the standard deviation of theimage intensity was 1 within the brain mask Finally the background outsidethe brain mask was set to the minimum intensity value within the brain mask
Since the segmentation could sometimes be rugged at the edges after regis-tration especially when the segmentations were initially made on low-resolutionscans we smoothed the segmentation using a 3x3x3 median filter (this was onlydone in the train set) For segmentations that contained more than one la-bel eg when the tumor necrosis and enhancement were separately segmentedall labels were collapsed into a single label to obtain a single segmentation ofthe whole tumor The genetic and histological labels and the segmentations ofeach patient were one-hot encoded The four scans ground truth labels andsegmentation of each patient were then used as the input to the network
44 Model
We based the architecture of our model on the U-Net architecture with someadaptations made to allow for a full 3D input and the auxiliary tasks [58] Ournetwork architecture which we have named PrognosAIs Structure-Net or PS-Net for short can be seen in Figure 8
To use the full 3D scan as an input to the network we replaced the firstpooling layer that is usually present in the U-Net with a strided convolutionwith a kernel size of 9x9x9 and a stride of 3x3x3 In the upsampling branch of
17
32
32 64
128
256
512 256
7x8x7256 128
128 64
64 32
32 2
Segmentation
145x182x152
49x61x51
25x31x26
13x16x13
1472
512 2IDH
512 2
1p19q
512 3Grade
Batch normalization Concatenation Convolution amp ReLU3x3x3
Convolution amp Softmax1x1x1
(De)convolution amp ReLU9x9x9
stride 3x3x3
Dense amp ReLU Dense amp Softmax Dropout
Max pooling2x2x2
Up-convolution amp ReLU2x2x2
Global maxpooling
Figure 8 Overview of the PrognosAIs Structure-Net (PS-Net) architecture usedfor our model The numbers below the different layers indicate the number offilters dense units or features at that layer We have also indicated the featuremap size at the different depths of the network
the network the last up-convolution is replaced by a deconvolution with thesame kernel size and stride
At each depth of the network we have added global max-pooling layersdirectly after the dropout layer to obtain imaging features that can be used topredict the genetic and histological features We chose global pooling layers asthey do not introduce any additional parameters that need to be trained thuskeeping the memory required by our model manageable The features from thedifferent depths of the network were concatenated and fed into three differentdense layers one for each of the genetic and histological outputs
l2 kernel regularization was used in all convolutional layers except for thelast convolutional layer used for the output of the segmentation In total thismodel contained 27042473 trainable an 2944 non-trainable parameters
18
45 Model training
Training of the model was done on eight NVidia RTX2080Tirsquos with 11GB ofmemory using TensorFlow 220 [59] To be able to use the full 3D scan asinput to the network without running into memory issues we had to optimizethe memory efficiency of the model training Most importantly we used mixed-precision training which means that most of the variables of the model (such asthe weights) were stored in float16 which requires half the memory of float32which is typically used to store these variables [60] Only the last softmaxactivation layers of each output were stored as float32 We also stored ourpre-processed scans as float16 to further reduce memory usage
However even with these settings we could not use a batch size larger than1 It is known that a larger batch size is preferable as it increases the stabilityof the gradient updates and allows for a better estimation of the normalizationparameters in batch normalization layers [61] Therefore we distributed thetraining over the eight GPUs using the NCCL AllReduce algorithm whichcombines the gradients calculated on each GPU before calculating the updateto the model parameters [62] We also used synchronized batch normalizationlayers which synchronize the updates of their parameters over the distributedmodels In this way our model had a virtual batch size of eight for the gradientupdates and the batch normalization layers parameters
To provide more samples to the algorithm and prevent potential overtrain-ing we applied four types of data augmentation during training croppingrotation brightness shifts and contrast shifts Each augmentation was appliedwith a certain augmentation probability which determined the probability ofthat augmentation type being applied to a specific sample When an image wascropped a random number of voxels between 0 and 20 was cropped from eachdimension and filled with zeros For the random rotation an angle betweenminus30 and 30 degrees was selected from a uniform distribution for each dimen-sion The brightness shift was applied with a delta uniformly drawn between0 and 02 and the contrast shift factor was randomly drawn between 085 and115 We also introduced an augmentation factor which determines how ofteneach sample was parsed as an input sample during a single epoch where eachtime it could be augmented differently
For the IDH 1p19q and grade output we used a masked categorical cross-entropy loss and for the segmentation we used a DICE loss see Appendix E fordetails We used AdamW as an optimizer which has shown improved general-ization performance over Adam by introducing the weight decay parameter as aseparate parameter from the learning rate [63] The learning rate was automat-ically reduced by a factor of 025 if the loss did not improve during the last fiveepochs with a minimum learning rate of 1 middot 10minus11 The model could train for amaximum of 150 epochs and training was stopped early if the average loss overthe last five epochs did not improve Once the model was finished training theweights from the epoch with the lowest loss were restored
19
46 Hyperparameter tuning
Hyperparameters involved in the training of the model needed to be tuned toachieve the best performance We tuned a total of six hyper parameters the l2-norm the dropout rate the augmentation factor the augmentation probabilitythe optimizerrsquos initial learning rate and the optimizerrsquos weight decay A fulloverview of the trained parameters and the values tested for the different settingsis presented in Appendix F
To tune these hyperparameters we split the train set into a hyperparametertraining set (851282 patients of the full train data) and a hyperparametervalidation set (15226 patients of the full train data) Models were trained fordifferent hyperparameter settings via an exhaustive search using the hyperpa-rameter train set and then evaluated on the hyperparameter validation set Nodata augmentation was applied to the hyperparameter validation to ensure thatresults between trained models were comparable The hyperparameters that ledto the lowest overall loss in the hyperparameter validation set were chosen asthe optimal hyperparameters We trained the final model using these optimalhyperparameters and the full train set
47 Post-processing
The predictions of the network were post-processed to obtain the final predictedlabels and segmentations for the samples Since softmax activations were usedfor the genetic and histological outputs a prediction between 0 and 1 was out-putted for each class where the individual predictions summed to 1 The finalpredicted label was then considered as the class with the highest prediction scoreFor the prediction of LGG (grade IIIII) vs HGG (grade IV) the predictionscores of grade II and grade III were combined to obtain the prediction scorefor LGG the prediction score of grade IV was used as the prediction score forHGG If a segmentation contained multiple unconnected components we onlyretained the largest component to obtain a single whole tumor segmentation
48 Model evaluation
The performance of the final trained model was evaluated on the independenttest set comparing the predicted labels with the ground truth labels For thegenetic and histological features we evaluated the AUC the accuracy the sen-sitivity and the specificity using scikit-learn version 0231 for details see Ap-pendix G [64] We evaluated these metrics on the full test set and in subcate-gories relevant to the WHO 2016 guidelines We evaluated the IDH performanceseparately in the LGG (grade IIIII) and HGG (grade IV) subgroups the 1p19qperformance in LGG and we also evaluated the performance of distinguishingbetween LGG and HGG instead of predicting the individual grades
To evaluate the performance of the segmentation we calculated the DICEscores Hausdorff distances and volumetric similarity coefficient comparing theautomatic segmentation of our method and the manual ground truth segmen-
20
tations for all patients in the test set These metrics were calculated usingthe EvaluateSegmentation toolbox version 20170425 [65] for details see Ap-pendix G
To prevent an overly optimistic estimation of our modelrsquos predictive valuewe only evaluated our model on the test set once all hyperparameters werechosen and the final model was trained In this way the performance in thetest set did not influence decisions made during the development of the modelpreventing possible overfitting by fine-tuning to the test set
To gain insight into the model we made saliency maps that show whichparts of the scan contribute the most to the prediction of the CNN [66] Saliencymaps were made using tf-keras-vis 052 changing the activation function of alloutput layers from softmax to linear activations using SmoothGrad to reducethe noisiness of the saliency maps [66]
Another way to gain insight into the networkrsquos behavior is to visualize thefilter outputs of the convolutional layers as they can give some idea as to whatoperations the network applies to the scans We visualized the filter outputs ofthe last convolutional layers in the downsample and upsample path at the firstdepth (at an image size of 49x61x51) of our network These filter outputs werevisualized by passing a sample through the network and showing the convolu-tional layersrsquo outputs replacing the ReLU activation with linear activations
49 Data availability
An overview of the patients included from the public datasets used in the train-ing and testing of the algorithm and their ground truth label is available inAppendix H The data from the public datasets are available in TCIA un-der DOIs 107937K9TCIA2015588OZUZB 107937k9tcia20183rje41q1107937K9TCIA2016XLwaN6nL and 107937K9TCIA201815quzvnb Datafrom the BraTS are available at httpbraintumorsegmentationorg Datafrom the in-house datasets are not publicly available due to participant privacyand consent
410 Code availability
The code used in this paper is available on GitHub under an Apache 2 license athttpsgithubcomSvdvoortPrognosAIs_glioma This code includes thefull pipeline from registration of the patients to the final post-processing of thepredictions The trained model is also available on GitHub along with code toapply it to new patients
21
Appendices
A Confusion matrices
Tables 3 4 and 5 show the confusion matrices for the IDH 1p19q and gradepredictions and Table 6 shows the confusion matrix for the WHO 2016 subtypes
Table 4 shows that the algorithm mainly has difficulty recognizing 1p19qco-deleted tumors which are mostly predicted as 1p19q intact Table 5 showsthat most of the incorrectly predicted grade III tumors are predicted as gradeIV tumors
Table 6 shows that our algorithm often incorrectly predicts IDH-wildtypeastrocytoma as IDH-wildtype glioblastoma The latest cIMPACT-NOW guide-lines propose a new categorization in which IDH-wildtype astrocytoma thatshow either TERT promoter methylation or EFGR gene amplification or chro-mosome 7 gainchromsome 10 loss are classified as IDH-wildtype glioblastoma[22] This new categorization is proposed since the survival of patients withthose IDH-wildtype astrocytoma is similar to the survival of patients with IDH-wildtype glioblastoma [22] From the 13 IDH-wildtype astrocytoma that werewrongly predicted as IDH-wildtype glioblastoma 12 would actually be cate-gorized as IDH-wildtype glioblastoma under this new categorization Thusalthough our method wrongly predicted the WHO 2016 subtype it might ac-tually have picked up on imaging features related to the aggressiveness of thetumor which might lead to a better categorization
Table 3 Confusion matrix of the IDH predictions
Predicted
Wildtype Mutated
Actu
al
Wildtype 120 9
Mutated 25 63
Table 4 Confusion matrix of the 1p19q predictions
Predicted
Intact Co-deleted
Actu
al
Intact 197 10
Co-deleted 16 10
22
Table 5 Confusion matrix of the grade predictions
Predicted
Grade II Grade III Grade IV
Actu
al Grade II 35 6 6
Grade III 19 10 30
Grade IV 2 5 125
Table 6 Confusion matrix of the WHO 2016 predictions The rsquootherrsquo categoryindicates patients that were predicted as a non-existing WHO 2016 subtype forexample IDH wildtype 1p19q co-deleted tumors Only one patient (TCGA-HT-A5RC) was predicted as a non-existing category It was predicted as anIDH wildtype 1p19q co-deleted grade IV tumor
Predicted
Oligodendrogliom
a
IDH-m
utated
astrocytoma
IDH-w
ildtype
astrocytoma
IDH-m
utated
glioblastoma
IDH-w
ildtype
glioblastoma
Other
Actu
al
Oligodendroglioma 10 8 1 0 7 0
IDH-mutatedastrocytoma 6 34 4 3 10 0
IDH-wildtypeastrocytoma 1 2 3 2 13 1
IDH-mutatedglioblastoma 0 1 0 0 3 0
IDH-wildtypeglioblastoma 0 3 3 1 96 0
Oligodendroglioma are IDH-mutated 1p19q co-deleted grade IIIII giomaIDH-mutated astrocytoma are IDH-mutated 1p19q intact grade IIIII gliomaIDH-wildtype astrocytoma are IDH-wildtype 1p19q intact grade IIIII gliomaIDH-mutated glioblastoma are IDH-mutated grade IV gliomaIDH-wildtype glioblastoma are IDH-wildtype grade IV glioma
23
B Segmentation examples
To demonstrate the automatic segmentations made by our method we randomlyselected five patients from both the TCGA-LGG and the TCGA-GBM datasetThe scans and segmentations of the five patients from the TCGA-LGG datasetand the TCGA-GBM dataset are shown in Figures 9 and 10 respectively TheDICE score Hausdorff distance and volumetric similarity coefficient for thesepatients are given in Table 7 The method seems to mostly focus on the hyper-intensities of the T2w-FLAIR scan Despite the registrations issues that can beseen for the T2w scan in Figure 10d the tumor was still properly segmenteddemonstrating the robustness of our method
Patient DICE HD (mm) VSC
TCGA-LGG
TCGA-DU-7301 089 103 095TCGA-FG-5964 080 58 082TCGA-FG-A713 073 78 088TCGA-HT-7475 087 149 090TCGA-HT-8106 088 112 099
TCGA-GBM
TCGA-02-0037 082 226 099TCGA-08-0353 091 130 098TCGA-12-1094 090 73 093TCGA-14-3477 090 165 099TCGA-19-5951 073 197 073
Table 7 The DICE score Hausdorff distance (HD) and volumetric similaritycoefficient (VSC) for the randomly selected patients from the TCGA-LGG andTCGA-GBM data collections
24
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-DU-7301 from the TCGA-LGG data collection
(b) Patient TCGA-FG-5964 from the TCGA-LGG data collection
(c) Patient TCGA-FG-A713 from the TCGA-LGG data collection
(d) Patient TCGA-HT-7475 from the TCGA-LGG data collection
(e) Patient TCGA-HT-8106 from the TCGA-LGG data collection
Figure 9 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-LGG data collection
25
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-02-0037 from the TCGA-GBM data collection
(b) Patient TCGA-08-0353 from the TCGA-GBM data collection
(c) Patient TCGA-12-1094 from the TCGA-GBM data collection
(d) Patient TCGA-14-3477 from the TCGA-GBM data collection
(e) Patient TCGA-19-5951 from the TCGA-GBM data collection
Figure 10 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-GBM data collection
26
C Prediction results in the test set
27
D Filter output visualizations
Figures 11 and 12 show the output of the convolution filters for the same LGGpatient as shown in Figure 5a and Figures 13 and 14 show the output of theconvolution filters for the same HGG patient as shown in Figure 5b Figures 11and 13 show the outputs of the last convolution layer in the downsample pathat the feature size of 49x61x51 (the fourth convolutional layer in the network)Figures 12 and 14 show the outputs of the last convolution layer in the upsamplepath at the feature size of 49x61x51 (the nineteenth convolutional layer in thenetwork)
Comparing Figure 11 to Figure 12 and Figure 13 to Figure 14 we can seethat the convolutional layers in the upsample path do not keep a lot of detailfor the healthy part of the brain as this region seems blurred However withinthe tumor different regions can still be distinguished The different parts ofthe tumor from the scans can also be seen such as the contrast-enhancing partand the high signal intensity on the T2w-FLAIR For the grade IV glioma inFigure 14 some filters such as filter 26 also seem to focus on the necrotic partof the tumor
28
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 11 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
29
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 12 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
30
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 13 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-06-0238 This is an IDH wildtype grade IV glioma
31
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 14 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-06-0238 This is an IDH wildtype grade IV glioma
32
E Training losses
During the training of the network we used masked categorical cross-entropy lossfor the IDH 1p19q and grade outputs The normal categorical cross-entropyloss is defined as
LCEbatch = minus 1
Nbatch
sumj
sumiisinC
yij log (yij) (1)
where LCEbatch is the total cross-entropy loss over a batch yij is the ground truth
label of sample j for class i yij is the prediction score for sample j for class iC is the set of classes and Nbatch is the number of samples in the batch Here itis assumed that the ground truth labels are one-hot-encoded thus yij is either0 or 1 for each class In our case the ground truth is not known for all sampleswhich can be incorporated in Equation (1) by setting yij to 0 for all classesfor a sample for which the ground truth is not known That sample wouldthen not contribute to the overall loss and would not contribute to the gradientupdate However this can skew the total loss over a batch since the loss is stillaveraged over the total number of samples in a batch regardless of whether theground truth is known resulting in a lower loss for batches that contained moresamples with unknown ground truth Therefore we used a masked categoricalcross-entropy loss
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
yij log (yij) (2)
where
microbatchj =
Nbatchsumij yij
sumi
yij (3)
is the batch weight for sample j In this way the total batch loss is only averagedover the samples that actually have a ground truth
Since there was an imbalance between the number of ground truth samplesfor each class we used class weights to compensate for this imbalance Thusthe loss becomes
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
microclassi yij log (yij) (4)
where
microclassi =
N
Ni |C|(5)
is the class weight for class i N is the total number of samples with knownground truth Ni is the number of samples of class i and |C| is the number ofclasses By determining the class weight in this way we ensured that
microclassi Ni =
N
|C|= constant (6)
33
Thus each class would have the same contribution to the overall loss Theseclass weights were (individually) determined for the IDH output the 1p19qoutput and the grade output
For the segmentation output we used the DICE loss
LDICEbatch =
sumj
1minus 2 middotsumvoxels
k yjk middot yjksumvoxelsk yjk + yjk
(7)
where yjk is the ground truth label in voxel k of sample j and yjk is theprediction score outputted for voxel k of sample j
The total loss that was optimized for the model was a weighted sum of thefour individual losses
Ltotal =summ
micromLm (8)
with
microm =1
Xm (9)
where Lm is the loss for output m microm is the loss weight for loss m (either theIDH 1p19q grade or segmentation loss) and Xm is the number of sampleswith known ground truth for output m In this way we could counteract theeffect of certain outputs having more known labels than other outputs
34
F Parameter tuning
Table 8 Hyperparameters that were tuned and the values that were testedValues in bold show the selected values used in the final model
Tuning parameter Tested values
Dropout rate 015 02 025 030 035 040l2-norm 00001 000001 0000001Learning rate 001 0001 00001 000001 00000001Weight decay 0001 00001 000001Augmentation factor 1 2 3Augmentation probability 025 030 035 040 045
35
G Evaluation metrics
We calculated the AUC accuracy sensitivity and specificity metrics for thegenetic and histological features for the definitions of these metrics see [67]
For the IDH and 1p19q co-deletion outputs the IDH mutated and the1p19q co-deleted samples were regarded as the positive class respectively Sincethe grade was a multi-class problem no single positive class could be determinedFor the prediction of the individual grades that grade was seen as the positiveclass and all other grades as the negative class (eg in the case of the gradeIII prediction grade III was regarded as the positive class and grade II and IVwere regarded as the negative class) For the LGG vs HGG prediction LGGwas considered as the positive class and HGG as the negative class For theevaluation of these metrics for the genetic and histological features only thesubjects with known ground truth were taken into account
The overall AUC for the grade was a multi-class AUC determined in a one-vs-one approach comparing each class against the others in this way thismetric was insensitive to class imbalance [68] A multi-class accuracy was usedto determine the overall accuracy for the grade predictions [67]
To evaluate the performance of the automated segmentation we evaluatedthe DICE score the Hausdorff distance and the volumetric similarity coefficientThe DICE score is a measure of overlap between two segmentations where avalue of 1 indicates perfect overlap and the Hausdorff distance is a measureof the closeness of the borders of the segmentations The volumetric similaritycoefficient is a measure of the agreement between the volumes of two segmen-tations without taking account the actual location of the tumor where a valueof 1 indicates perfect agreement See [65] for the definitions of these metrics
36
H Ground truth labels of patients included frompublic datasets
Acknowledgments
Sebastian van der Voort and Fatih Incekara acknowledge funding by the DutchCancer Society (KWF project number EMCR 2015-7859)
Data used in this publication were generated by the National Cancer Insti-tute Clinical Proteomic Tumor Analysis Consortium (CPTAC)
The results published here are in whole or part based upon data generatedby the TCGA Research Network httpcancergenomenihgov
Author contributions
SRvdV FI WJN MS and SK contrived the study and designed theexperiments FI MMJW JWS RNT GJL PCDWH RSEAJPEV MJvdB and MS included patients in the different studiesSRvdV FI MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV MJvdB and MS collected the dataSRvdV carried out the experiments SRvdV FI MS and SK inter-preted the results SRvdV FI MJvdB MS and SK created the initialdraft of the paper MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV WJN and MJvdB revised the paper
References
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[2] Hendrikus J Dubbink Peggy N Atmodimedjo Johan M Kros Pim JFrench Marc Sanson Ahmed Idbaih Pieter Wesseling Roelien EntingWim Spliet Cees Tijssen Winand N M Dinjens Thierry Gorlia andMartin J van den Bent Molecular classification of anaplastic oligoden-droglioma using next-generation sequencing a report of the prospectiverandomized EORTC brain tumor group 26951 phase III trial Neuro-Oncology 18(3)388ndash400 March 2016 ISSN 1522-8517 URL https
doiorg101093neuoncnov182
[3] Jeanette E Eckel-Passow Daniel H Lachance Annette M Molinaro Kyle MWalsh Paul A Decker Hugues Sicotte Melike Pekmezci Terri Rice Matt LKosel Ivan V Smirnov Gobinda Sarkar Alissa A Caron Thomas M
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Kollmeyer Corinne E Praska Anisha R Chada Chandralekha Halder He-len M Hansen Lucie S McCoy Paige M Bracci Roxanne Marshall ShichunZheng Gerald F Reis Alexander R Pico Brian P OrsquoNeill Jan C BucknerCaterina Giannini Jason T Huse Arie Perry Tarik Tihan Mitchell SBerger Susan M Chang Michael D Prados Joseph Wiemels John KWiencke Margaret R Wrensch and Robert B Jenkins Glioma groupsbased on 1p19q IDH and TERT promoter mutations in tumors NewEngland Journal of Medicine 372(26)2499ndash2508 June 2015 ISSN 0028-4793 URL httpsdoiorg101056NEJMoa1407279
[4] David N Louis Arie Perry Guido Reifenberger Andreas von Deimling Do-minique Figarella-Branger Webster K Cavenee Hiroko Ohgaki Otmar DWiestler Paul Kleihues and David W Ellison The 2016 world health orga-nization classification of tumors of the central nervous system a summaryActa Neuropathologica 131(6)803ndash820 June 2016 ISSN 0001-6322 URLhttpsdoiorg101007s00401-016-1545-1
[5] Ching-Chang Chen Peng-Wei Hsu Tai-Wei Erich Wu Shih-Tseng LeeChen-Nen Chang Kuo-chen Wei Chih-Cheng Chuang Chieh-Tsai WuTai-Ngar Lui Yung-Hsin Hsu Tzu-Kang Lin Sai-Cheung Lee and Yin-Cheng Huang Stereotactic brain biopsy Single center retrospective anal-ysis of complications Clinical Neurology and Neurosurgery 111(10)835ndash839 December 2009 ISSN 0303-8467 URL httpsdoiorg101016
jclineuro200908013
[6] Robert J Jackson Gregory N Fuller Dima Abi-Said Frederick F LangZiya L Gokaslan Wei Ming Shi David M Wildrick and Raymond SawayaLimitations of stereotactic biopsy in the initial management of gliomasNeuro-Oncology 3(3)193ndash200 July 2001 ISSN 1522-8517 URL https
doiorg101093neuonc33193
[7] M Zhou J Scott B Chaudhury L Hall D Goldgof KW Yeom M IvY Ou J Kalpathy-Cramer S Napel R Gillies O Gevaert and R GatenbyRadiomics in brain tumor Image assessment quantitative feature de-scriptors and machine-learning approaches American Journal of Neu-roradiology 39(2)208ndash216 February 2018 ISSN 0195-6108 URL https
doiorg103174ajnrA5391
[8] Wenya Linda Bi Ahmed Hosny Matthew B Schabath Maryellen LGiger Nicolai J Birkbak Alireza Mehrtash Tavis Allison Omar ArnaoutChristopher Abbosh Ian F Dunn Raymond H Mak Rulla M TamimiClare M Tempany Charles Swanton Udo Hoffmann Lawrence H SchwartzRobert J Gillies Raymond Y Huang and Hugo J W L Aerts Artificialintelligence in cancer imaging Clinical challenges and applications CAA Cancer Journal for Clinicians 69(2)caac21552 February 2019 ISSN0007-9235 URL httpsdoiorg103322caac21552
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[9] Ahmad Chaddad Michael Jonathan Kucharczyk Paul Daniel SihamSabri Bertrand J Jean-Claude Tamim Niazi and Bassam AbdulkarimRadiomics in glioblastoma Current status and challenges facing clinicalimplementation Frontiers in Oncology 9374ndash374 May 2019 ISSN 2234-943X URL httpsdoiorg103389fonc201900374
[10] Marion Smits Imaging of oligodendroglioma The British Journal of Ra-diology 89(1060)20150857 April 2016 ISSN 0007-1285 URL https
doiorg101259bjr20150857
[11] Rachel L Delfanti David E Piccioni Jason Handwerker Naeim BahramiAnithaPriya Krishnan Roshan Karunamuni Jona A Hattangadi-GluthTyler M Seibert Ashwin Srikant Karra A Jones Vivian S Snyder An-ders M Dale Nathan S White Carrie R McDonald and Nikdokht FaridImaging correlates for the 2016 update on WHO classification of gradeIIIII gliomas implications for IDH 1p19q and ATRX status Journal ofNeuro-Oncology 135(3)601ndash609 December 2017 ISSN 0167-594X URLhttpsdoiorg101007s11060-017-2613-7
[12] Sonal Gore Tanay Chougule Jayant Jagtap Jitender Saini and MadhuraIngalhalikar A review of radiomics and deep predictive modeling in gliomacharacterization Academic Radiology July 2020 ISSN 1076-6332 URLhttpsdoiorg101016jacra202006016
[13] Hugo J W L Aerts Emmanuel Rios Velazquez Ralph T H LeijenaarChintan Parmar Patrick Grossmann Sara Carvalho Johan BussinkRene Monshouwer Benjamin Haibe-Kains Derek Rietveld Frank HoebersMichelle M Rietbergen C Rene Leemans Andre Dekker John Quacken-bush Robert J Gillies and Philippe Lambin Decoding tumour pheno-type by noninvasive imaging using a quantitative radiomics approach Na-ture Communications 5(1)4006 September 2014 ISSN 2041-1723 URLhttpsdoiorg101038ncomms5006
[14] Sarah Chihati and Djamel Gaceb A review of recent progress in deeplearning-based methods for MRI brain tumor segmentation In 202011th International Conference on Information and Communication Sys-tems (ICICS) pages 149ndash154 Institute of Electrical and Electronics Engi-neers (IEEE) April 2020 ISBN 9781728162270 URL httpsdoiorg
101109icics494692020239550
[15] Robert J Gillies Paul E Kinahan and Hedvig Hricak Radiomics Imagesare more than pictures they are data Radiology 278(2)563ndash577 Febru-ary 2016 ISSN 0033-8419 URL httpsdoiorg101148radiol
2015151169
[16] James H Thrall Xiang Li Quanzheng Li Cinthia Cruz Synho Do KeithDreyer and James Brink Artificial intelligence and machine learning in ra-diology Opportunities challenges pitfalls and criteria for success Journal
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of the American College of Radiology 15(3 Part B)504ndash508 March 2018ISSN 1546-1440 URL httpsdoiorg101016jjacr201712026
[17] Ahmed Hosny Chintan Parmar John Quackenbush Lawrence H Schwartzand Hugo J W L Aerts Artificial intelligence in radiology Nature ReviewsCancer 18(8)500ndash510 August 2018 ISSN 1474-175X URL https
doiorg101038s41568-018-0016-5
[18] Okan Kopuklu Neslihan Kose Ahmet Gunduz and Gerhard Rigoll Re-source efficient 3D convolutional neural networks In 2019 IEEECVFInternational Conference on Computer Vision Workshop (ICCVW) Insti-tute of Electrical and Electronics Engineers (IEEE) October 2019 ISBN9781728150239 URL httpsdoiorg101109iccvw201900240
[19] S C Thust S Heiland A Falini H R Jager A D Waldman P C SundgrenC Godi V K Katsaros A Ramos N Bargallo M W Vernooij T YousryM Bendszus and M Smits Glioma imaging in europe A survey of 220centres and recommendations for best clinical practice European Radiology28(8)3306ndash3317 August 2018 ISSN 0938-7994 URL httpsdoiorg
101007s00330-018-5314-5
[20] Christian F Freyschlag Sandro M Krieg Johannes Kerschbaumer DanielPinggera Marie-Therese Forster Dominik Cordier Marco Rossi GabrieleMiceli Alexandre Roux Andres Reyes Silvio Sarubbo Anja Smits JoannaSierpowska Pierre A Robe Geert-Jan Rutten Thomas Santarius TomaszMatys Marc Zanello Fabien Almairac Lydiane Mondot Asgeir S JakolaMaria Zetterling Adria Rofes Gord von Campe Remy Guillevin DanieleBagatto Vincent Lubrano Marion Rapp John Goodden Philip C De WittHamer Johan Pallud Lorenzo Bello Claudius Thome Hugues Duffauand Emmanuel Mandonnet Imaging practice in low-grade gliomas amongeuropean specialized centers and proposal for a minimum core of imagingJournal of Neuro-Oncology 139(3)699ndash711 September 2018 ISSN 0167-594X URL httpsdoiorg101007s11060-018-2916-3
[21] M Labussiere A Idbaih X-W Wang Y Marie B Boisselier C FaletS Paris J Laffaire C Carpentier E Criniere F Ducray S El HallaniK Mokhtari K Hoang-Xuan J-Y Delattre and M Sanson All the 1p19qcodeleted gliomas are mutated on IDH1 or IDH2 Neurology 74(23)1886ndash1890 June 2010 ISSN 0028-3878 URL httpsdoiorg101212WNL
0b013e3181e1cf3a
[22] David N Louis Pieter Wesseling Kenneth Aldape Daniel J Brat DavidCapper Ian A Cree Charles Eberhart Dominique Figarella-BrangerMaryam Fouladi Gregory N Fuller Caterina Giannini Christine Haber-ler Cynthia Hawkins Takashi Komori Johan M Kros HK Ng Brent AOrr Sung-Hye Park Werner Paulus Arie Perry Torsten Pietsch GuidoReifenberger Marc Rosenblum Brian Rous Felix Sahm Chitra SarkarDavid A Solomon Uri Tabori Martin J Bent Andreas Deimling Michael
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Weller Valerie A White and David W Ellison cIMPACT-NOW up-date 6 new entity and diagnostic principle recommendations of thecIMPACT-Utrecht meeting on future CNS tumor classification and grad-ing Brain Pathology 30(4)844ndash856 July 2020 ISSN 1015-6305 URLhttpsdoiorg101111bpa12832
[23] Zhenyu Tang Yuyun Xu Zhicheng Jiao Junfeng Lu Lei Jin Abudumi-jiti Aibaidula Jinsong Wu Qian Wang Han Zhang and Dinggang ShenPre-operative overall survival time prediction for glioblastoma patients us-ing deep learning on both imaging phenotype and genotype In Ding-gang Shen Tianming Liu Terry M Peters Lawrence H Staib CarolineEssert Sean Zhou Pew-Thian Yap and Ali Khan editors Lecture Notesin Computer Science pages 415ndash422 Springer Science and Business Me-dia LLC 2019 ISBN 9783030322380 URL httpsdoiorg101007
978-3-030-32239-7_46
[24] Zhiyuan Xue Bowen Xin Dingqian Wang and Xiuying Wang Radiomics-enhanced multi-task neural network for non-invasive glioma subtypingand segmentation In Hassan Mohy-ud Din and Saima Rathore editorsRadiomics and Radiogenomics in Neuro-oncology pages 81ndash90 SpringerScience and Business Media LLC 2020 ISBN 9783030401238 URLhttpsdoiorg101007978-3-030-40124-5_9
[25] Milan Decuyper Stijn Bonte Karel Deblaere and Roel Van Holen Au-tomated MRI based pipeline for glioma segmentation and prediction ofgrade IDH mutation and 1p19q co-deletion 2020 Preprint at https
arxivorgabs200511965
[26] Stefania Rizzo Francesca Botta Sara Raimondi Daniela Origgi Cris-tiana Fanciullo Alessio Giuseppe Morganti and Massimo Bellomi Ra-diomics the facts and the challenges of image analysis European Ra-diology Experimental 2(1)36 December 2018 ISSN 2509-9280 URLhttpsdoiorg101186s41747-018-0068-z
[27] Philipp Lohmann Norbert Galldiks Martin Kocher Alexander HeinzelChristian P Filss Carina Stegmayr Felix M Mottaghy Gereon R FinkN Jon Shah and Karl-Josef Langen Radiomics in neuro-oncology Basicsworkflow and applications Methods June 2020 ISSN 1046-2023 URLhttpsdoiorg101016jymeth202006003
[28] Stephen S F Yip and Hugo J W L Aerts Applications and limitationsof radiomics Physics in Medicine and Biology 61(13)R150ndashR166 July2016 ISSN 0031-9155 URL httpsdoiorg1010880031-915561
13r150
[29] Annika Malmstrom Ma lgorzata Lysiak Bjarne Winther Kristensen Eliz-abeth Hovey Roger Henriksson and Peter Soderkvist Do we really knowwho has an MGMT methylated glioma Results of an international survey
41
regarding use of MGMT analyses for glioma Neuro-Oncology Practice 7(1)68ndash76 09 2019 ISSN 2054-2577 URL httpsdoiorg101093
nopnpz039
[30] Takahiro Sasaki Manabu Kinoshita Koji Fujita Junya Fukai NobuhideHayashi Yuji Uematsu Yoshiko Okita Masahiro Nonaka Shusuke Mo-riuchi Takehiro Uda Naohiro Tsuyuguchi Hideyuki Arita Kanji MoriKenichi Ishibashi Koji Takano Namiko Nishida Tomoko Shofuda EmaYoshioka Daisuke Kanematsu Yoshinori Kodama Masayuki ManoNaoyuki Nakao and Yonehiro Kanemura Radiomics and MGMT pro-moter methylation for prognostication of newly diagnosed glioblastomaScientific Reports 9(1)14435 December 2019 ISSN 2045-2322 URLhttpsdoiorg101038s41598-019-50849-y
[31] A Gupta A Prager RJ Young W Shi AMP Omuro and JJ GraberDiffusion-weighted MR imaging and MGMT methylation status in glioblas-toma A reappraisal of the role of preoperative quantitative ADC measure-ments American Journal of Neuroradiology 34(1)E10ndashE11 January 2013ISSN 0195-6108 URL httpsdoiorg103174ajnrA3467
[32] JA Carrillo A Lai PL Nghiemphu HJ Kim HS Phillips S KharbandaP Moftakhar S Lalaezari W Yong BM Ellingson TF Cloughesy andWB Pope Relationship between tumor enhancement edema IDH1 muta-tional status MGMT promoter methylation and survival in glioblastomaAmerican Journal of Neuroradiology 33(7)1349ndash1355 August 2012 ISSN0195-6108 URL httpsdoiorg103174ajnrA2950
[33] Vilde Elisabeth Mikkelsen Hong Yan Dai Anne Line Stensjoslashen Erik Mag-nus Berntsen Oslashyvind Salvesen Ole Solheim and Sverre Helge TorpMGMT promoter methylation status is not related to histological or radio-logical features in IDH wild-type glioblastomas Journal of Neuropathologyamp Experimental Neurology 79(8)855ndash862 August 2020 ISSN 0022-3069URL httpsdoiorg101093jnennlaa060
[34] Martin J van den Bent Interobserver variation of the histopathologicaldiagnosis in clinical trials on glioma a clinicianrsquos perspective Acta Neu-ropathologica 120(3)297ndash304 September 2010 ISSN 1432-0533 URLhttpsdoiorg101007s00401-010-0725-7
[35] Ji Eun Park Ho Sung Kim Youngheun Jo Roh-Eul Yoo Seung Hong ChoiSoo Jung Nam and Jeong Hoon Kim Radiomics prognostication modelin glioblastoma using diffusion- and perfusion-weighted MRI ScientificReports 10(1)4250 December 2020 ISSN 2045-2322 URL httpsdoi
org101038s41598-020-61178-w
[36] Minjae Kim So Yeong Jung Ji Eun Park Yeongheun Jo Seo YoungPark Soo Jung Nam Jeong Hoon Kim and Ho Sung Kim Diffusion-and perfusion-weighted MRI radiomics model may predict isocitrate dehy-drogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade
42
glioma European Radiology 30(4)2142ndash2151 April 2020 ISSN 0938-7994URL httpsdoiorg101007s00330-019-06548-3
[37] M Visser DMJ Muller RJM van Duijn M Smits N Verburg EJ HendriksRJA Nabuurs JCJ Bot RS Eijgelaar M Witte MB van Herk F BarkhofPC de WittHamer and JC de Munck Inter-rater agreement in glioma seg-mentations on longitudinal MRI NeuroImage Clinical 22101727 2019ISSN 2213-1582 URL httpsdoiorg101016jnicl2019101727
[38] Kenneth Clark Bruce Vendt Kirk Smith John Freymann Justin KirbyPaul Koppel Stephen Moore Stanley Phillips David Maffitt MichaelPringle Lawrence Tarbox and Fred Prior The cancer imaging archive(TCIA) Maintaining and operating a public information repository Jour-nal of Digital Imaging 26(6)1045ndash1057 December 2013 ISSN 0897-1889URL httpsdoiorg101007s10278-013-9622-7
[39] Lisa Scarpace Adam E Flanders Rajan Jain Tom Mikkelsen and David WAndrews Data from REMBRANDT The Cancer Imaging Archive 2015URL httpsdoiorg107937K9TCIA2015588OZUZB
[40] National Cancer Institute Clinical Proteomic Tumor Analysis Consortium(CPTAC) Radiology data from the clinical proteomic tumor analysisconsortium glioblastoma multiforme CPTAC-GBM collection The CancerImaging Archive 2018 URL httpsdoiorg107937k9tcia2018
3rje41q1
[41] Nameeta Shah Xu Feng Michael Lankerovich Ralph B Puchalski andBart Keogh Data from Ivy GAP The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016XLwaN6nL
[42] Ralph B Puchalski Nameeta Shah Jeremy Miller Rachel Dalley Steve RNomura Jae-Guen Yoon Kimberly A Smith Michael Lankerovich DarrenBertagnolli Kris Bickley Andrew F Boe Krissy Brouner Stephanie But-ler Shiella Caldejon Mike Chapin Suvro Datta Nick Dee Tsega DestaTim Dolbeare Nadezhda Dotson Amanda Ebbert David Feng Xu FengMichael Fisher Garrett Gee Jeff Goldy Lindsey Gourley Benjamin WGregor Guangyu Gu Nika Hejazinia John Hohmann Parvinder HothiRobert Howard Kevin Joines Ali Kriedberg Leonard Kuan Chris LauFelix Lee Hwahyung Lee Tracy Lemon Fuhui Long Naveed Mastan ErikaMott Chantal Murthy Kiet Ngo Eric Olson Melissa Reding Zack RileyDavid Rosen David Sandman Nadiya Shapovalova Clifford R Slaughter-beck Andrew Sodt Graham Stockdale Aaron Szafer Wayne WakemanPaul E Wohnoutka Steven J White Don Marsh Robert C RostomilyLydia Ng Chinh Dang Allan Jones Bart Keogh Haley R GittlemanJill S Barnholtz-Sloan Patrick J Cimino Megha S Uppin C Dirk KeeneFarrokh R Farrokhi Justin D Lathia Michael E Berens Antonio IavaroneAmy Bernard Ed Lein John W Phillips Steven W Rostad Charles Cobbs
43
Michael J Hawrylycz and Greg D Foltz An anatomic transcriptional at-las of human glioblastoma Science 360(6389)660ndash663 May 2018 ISSN0036-8075 URL httpsdoiorg101126scienceaaf2666
[43] Kathleen Schmainda and Melissa Prah Data from Brain-Tumor-Progression The Cancer Imaging Archive 2018 URL httpsdoiorg
107937K9TCIA201815quzvnb
[44] B H Menze A Jakab S Bauer J Kalpathy-Cramer K FarahaniJ Kirby Y Burren N Porz J Slotboom R Wiest L Lanczi E GerstnerM Weber T Arbel B B Avants N Ayache P Buendia D L CollinsN Cordier J J Corso A Criminisi T Das H Delingette C Demi-ralp C R Durst M Dojat S Doyle J Festa F Forbes E GeremiaB Glocker P Golland X Guo A Hamamci K M IftekharuddinR Jena N M John E Konukoglu D Lashkari J A Mariz R MeierS Pereira D Precup S J Price T R Raviv S M S Reza M RyanD Sarikaya L Schwartz H Shin J Shotton C A Silva N SousaN K Subbanna G Szekely T J Taylor O M Thomas N J Tusti-son G Unal F Vasseur M Wintermark D H Ye L Zhao B ZhaoD Zikic M Prastawa M Reyes and K Van Leemput The mul-timodal brain tumor image segmentation benchmark (BRATS) IEEETransactions on Medical Imaging 34(10)1993ndash2024 2015 URL https
doiorg101109TMI20142377694
[45] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin S Kirby John B Freymann Keyvan Farahani and ChristosDavatzikos Advancing the cancer genome atlas glioma MRI collectionswith expert segmentation labels and radiomic features Scientific Data 4(1)170117 December 2017 ISSN 2052-4463 URL httpsdoiorg10
1038sdata2017117
[46] Spyridon Bakas Mauricio Reyes Andras Jakab Stefan Bauer MarkusRempfler Alessandro Crimi Russell Takeshi Shinohara Christoph BergerSung Min Ha Martin Rozycki Marcel Prastawa Esther Alberts JanaLipkova John Freymann Justin Kirby Michel Bilello Hassan Fathallah-Shaykh Roland Wiest Jan Kirschke Benedikt Wiestler Rivka ColenAikaterini Kotrotsou Pamela Lamontagne Daniel Marcus MikhailMilchenko Arash Nazeri Marc-Andre Weber Abhishek Mahajan UjjwalBaid Elizabeth Gerstner Dongjin Kwon Gagan Acharya Manu Agar-wal Mahbubul Alam Alberto Albiol Antonio Albiol Francisco J AlbiolVarghese Alex Nigel Allinson Pedro H A Amorim Abhijit AmrutkarGanesh Anand Simon Andermatt Tal Arbel Pablo Arbelaez Aaron Av-ery Muneeza Azmat Pranjal B W Bai Subhashis Banerjee Bill BarthThomas Batchelder Kayhan Batmanghelich Enzo Battistella AndrewBeers Mikhail Belyaev Martin Bendszus Eze Benson Jose Bernal Ha-landur Nagaraja Bharath George Biros Sotirios Bisdas James BrownMariano Cabezas Shilei Cao Jorge M Cardoso Eric N Carver Adria
44
Casamitjana Laura Silvana Castillo Marcel Cata Philippe Cattin Al-bert Cerigues Vinicius S Chagas Siddhartha Chandra Yi-Ju ChangShiyu Chang Ken Chang Joseph Chazalon Shengcong Chen Wei ChenJefferson W Chen Zhaolin Chen Kun Cheng Ahana Roy ChoudhuryRoger Chylla Albert Clerigues Steven Colleman Ramiro German Ro-driguez Colmeiro Marc Combalia Anthony Costa Xiaomeng Cui Zhen-zhen Dai Lutao Dai Laura Alexandra Daza Eric Deutsch ChangxingDing Chao Dong Shidu Dong Wojciech Dudzik Zach Eaton-Rosen GaryEgan Guilherme Escudero Theo Estienne Richard Everson JonathanFabrizio Yong Fan Longwei Fang Xue Feng Enzo Ferrante Lucas Fi-don Martin Fischer Andrew P French Naomi Fridman Huan Fu DavidFuentes Yaozong Gao Evan Gates David Gering Amir Gholami WilliGierke Ben Glocker Mingming Gong Sandra Gonzalez-Villa T Gros-ges Yuanfang Guan Sheng Guo Sudeep Gupta Woo-Sup Han Il SongHan Konstantin Harmuth Huiguang He Aura Hernandez-Sabate EvelynHerrmann Naveen Himthani Winston Hsu Cheyu Hsu Xiaojun Hu Xi-aobin Hu Yan Hu Yifan Hu Rui Hua Teng-Yi Huang Weilin HuangSabine Van Huffel Quan Huo Vivek HV Khan M Iftekharuddin FabianIsensee Mobarakol Islam Aaron S Jackson Sachin R Jambawalikar An-drew Jesson Weijian Jian Peter Jin V Jeya Maria Jose Alain JungoB Kainz Konstantinos Kamnitsas Po-Yu Kao Ayush Karnawat ThomasKellermeier Adel Kermi Kurt Keutzer Mohamed Tarek Khadir Mahen-dra Khened Philipp Kickingereder Geena Kim Nik King Haley KnappUrspeter Knecht Lisa Kohli Deren Kong Xiangmao Kong Simon Kop-pers Avinash Kori Ganapathy Krishnamurthi Egor Krivov Piyush Ku-mar Kaisar Kushibar Dmitrii Lachinov Tryphon Lambrou Joon LeeChengen Lee Yuehchou Lee M Lee Szidonia Lefkovits Laszlo LefkovitsJames Levitt Tengfei Li Hongwei Li Wenqi Li Hongyang Li XiaochuanLi Yuexiang Li Heng Li Zhenye Li Xiaoyu Li Zeju Li XiaoGang LiWenqi Li Zheng-Shen Lin Fengming Lin Pietro Lio Chang Liu Bo-qiang Liu Xiang Liu Mingyuan Liu Ju Liu Luyan Liu Xavier LladoMarc Moreno Lopez Pablo Ribalta Lorenzo Zhentai Lu Lin Luo ZhigangLuo Jun Ma Kai Ma Thomas Mackie Anant Madabushi Issam Mah-moudi Klaus H Maier-Hein Pradipta Maji CP Mammen Andreas MangB S Manjunath Michal Marcinkiewicz S McDonagh Stephen McKennaRichard McKinley Miriam Mehl Sachin Mehta Raghav Mehta RaphaelMeier Christoph Meinel Dorit Merhof Craig Meyer Robert Miller Sush-mita Mitra Aliasgar Moiyadi David Molina-Garcia Miguel A B Mon-teiro Grzegorz Mrukwa Andriy Myronenko Jakub Nalepa Thuyen NgoDong Nie Holly Ning Chen Niu Nicholas K Nuechterlein Eric Oer-mann Arlindo Oliveira Diego D C Oliveira Arnau Oliver AlexanderF I Osman Yu-Nian Ou Sebastien Ourselin Nikos Paragios Moo SungPark Brad Paschke J Gregory Pauloski Kamlesh Pawar Nick PawlowskiLinmin Pei Suting Peng Silvio M Pereira Julian Perez-Beteta Vic-tor M Perez-Garcia Simon Pezold Bao Pham Ashish Phophalia GemmaPiella G N Pillai Marie Piraud Maxim Pisov Anmol Popli Michael P
45
Pound Reza Pourreza Prateek Prasanna Vesna Prkovska Tony P Prid-more Santi Puch Elodie Puybareau Buyue Qian Xu Qiao MartinRajchl Swapnil Rane Michael Rebsamen Hongliang Ren Xuhua RenKarthik Revanuru Mina Rezaei Oliver Rippel Luis Carlos Rivera Char-lotte Robert Bruce Rosen Daniel Rueckert Mohammed Safwan MostafaSalem Joaquim Salvi Irina Sanchez Irina Sanchez Heitor M Santos Em-mett Sartor Dawid Schellingerhout Klaudius Scheufele Matthew R ScottArtur A Scussel Sara Sedlar Juan Pablo Serrano-Rubio N Jon ShahNameetha Shah Mazhar Shaikh B Uma Shankar Zeina Shboul HaipengShen Dinggang Shen Linlin Shen Haocheng Shen Varun Shenoy FengShi Hyung Eun Shin Hai Shu Diana Sima M Sinclair Orjan SmedbyJames M Snyder Mohammadreza Soltaninejad Guidong Song MehulSoni Jean Stawiaski Shashank Subramanian Li Sun Roger Sun JiaweiSun Kay Sun Yu Sun Guoxia Sun Shuang Sun Yannick R Suter Las-zlo Szilagyi Sanjay Talbar Dacheng Tao Dacheng Tao Zhongzhao TengSiddhesh Thakur Meenakshi H Thakur Sameer Tharakan Pallavi Ti-wari Guillaume Tochon Tuan Tran Yuhsiang M Tsai Kuan-Lun TsengTran Anh Tuan Vadim Turlapov Nicholas Tustison Maria VakalopoulouSergi Valverde Rami Vanguri Evgeny Vasiliev Jonathan Ventura LuisVera Tom Vercauteren C A Verrastro Lasitha Vidyaratne Veronica Vi-laplana Ajeet Vivekanandan Guotai Wang Qian Wang Chiatse J WangWeichung Wang Duo Wang Ruixuan Wang Yuanyuan Wang ChunliangWang Guotai Wang Ning Wen Xin Wen Leon Weninger Wolfgang WickShaocheng Wu Qiang Wu Yihong Wu Yong Xia Yanwu Xu XiaowenXu Peiyuan Xu Tsai-Ling Yang Xiaoping Yang Hao-Yu Yang JunlinYang Haojin Yang Guang Yang Hongdou Yao Xujiong Ye ChangchangYin Brett Young-Moxon Jinhua Yu Xiangyu Yue Songtao Zhang AngelaZhang Kun Zhang Xuejie Zhang Lichi Zhang Xiaoyue Zhang YazhuoZhang Lei Zhang Jianguo Zhang Xiang Zhang Tianhao Zhang SichengZhao Yu Zhao Xiaomei Zhao Liang Zhao Yefeng Zheng Liming ZhongChenhong Zhou Xiaobing Zhou Fan Zhou Hongtu Zhu Jin Zhu YingZhuge Weiwei Zong Jayashree Kalpathy-Cramer Keyvan Farahani Chris-tos Davatzikos Koen van Leemput and Bjoern Menze Identifying the bestmachine learning algorithms for brain tumor segmentation progression as-sessment and overall survival prediction in the BRATS challenge 2018Preprint at httpsarxivorgabs181102629
[47] Nancy Pedano Adam E Flanders Lisa Scarpace Tom Mikkelsen Jen-nifer M Eschbacher Beth Hermes Victor Sisneros Jill Barnholtz-Sloanand Quinn Ostrom Radiology data from the cancer genome atlas lowgrade glioma [TCGA-LGG] collection The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016L4LTD3TK
[48] Lisa Scarpace Tom Mikkelsen Soonmee Cha Sujaya Rao SangeetaTekchandani David Gutman Joel H Saltz Bradley J Erickson NancyPedano Adam E Flanders Jill Barnholtz-Sloan Quinn Ostrom DanielBarboriak and Laura J Pierce Radiology data from the cancer genome
46
atlas glioblastoma multiforme [TCGA-GBM] collection The Cancer Imag-ing Archive 2016 URL httpsdoiorg107937K9TCIA2016
RNYFUYE9
[49] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin Kirby John Freymann Keyvan Farahani and ChristosDavatzikos Segmentation labels and radiomic features for the pre-operativescans of the TCGA-LGG collection [data set] The Cancer Imaging Archive2017 URL httpsdoiorg107937K9TCIA2017GJQ7R0EF
[50] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello Mar-tin Rozycki Justin Kirby John Freymann Keyvan Farahani and Chris-tos Davatzikos Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [data set] The CancerImaging Archive 2017 URL httpsdoiorg107937K9TCIA2017
KLXWJJ1Q
[51] Xiangrui Li Paul S Morgan John Ashburner Jolinda Smith and Christo-pher Rorden The first step for neuroimaging data analysis DICOM toNIfTI conversion Journal of Neuroscience Methods 26447ndash56 May 2016ISSN 0165-0270 URL httpsdoiorg101016jjneumeth201603
001
[52] Vladimir Fonov Alan C Evans Kelly Botteron C Robert Almli Robert CMcKinstry and D Louis Collins Unbiased average age-appropriate at-lases for pediatric studies NeuroImage 54(1)313ndash327 January 2011ISSN 1053-8119 URL httpsdoiorg101016jneuroimage2010
07033
[53] VS Fonov AC Evans RC McKinstry CR Almli and DL Collins Unbiasednonlinear average age-appropriate brain templates from birth to adulthoodNeuroImage 47S102 July 2009 ISSN 1053-8119 URL httpsdoi
org101016S1053-8119(09)70884-5 Organization for Human BrainMapping 2009 Annual Meeting
[54] S Klein M Staring K Murphy MA Viergever and J Pluim elastix Atoolbox for intensity-based medical image registration IEEE Transactionson Medical Imaging 29(1)196ndash205 January 2010 ISSN 0278-0062 URLhttpsdoiorg101109TMI20092035616
[55] Denis Shamonin Fast parallel image registration on CPU and GPU fordiagnostic classification of alzheimerrsquos disease Frontiers in Neuroinfor-matics 750 2013 ISSN 1662-5196 URL httpsdoiorg103389
fninf201300050
[56] Bradley C Lowekamp David T Chen Luis Ibanez and Daniel Blezek Thedesign of SimpleITK Frontiers in Neuroinformatics 745 2013 ISSN1662-5196 URL httpsdoiorg103389fninf201300045
47
[57] Fabian Isensee Marianne Schell Irada Pflueger Gianluca Brugnara DavidBonekamp Ulf Neuberger Antje Wick Heinz-Peter Schlemmer SabineHeiland Wolfgang Wick Martin Bendszus Klaus H Maier-Hein andPhilipp Kickingereder Automated brain extraction of multisequence MRIusing artificial neural networks Human Brain Mapping 40(17)4952ndash4964December 2019 ISSN 1065-9471 URL httpsdoiorg101002hbm
24750
[58] Olaf Ronneberger Invited talk U-net convolutional networks for biomed-ical image segmentation In geb Fritzsche K Maier-Hein geb Lehmann TDeserno Heinz Handels and Thomas Tolxdorff editors Bildverarbeitungfur die Medizin 2017 Informatik aktuell pages 3ndash3 Springer Scienceand Business Media LLC 2017 ISBN 9783662543443 URL https
doiorg101007978-3-662-54345-0_3
[59] Martın Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy DavisJeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving MichaelIsard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry MooreDerek G Murray Benoit Steiner Paul Tucker Vijay Vasudevan PeteWarden Martin Wicke Yuan Yu and Xiaoqiang Zheng Tensor-Flow A system for large-scale machine learning In 12th USENIXSymposium on Operating Systems Design and Implementation (OSDI16) pages 265ndash283 USENIX Association November 2016 ISBN978-1-931971-33-1 URL httpswwwusenixorgconferenceosdi16
technical-sessionspresentationabadi
[60] Dipankar Das Naveen Mellempudi Dheevatsa Mudigere Dhiraj KalamkarSasikanth Avancha Kunal Banerjee Srinivas Sridharan KarthikVaidyanathan Bharat Kaul Evangelos Georganas Alexander HeineckePradeep Dubey Jesus Corbal Nikita Shustrov Roma Dubtsov EvaristFomenko and Vadim Pirogov Mixed precision training of convolutionalneural networks using integer operations In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=H135uzZ0-
[61] Samuel L Smith Pieter-Jan Kindermans and Quoc V Le Donrsquot decaythe learning rate increase the batch size In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=B1Yy1BxCZ
[62] Cliff Woolley NCCL accelarated multi-GPU collective communicationsURL httpsimagesnvidiacomeventssc15pdfsNCCL-Woolley
pdf Accessed on 2020-09-30
[63] Ilya Loshchilov and Frank Hutter Decoupled weight decay regularization2017 Preprint at httpsarxivorgabs171105101
48
[64] F Pedregosa G Varoquaux A Gramfort V Michel B Thirion O GriselM Blondel P Prettenhofer R Weiss V Dubourg J Vanderplas A Pas-sos D Cournapeau M Brucher M Perrot and E Duchesnay Scikit-learn Machine learning in python Journal of Machine Learning Re-search 122825ndash2830 2011 URL httpswwwjmlrorgpapersv12
pedregosa11ahtml
[65] Abdel Aziz Taha and Allan Hanbury Metrics for evaluating 3d medicalimage segmentation analysis selection and tool BMC Medical Imaging15(1)29 August 2015 ISSN 1471-2342 URL httpsdoiorg101186
s12880-015-0068-x
[66] Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viegas and MartinWattenberg SmoothGrad removing noise by adding noise 2017 Preprintat httpsarxivorgabs170603825
[67] Alaa Tharwat Classification assessment methods Applied Computing andInformatics 2018 ISSN 2210-8327 URL httpsdoiorg101016j
aci201808003
[68] David J Hand and Robert J Till A simple generalisation of the areaunder the ROC curve for multiple class classification problems MachineLearning 45(2)171ndash186 November 2001 ISSN 1573-0565 URL https
doiorg101023A1010920819831
49
Data Collection | Patient | IDH_mutated | 1p19q_codeleted | Grade | |||||
BTumorP | PGBM-001 | -1 | -1 | -1 | |||||
BTumorP | PGBM-002 | -1 | -1 | -1 | |||||
BTumorP | PGBM-003 | -1 | -1 | -1 | |||||
BTumorP | PGBM-004 | -1 | -1 | -1 | |||||
BTumorP | PGBM-005 | -1 | -1 | -1 | |||||
BTumorP | PGBM-006 | -1 | -1 | -1 | |||||
BTumorP | PGBM-007 | -1 | -1 | -1 | |||||
BTumorP | PGBM-008 | -1 | -1 | -1 | |||||
BTumorP | PGBM-009 | -1 | -1 | -1 | |||||
BTumorP | PGBM-010 | -1 | -1 | -1 | |||||
BTumorP | PGBM-011 | -1 | -1 | -1 | |||||
BTumorP | PGBM-012 | -1 | -1 | -1 | |||||
BTumorP | PGBM-013 | -1 | -1 | -1 | |||||
BTumorP | PGBM-014 | -1 | -1 | -1 | |||||
BTumorP | PGBM-015 | -1 | -1 | -1 | |||||
BTumorP | PGBM-016 | -1 | -1 | -1 | |||||
BTumorP | PGBM-017 | -1 | -1 | -1 | |||||
BTumorP | PGBM-018 | -1 | -1 | -1 | |||||
BTumorP | PGBM-019 | -1 | -1 | -1 | |||||
BTumorP | PGBM-020 | -1 | -1 | -1 | |||||
BraTS | 2013_0 | -1 | -1 | -1 | |||||
BraTS | 2013_10 | -1 | -1 | -1 | |||||
BraTS | 2013_11 | -1 | -1 | -1 | |||||
BraTS | 2013_12 | -1 | -1 | -1 | |||||
BraTS | 2013_13 | -1 | -1 | -1 | |||||
BraTS | 2013_14 | -1 | -1 | -1 | |||||
BraTS | 2013_15 | -1 | -1 | -1 | |||||
BraTS | 2013_16 | -1 | -1 | -1 | |||||
BraTS | 2013_17 | -1 | -1 | -1 | |||||
BraTS | 2013_18 | -1 | -1 | -1 | |||||
BraTS | 2013_19 | -1 | -1 | -1 | |||||
BraTS | 2013_1 | -1 | -1 | -1 | |||||
BraTS | 2013_20 | -1 | -1 | -1 | |||||
BraTS | 2013_21 | -1 | -1 | -1 | |||||
BraTS | 2013_22 | -1 | -1 | -1 | |||||
BraTS | 2013_23 | -1 | -1 | -1 | |||||
BraTS | 2013_24 | -1 | -1 | -1 | |||||
BraTS | 2013_25 | -1 | -1 | -1 | |||||
BraTS | 2013_26 | -1 | -1 | -1 | |||||
BraTS | 2013_27 | -1 | -1 | -1 | |||||
BraTS | 2013_28 | -1 | -1 | -1 | |||||
BraTS | 2013_29 | -1 | -1 | -1 | |||||
BraTS | 2013_2 | -1 | -1 | -1 | |||||
BraTS | 2013_3 | -1 | -1 | -1 | |||||
BraTS | 2013_4 | -1 | -1 | -1 | |||||
BraTS | 2013_5 | -1 | -1 | -1 | |||||
BraTS | 2013_6 | -1 | -1 | -1 | |||||
BraTS | 2013_7 | -1 | -1 | -1 | |||||
BraTS | 2013_8 | -1 | -1 | -1 | |||||
BraTS | 2013_9 | -1 | -1 | -1 | |||||
BraTS | CBICA_AAB | -1 | -1 | -1 | |||||
BraTS | CBICA_AAG | -1 | -1 | -1 | |||||
BraTS | CBICA_AAL | -1 | -1 | -1 | |||||
BraTS | CBICA_AAP | -1 | -1 | -1 | |||||
BraTS | CBICA_ABB | -1 | -1 | -1 | |||||
BraTS | CBICA_ABE | -1 | -1 | -1 | |||||
BraTS | CBICA_ABM | -1 | -1 | -1 | |||||
BraTS | CBICA_ABN | -1 | -1 | -1 | |||||
BraTS | CBICA_ABO | -1 | -1 | -1 | |||||
BraTS | CBICA_ABY | -1 | -1 | -1 | |||||
BraTS | CBICA_ALN | -1 | -1 | -1 | |||||
BraTS | CBICA_ALU | -1 | -1 | -1 | |||||
BraTS | CBICA_ALX | -1 | -1 | -1 | |||||
BraTS | CBICA_AME | -1 | -1 | -1 | |||||
BraTS | CBICA_AMH | -1 | -1 | -1 | |||||
BraTS | CBICA_ANG | -1 | -1 | -1 | |||||
BraTS | CBICA_ANI | -1 | -1 | -1 | |||||
BraTS | CBICA_ANP | -1 | -1 | -1 | |||||
BraTS | CBICA_ANV | -1 | -1 | -1 | |||||
BraTS | CBICA_ANZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AOC | -1 | -1 | -1 | |||||
BraTS | CBICA_AOD | -1 | -1 | -1 | |||||
BraTS | CBICA_AOH | -1 | -1 | -1 | |||||
BraTS | CBICA_AOO | -1 | -1 | -1 | |||||
BraTS | CBICA_AOP | -1 | -1 | -1 | |||||
BraTS | CBICA_AOS | -1 | -1 | -1 | |||||
BraTS | CBICA_AOZ | -1 | -1 | -1 | |||||
BraTS | CBICA_APK | -1 | -1 | -1 | |||||
BraTS | CBICA_APR | -1 | -1 | -1 | |||||
BraTS | CBICA_APY | -1 | -1 | -1 | |||||
BraTS | CBICA_APZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQA | -1 | -1 | -1 | |||||
BraTS | CBICA_AQD | -1 | -1 | -1 | |||||
BraTS | CBICA_AQG | -1 | -1 | -1 | |||||
BraTS | CBICA_AQJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQN | -1 | -1 | -1 | |||||
BraTS | CBICA_AQO | -1 | -1 | -1 | |||||
BraTS | CBICA_AQP | -1 | -1 | -1 | |||||
BraTS | CBICA_AQQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQR | -1 | -1 | -1 | |||||
BraTS | CBICA_AQT | -1 | -1 | -1 | |||||
BraTS | CBICA_AQU | -1 | -1 | -1 | |||||
BraTS | CBICA_AQV | -1 | -1 | -1 | |||||
BraTS | CBICA_AQY | -1 | -1 | -1 | |||||
BraTS | CBICA_AQZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ARF | -1 | -1 | -1 | |||||
BraTS | CBICA_ARW | -1 | -1 | -1 | |||||
BraTS | CBICA_ARZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ASA | -1 | -1 | -1 | |||||
BraTS | CBICA_ASE | -1 | -1 | -1 | |||||
BraTS | CBICA_ASF | -1 | -1 | -1 | |||||
BraTS | CBICA_ASG | -1 | -1 | -1 | |||||
BraTS | CBICA_ASH | -1 | -1 | -1 | |||||
BraTS | CBICA_ASK | -1 | -1 | -1 | |||||
BraTS | CBICA_ASN | -1 | -1 | -1 | |||||
BraTS | CBICA_ASO | -1 | -1 | -1 | |||||
BraTS | CBICA_ASR | -1 | -1 | -1 | |||||
BraTS | CBICA_ASU | -1 | -1 | -1 | |||||
BraTS | CBICA_ASV | -1 | -1 | -1 | |||||
BraTS | CBICA_ASW | -1 | -1 | -1 | |||||
BraTS | CBICA_ASY | -1 | -1 | -1 | |||||
BraTS | CBICA_ATB | -1 | -1 | -1 | |||||
BraTS | CBICA_ATD | -1 | -1 | -1 | |||||
BraTS | CBICA_ATF | -1 | -1 | -1 | |||||
BraTS | CBICA_ATN | -1 | -1 | -1 | |||||
BraTS | CBICA_ATP | -1 | -1 | -1 | |||||
BraTS | CBICA_ATV | -1 | -1 | -1 | |||||
BraTS | CBICA_ATX | -1 | -1 | -1 | |||||
BraTS | CBICA_AUA | -1 | -1 | -1 | |||||
BraTS | CBICA_AUN | -1 | -1 | -1 | |||||
BraTS | CBICA_AUQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AUR | -1 | -1 | -1 | |||||
BraTS | CBICA_AUW | -1 | -1 | -1 | |||||
BraTS | CBICA_AUX | -1 | -1 | -1 | |||||
BraTS | CBICA_AVB | -1 | -1 | -1 | |||||
BraTS | CBICA_AVF | -1 | -1 | -1 | |||||
BraTS | CBICA_AVG | -1 | -1 | -1 | |||||
BraTS | CBICA_AVJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AVT | -1 | -1 | -1 | |||||
BraTS | CBICA_AVV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWG | -1 | -1 | -1 | |||||
BraTS | CBICA_AWH | -1 | -1 | -1 | |||||
BraTS | CBICA_AWI | -1 | -1 | -1 | |||||
BraTS | CBICA_AWV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWX | -1 | -1 | -1 | |||||
BraTS | CBICA_AXJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXL | -1 | -1 | -1 | |||||
BraTS | CBICA_AXM | -1 | -1 | -1 | |||||
BraTS | CBICA_AXN | -1 | -1 | -1 | |||||
BraTS | CBICA_AXO | -1 | -1 | -1 | |||||
BraTS | CBICA_AXQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXW | -1 | -1 | -1 | |||||
BraTS | CBICA_AYA | -1 | -1 | -1 | |||||
BraTS | CBICA_AYC | -1 | -1 | -1 | |||||
BraTS | CBICA_AYG | -1 | -1 | -1 | |||||
BraTS | CBICA_AYI | -1 | -1 | -1 | |||||
BraTS | CBICA_AYU | -1 | -1 | -1 | |||||
BraTS | CBICA_AYW | -1 | -1 | -1 | |||||
BraTS | CBICA_AZD | -1 | -1 | -1 | |||||
BraTS | CBICA_AZH | -1 | -1 | -1 | |||||
BraTS | CBICA_BAN | -1 | -1 | -1 | |||||
BraTS | CBICA_BAP | -1 | -1 | -1 | |||||
BraTS | CBICA_BAX | -1 | -1 | -1 | |||||
BraTS | CBICA_BBG | -1 | -1 | -1 | |||||
BraTS | CBICA_BCF | -1 | -1 | -1 | |||||
BraTS | CBICA_BCL | -1 | -1 | -1 | |||||
BraTS | CBICA_BDK | -1 | -1 | -1 | |||||
BraTS | CBICA_BEM | -1 | -1 | -1 | |||||
BraTS | CBICA_BFB | -1 | -1 | -1 | |||||
BraTS | CBICA_BFP | -1 | -1 | -1 | |||||
BraTS | CBICA_BGE | -1 | -1 | -1 | |||||
BraTS | CBICA_BGG | -1 | -1 | -1 | |||||
BraTS | CBICA_BGN | -1 | -1 | -1 | |||||
BraTS | CBICA_BGO | -1 | -1 | -1 | |||||
BraTS | CBICA_BGR | -1 | -1 | -1 | |||||
BraTS | CBICA_BGT | -1 | -1 | -1 | |||||
BraTS | CBICA_BGW | -1 | -1 | -1 | |||||
BraTS | CBICA_BGX | -1 | -1 | -1 | |||||
BraTS | CBICA_BHB | -1 | -1 | -1 | |||||
BraTS | CBICA_BHK | -1 | -1 | -1 | |||||
BraTS | CBICA_BHM | -1 | -1 | -1 | |||||
BraTS | CBICA_BHQ | -1 | -1 | -1 | |||||
BraTS | CBICA_BHV | -1 | -1 | -1 | |||||
BraTS | CBICA_BHZ | -1 | -1 | -1 | |||||
BraTS | CBICA_BIC | -1 | -1 | -1 | |||||
BraTS | CBICA_BJY | -1 | -1 | -1 | |||||
BraTS | CBICA_BKV | -1 | -1 | -1 | |||||
BraTS | CBICA_BLJ | -1 | -1 | -1 | |||||
BraTS | CBICA_BNR | -1 | -1 | -1 | |||||
BraTS | TMC_6290 | -1 | -1 | -1 | |||||
BraTS | TMC_6643 | -1 | -1 | -1 | |||||
BraTS | TMC_9043 | -1 | -1 | -1 | |||||
BraTS | TMC_11964 | -1 | -1 | -1 | |||||
BraTS | TMC_12866 | -1 | -1 | -1 | |||||
BraTS | TMC_15477 | -1 | -1 | -1 | |||||
BraTS | TMC_21360 | -1 | -1 | -1 | |||||
BraTS | TMC_27374 | -1 | -1 | -1 | |||||
BraTS | TMC_30014 | -1 | -1 | -1 | |||||
CPTAC-GBM | C3L-00016 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00019 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00265 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00278 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00349 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00424 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00429 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00506 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00528 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00591 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00631 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00636 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00671 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00674 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00677 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01045 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01046 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01142 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01156 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01327 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02041 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02465 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02504 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02704 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02706 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02707 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02708 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03260 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03266 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03727 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03728 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03747 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03748 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-04084 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00661 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00662 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00663 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00665 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01192 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01196 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01505 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01849 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01851 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01852 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02255 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02256 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02286 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03001 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03003 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03755 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-04686 | -1 | -1 | 4 | |||||
IvyGAP | W10 | 1 | 1 | 4 | |||||
IvyGAP | W11 | 0 | 0 | 4 | |||||
IvyGAP | W12 | 0 | 0 | 4 | |||||
IvyGAP | W13 | 0 | 0 | 4 | |||||
IvyGAP | W16 | 0 | 0 | 4 | |||||
IvyGAP | W18 | 0 | 0 | 4 | |||||
IvyGAP | W19 | 0 | 0 | 4 | |||||
IvyGAP | W1 | 0 | 0 | 4 | |||||
IvyGAP | W20 | 0 | 0 | 4 | |||||
IvyGAP | W21 | 0 | 0 | 4 | |||||
IvyGAP | W22 | 0 | 0 | -1 | |||||
IvyGAP | W26 | 0 | -1 | 4 | |||||
IvyGAP | W29 | 0 | 0 | 4 | |||||
IvyGAP | W2 | 0 | 1 | 4 | |||||
IvyGAP | W30 | 0 | 0 | 4 | |||||
IvyGAP | W31 | 1 | 1 | 4 | |||||
IvyGAP | W32 | 0 | 0 | 4 | |||||
IvyGAP | W33 | 0 | 0 | 4 | |||||
IvyGAP | W34 | 0 | 0 | 4 | |||||
IvyGAP | W35 | 1 | 0 | 3 | |||||
IvyGAP | W36 | 0 | 0 | 4 | |||||
IvyGAP | W38 | 0 | 0 | 4 | |||||
IvyGAP | W39 | 0 | 0 | 4 | |||||
IvyGAP | W3 | 1 | 0 | 4 | |||||
IvyGAP | W40 | 0 | 0 | 4 | |||||
IvyGAP | W42 | 0 | -1 | 4 | |||||
IvyGAP | W43 | 0 | -1 | 4 | |||||
IvyGAP | W45 | -1 | -1 | 4 | |||||
IvyGAP | W48 | 0 | -1 | 4 | |||||
IvyGAP | W4 | 1 | 0 | 4 | |||||
IvyGAP | W50 | 0 | -1 | 3 | |||||
IvyGAP | W53 | 1 | -1 | 4 | |||||
IvyGAP | W54 | 0 | -1 | 4 | |||||
IvyGAP | W55 | 0 | -1 | 4 | |||||
IvyGAP | W5 | 0 | 0 | 4 | |||||
IvyGAP | W6 | 0 | 0 | 4 | |||||
IvyGAP | W7 | 0 | 0 | 4 | |||||
IvyGAP | W8 | 0 | 0 | 4 | |||||
IvyGAP | W9 | 0 | 0 | 4 | |||||
REMBRANDT | 900-00-5299 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5303 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5308 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5316 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5317 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5332 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5339 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5341 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5342 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5346 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5380 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5381 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5382 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5385 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5396 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5404 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5412 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5414 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5458 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5459 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5462 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5468 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5476 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5477 | -1 | -1 | 2 | |||||
REMBRANDT | HF0763 | -1 | -1 | -1 | |||||
REMBRANDT | HF0828 | -1 | -1 | 3 | |||||
REMBRANDT | HF0835 | -1 | -1 | 2 | |||||
REMBRANDT | HF0855 | -1 | -1 | 2 | |||||
REMBRANDT | HF0868 | -1 | -1 | -1 | |||||
REMBRANDT | HF0883 | -1 | -1 | -1 | |||||
REMBRANDT | HF0899 | -1 | -1 | 2 | |||||
REMBRANDT | HF0920 | -1 | -1 | 2 | |||||
REMBRANDT | HF0931 | -1 | -1 | 2 | |||||
REMBRANDT | HF0953 | -1 | -1 | 2 | |||||
REMBRANDT | HF0960 | -1 | -1 | 2 | |||||
REMBRANDT | HF0966 | -1 | -1 | 3 | |||||
REMBRANDT | HF0986 | -1 | -1 | 4 | |||||
REMBRANDT | HF0990 | -1 | -1 | 4 | |||||
REMBRANDT | HF1000 | -1 | -1 | 2 | |||||
REMBRANDT | HF1058 | -1 | -1 | 4 | |||||
REMBRANDT | HF1059 | -1 | -1 | 3 | |||||
REMBRANDT | HF1071 | -1 | -1 | 4 | |||||
REMBRANDT | HF1077 | -1 | -1 | 4 | |||||
REMBRANDT | HF1078 | -1 | -1 | 4 | |||||
REMBRANDT | HF1097 | -1 | -1 | 4 | |||||
REMBRANDT | HF1113 | -1 | -1 | -1 | |||||
REMBRANDT | HF1122 | -1 | -1 | 4 | |||||
REMBRANDT | HF1136 | -1 | -1 | 3 | |||||
REMBRANDT | HF1139 | -1 | -1 | 4 | |||||
REMBRANDT | HF1150 | -1 | -1 | 3 | |||||
REMBRANDT | HF1156 | -1 | -1 | 2 | |||||
REMBRANDT | HF1167 | -1 | -1 | 2 | |||||
REMBRANDT | HF1185 | -1 | -1 | 3 | |||||
REMBRANDT | HF1191 | -1 | -1 | 4 | |||||
REMBRANDT | HF1199 | -1 | -1 | -1 | |||||
REMBRANDT | HF1219 | -1 | -1 | 3 | |||||
REMBRANDT | HF1227 | -1 | -1 | 2 | |||||
REMBRANDT | HF1232 | -1 | -1 | 3 | |||||
REMBRANDT | HF1235 | -1 | -1 | 2 | |||||
REMBRANDT | HF1242 | -1 | -1 | 3 | |||||
REMBRANDT | HF1246 | -1 | -1 | 2 | |||||
REMBRANDT | HF1264 | -1 | -1 | 2 | |||||
REMBRANDT | HF1269 | -1 | -1 | 4 | |||||
REMBRANDT | HF1280 | -1 | -1 | 3 | |||||
REMBRANDT | HF1292 | -1 | -1 | 4 | |||||
REMBRANDT | HF1293 | -1 | -1 | -1 | |||||
REMBRANDT | HF1297 | -1 | -1 | 4 | |||||
REMBRANDT | HF1300 | -1 | -1 | -1 | |||||
REMBRANDT | HF1307 | -1 | -1 | -1 | |||||
REMBRANDT | HF1316 | -1 | -1 | 2 | |||||
REMBRANDT | HF1318 | -1 | -1 | -1 | |||||
REMBRANDT | HF1325 | -1 | -1 | 2 | |||||
REMBRANDT | HF1331 | -1 | -1 | -1 | |||||
REMBRANDT | HF1334 | -1 | -1 | 2 | |||||
REMBRANDT | HF1344 | -1 | -1 | 2 | |||||
REMBRANDT | HF1345 | -1 | -1 | 2 | |||||
REMBRANDT | HF1357 | -1 | -1 | 3 | |||||
REMBRANDT | HF1381 | -1 | -1 | 2 | |||||
REMBRANDT | HF1397 | -1 | -1 | 4 | |||||
REMBRANDT | HF1398 | -1 | -1 | 3 | |||||
REMBRANDT | HF1407 | -1 | -1 | 2 | |||||
REMBRANDT | HF1409 | -1 | -1 | 3 | |||||
REMBRANDT | HF1420 | -1 | -1 | -1 | |||||
REMBRANDT | HF1429 | -1 | -1 | -1 | |||||
REMBRANDT | HF1433 | -1 | -1 | 2 | |||||
REMBRANDT | HF1437 | -1 | -1 | -1 | |||||
REMBRANDT | HF1442 | -1 | -1 | 2 | |||||
REMBRANDT | HF1458 | -1 | -1 | 3 | |||||
REMBRANDT | HF1463 | -1 | -1 | 2 | |||||
REMBRANDT | HF1489 | -1 | -1 | 2 | |||||
REMBRANDT | HF1490 | -1 | -1 | 3 | |||||
REMBRANDT | HF1493 | -1 | -1 | -1 | |||||
REMBRANDT | HF1510 | -1 | -1 | -1 | |||||
REMBRANDT | HF1511 | -1 | -1 | 2 | |||||
REMBRANDT | HF1517 | -1 | -1 | 4 | |||||
REMBRANDT | HF1538 | -1 | -1 | 4 | |||||
REMBRANDT | HF1551 | -1 | -1 | 2 | |||||
REMBRANDT | HF1553 | -1 | -1 | 2 | |||||
REMBRANDT | HF1560 | -1 | -1 | 4 | |||||
REMBRANDT | HF1568 | -1 | -1 | 2 | |||||
REMBRANDT | HF1587 | -1 | -1 | 3 | |||||
REMBRANDT | HF1588 | -1 | -1 | 2 | |||||
REMBRANDT | HF1606 | -1 | -1 | 2 | |||||
REMBRANDT | HF1613 | -1 | -1 | 3 | |||||
REMBRANDT | HF1628 | -1 | -1 | 4 | |||||
REMBRANDT | HF1652 | -1 | -1 | -1 | |||||
REMBRANDT | HF1677 | -1 | -1 | 2 | |||||
REMBRANDT | HF1702 | -1 | -1 | 3 | |||||
REMBRANDT | HF1708 | -1 | -1 | 2 | |||||
TCGA-GBM | TCGA-02-0003 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0006 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0009 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0011 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0027 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0033 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0034 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0037 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0046 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0047 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0048 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0054 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0059 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0060 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0064 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0068 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0069 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0070 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0075 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0085 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0086 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0087 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0102 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0106 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0116 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0119 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0122 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0128 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0130 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0132 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0133 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0137 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0138 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0139 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0142 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0145 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0149 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0154 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0158 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0162 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0164 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0166 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0168 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0175 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0176 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0177 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0179 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0182 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0184 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0185 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0187 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0188 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0189 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0190 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0192 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0213 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0238 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0240 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0241 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0644 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0646 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0648 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0649 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1084 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1802 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-2570 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5408 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5412 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5413 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5417 | 1 | -1 | 4 | |||||
TCGA-GBM | TCGA-06-6389 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0350 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0352 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0353 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0354 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0355 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0356 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0357 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0358 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0359 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0360 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0385 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0389 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0392 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0512 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0520 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0521 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0522 | -1 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0524 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0529 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0616 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0776 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1093 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1094 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1098 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1598 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1601 | 0 | -1 | -1 | |||||
TCGA-GBM | TCGA-12-1602 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-3650 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-0789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1456 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1794 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1825 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-3477 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-0963 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2624 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2631 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5951 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5954 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5958 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5960 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1834 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1838 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-2526 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4932 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-76-4934 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4935 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6191 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6193 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6280 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6282 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6285 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6656 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6657 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6661 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6662 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6663 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6664 | 0 | 0 | 4 | |||||
TCGA-LGG | TCGA-CS-4941 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4942 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4944 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5393 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-5395 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5396 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-CS-5397 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6186 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6188 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6290 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6665 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6666 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6667 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-6668 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-CS-6669 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5849 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-5851 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5852 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5854 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5871 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5874 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6397 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6399 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6400 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6404 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6405 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6407 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6410 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6542 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7008 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7010 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7014 | -1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7015 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7018 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7019 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7294 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-7298 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7300 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7302 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7309 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8162 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8164 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-8165 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8167 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8168 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-EZ-7265A | -1 | -1 | -1 | |||||
TCGA-LGG | TCGA-FG-5964 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-6688 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6691 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6692 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-7643 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-A713 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7473 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7616 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7680 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7684 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7686 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7692 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7693 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7694 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7856 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7860 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7874 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7879 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7882 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8105 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-8106 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8107 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8111 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8114 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8563 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A614 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0 | 2 |
Data_collection | Patient | IDH_mutated | Prediction_score_IDH_wildtype | Prediction_score_IDH_mutated | 1p19q_codeleted | Prediction_score_1p19q_codeleted | Prediction_score_1p19q_intact | Grade | Prediction_score_grade_2 | Prediction_score_grade_3 | Prediction_score_grade_4 | ||||||||||||
TCGA-GBM | TCGA-02-0003 | 0 | 099998915 | 10867886E-05 | 0 | 099996686 | 3308471E-05 | 4 | 7377526E-05 | 000074111245 | 099918514 | ||||||||||||
TCGA-GBM | TCGA-02-0006 | 0 | 042321962 | 05767803 | 0 | 068791837 | 031208166 | 4 | 060229343 | 026596427 | 013174225 | ||||||||||||
TCGA-GBM | TCGA-02-0009 | 0 | 099306935 | 0006930672 | 0 | 09906961 | 0009303949 | 4 | 0056565534 | 010282235 | 08406121 | ||||||||||||
TCGA-GBM | TCGA-02-0011 | 0 | 013531776 | 08646823 | 0 | 085318035 | 01468197 | 4 | 0015055533 | 092510724 | 005983725 | ||||||||||||
TCGA-GBM | TCGA-02-0027 | 0 | 09997279 | 000027212297 | 0 | 09986827 | 00013172914 | 4 | 00016104137 | 00038575265 | 0994532 | ||||||||||||
TCGA-GBM | TCGA-02-0033 | 0 | 099974436 | 000025564007 | 0 | 099940693 | 0000593021 | 4 | 00020670628 | 0003761288 | 09941717 | ||||||||||||
TCGA-GBM | TCGA-02-0034 | 0 | 091404164 | 008595832 | 0 | 089209336 | 01079066 | 4 | 00116944825 | 0061110377 | 092719513 | ||||||||||||
TCGA-GBM | TCGA-02-0037 | 0 | 09999577 | 42315594E-05 | 0 | 099992716 | 72827526E-05 | 4 | 82080274E-05 | 0009249337 | 09906686 | ||||||||||||
TCGA-GBM | TCGA-02-0046 | 0 | 0999129 | 00008710656 | 0 | 09989637 | 00010362669 | 4 | 0004290756 | 0022799779 | 097290945 | ||||||||||||
TCGA-GBM | TCGA-02-0047 | 0 | 099991703 | 83008505E-05 | 0 | 09999292 | 70863265E-05 | 4 | 000016252015 | 0040118434 | 095971906 | ||||||||||||
TCGA-GBM | TCGA-02-0048 | 0 | 09998785 | 000012148175 | 0 | 099959475 | 000040527192 | 4 | 00002215901 | 000039696065 | 09993814 | ||||||||||||
TCGA-GBM | TCGA-02-0054 | 0 | 09999831 | 1689829E-05 | 0 | 09999442 | 5583975E-05 | 4 | 00010063206 | 0060579527 | 093841416 | ||||||||||||
TCGA-GBM | TCGA-02-0059 | -1 | 09993749 | 000062511285 | 0 | 09996424 | 00003576683 | 4 | 00007046657 | 0010920537 | 09883748 | ||||||||||||
TCGA-GBM | TCGA-02-0060 | 0 | 07197039 | 028029615 | 0 | 09016612 | 009833879 | 4 | 017739706 | 03728545 | 04497484 | ||||||||||||
TCGA-GBM | TCGA-02-0064 | 0 | 09999083 | 9170197E-05 | 0 | 09995073 | 000049264234 | 4 | 000043781495 | 00028024286 | 099675983 | ||||||||||||
TCGA-GBM | TCGA-02-0068 | 0 | 099187535 | 0008124709 | 0 | 099528164 | 00047183693 | 4 | 00030539853 | 059695286 | 039999318 | ||||||||||||
TCGA-GBM | TCGA-02-0069 | 0 | 09890871 | 0010912909 | 0 | 099704784 | 0002952148 | 4 | 00057067247 | 0061368063 | 09329252 | ||||||||||||
TCGA-GBM | TCGA-02-0070 | 0 | 09940659 | 00059340666 | 0 | 0957794 | 0042206 | 4 | 0008216515 | 003556913 | 09562143 | ||||||||||||
TCGA-GBM | TCGA-02-0075 | 0 | 099933076 | 00006693099 | 0 | 099735296 | 00026470982 | 4 | 000044697264 | 00035736929 | 09959793 | ||||||||||||
TCGA-GBM | TCGA-02-0085 | 0 | 099114406 | 0008855922 | 0 | 09756698 | 002433019 | 4 | 00065203947 | 0035171553 | 095830804 | ||||||||||||
TCGA-GBM | TCGA-02-0086 | 0 | 099965334 | 000034666777 | 0 | 0998698 | 00013019645 | 4 | 000032699382 | 00018025768 | 099787045 | ||||||||||||
TCGA-GBM | TCGA-02-0087 | -1 | 09974885 | 00025114634 | 0 | 09990638 | 000093628286 | 4 | 0007505083 | 0008562708 | 098393226 | ||||||||||||
TCGA-GBM | TCGA-02-0102 | 0 | 09797647 | 0020235319 | 0 | 098292196 | 0017078074 | 4 | 003512482 | 03901857 | 05746895 | ||||||||||||
TCGA-GBM | TCGA-02-0106 | -1 | 099993694 | 6302759E-05 | 0 | 099980897 | 000019110431 | 4 | 60797247E-05 | 00008735659 | 09990657 | ||||||||||||
TCGA-GBM | TCGA-02-0116 | 0 | 09999778 | 22125667E-05 | 0 | 09996886 | 00003113695 | 4 | 000015498884 | 000051770627 | 09993273 | ||||||||||||
TCGA-GBM | TCGA-06-0119 | 0 | 09999362 | 63770494E-05 | 0 | 09999355 | 6452215E-05 | 4 | 000013225728 | 00028902534 | 099697745 | ||||||||||||
TCGA-GBM | TCGA-06-0122 | 0 | 09915298 | 0008470196 | 0 | 09859093 | 00140907345 | 4 | 00121390615 | 027333176 | 071452916 | ||||||||||||
TCGA-GBM | TCGA-06-0128 | 1 | 099988174 | 000011820537 | 0 | 099980634 | 000019373452 | 4 | 000016409029 | 0007865882 | 099197 | ||||||||||||
TCGA-GBM | TCGA-06-0130 | 0 | 099998784 | 12123987E-05 | 0 | 09999323 | 6775062E-05 | 4 | 80872844E-05 | 00026260202 | 099729306 | ||||||||||||
TCGA-GBM | TCGA-06-0132 | 0 | 09998566 | 000014341719 | 0 | 099988496 | 000011501736 | 4 | 000072843547 | 0005115947 | 099415565 | ||||||||||||
TCGA-GBM | TCGA-06-0133 | 0 | 097782 | 002218004 | 0 | 0993807 | 00061929906 | 4 | 0026753133 | 004659919 | 09266477 | ||||||||||||
TCGA-GBM | TCGA-06-0137 | 0 | 096448904 | 003551094 | 0 | 099403125 | 0005968731 | 4 | 000649511 | 038909483 | 060441005 | ||||||||||||
TCGA-GBM | TCGA-06-0138 | 0 | 09977743 | 00022256707 | 0 | 099736834 | 0002631674 | 4 | 00032954598 | 0011606657 | 09850979 | ||||||||||||
TCGA-GBM | TCGA-06-0139 | 0 | 09992649 | 00007350447 | 0 | 099898964 | 00010103129 | 4 | 00021781863 | 00069256434 | 099089617 | ||||||||||||
TCGA-GBM | TCGA-06-0142 | 0 | 099909425 | 00009057334 | 0 | 09985896 | 00014103584 | 4 | 0002598974 | 0046451908 | 09509491 | ||||||||||||
TCGA-GBM | TCGA-06-0145 | 0 | 099964654 | 000035350278 | 0 | 0999652 | 000034802416 | 4 | 00009068022 | 0021991275 | 0977102 | ||||||||||||
TCGA-GBM | TCGA-06-0149 | -1 | 09992161 | 00007839425 | 0 | 09981067 | 00018932257 | 4 | 00057726577 | 0013888515 | 09803388 | ||||||||||||
TCGA-GBM | TCGA-06-0154 | 0 | 099968064 | 000031937403 | 0 | 0999729 | 000027106237 | 4 | 000041507537 | 023430935 | 07652756 | ||||||||||||
TCGA-GBM | TCGA-06-0158 | 0 | 09999199 | 8014118E-05 | 0 | 099992514 | 74846226E-05 | 4 | 00026547876 | 020762624 | 0789719 | ||||||||||||
TCGA-GBM | TCGA-06-0162 | -1 | 099964297 | 00003569706 | 0 | 09997459 | 0000254147 | 4 | 000033955855 | 004318936 | 09564711 | ||||||||||||
TCGA-GBM | TCGA-06-0164 | -1 | 09983991 | 00016009645 | 0 | 09873262 | 0012673735 | 4 | 00016517473 | 00048346478 | 09935136 | ||||||||||||
TCGA-GBM | TCGA-06-0166 | 0 | 099991715 | 82846556E-05 | 0 | 0999554 | 00004459562 | 4 | 000013499439 | 0011635037 | 098823 | ||||||||||||
TCGA-GBM | TCGA-06-0168 | 0 | 09975561 | 00024438864 | 0 | 09964825 | 00035174883 | 4 | 0004766434 | 010448053 | 089075303 | ||||||||||||
TCGA-GBM | TCGA-06-0175 | -1 | 09996252 | 000037482675 | 0 | 09988098 | 00011902251 | 4 | 00026097735 | 004992068 | 094746953 | ||||||||||||
TCGA-GBM | TCGA-06-0176 | 0 | 099550986 | 00044901576 | 0 | 09998872 | 000011279297 | 4 | 0032868527 | 036690876 | 06002227 | ||||||||||||
TCGA-GBM | TCGA-06-0177 | -1 | 081774735 | 018225263 | 0 | 09946464 | 00053536464 | 4 | 0026683953 | 013013016 | 08431859 | ||||||||||||
TCGA-GBM | TCGA-06-0179 | -1 | 09997508 | 000024923254 | 0 | 09989778 | 00010222099 | 4 | 0002628482 | 0004127114 | 099324447 | ||||||||||||
TCGA-GBM | TCGA-06-0182 | -1 | 099999547 | 45838406E-06 | 0 | 099998736 | 12656287E-05 | 4 | 00002591103 | 000018499703 | 09995559 | ||||||||||||
TCGA-GBM | TCGA-06-0184 | 0 | 09935369 | 00064631375 | 0 | 099458355 | 00054164114 | 4 | 0023110552 | 0017436244 | 09594532 | ||||||||||||
TCGA-GBM | TCGA-06-0185 | 0 | 09999337 | 66310655E-05 | 0 | 099986255 | 000013738607 | 4 | 7657532E-05 | 0016089642 | 098383385 | ||||||||||||
TCGA-GBM | TCGA-06-0187 | 0 | 09991689 | 00008312097 | 0 | 099700147 | 00029984985 | 4 | 00020616595 | 0033111423 | 096482694 | ||||||||||||
TCGA-GBM | TCGA-06-0188 | 0 | 09883802 | 0011619771 | 0 | 09826743 | 0017325714 | 4 | 0013776424 | 0112841725 | 087338185 | ||||||||||||
TCGA-GBM | TCGA-06-0189 | 0 | 099906737 | 0000932636 | 0 | 09983865 | 00016135005 | 4 | 00022760795 | 00106745735 | 09870494 | ||||||||||||
TCGA-GBM | TCGA-06-0190 | 0 | 099954176 | 000045831292 | 0 | 09967013 | 00032986512 | 4 | 000040555766 | 0001246768 | 099834764 | ||||||||||||
TCGA-GBM | TCGA-06-0192 | 0 | 09997876 | 00002123566 | 0 | 09992735 | 00007264875 | 4 | 00004505576 | 00014473333 | 09981021 | ||||||||||||
TCGA-GBM | TCGA-06-0213 | 0 | 099986935 | 00001305845 | 0 | 099971646 | 000028351307 | 4 | 8755587E-05 | 00013480412 | 09985644 | ||||||||||||
TCGA-GBM | TCGA-06-0238 | 0 | 09999982 | 17603431E-06 | 0 | 09999894 | 10616134E-05 | 4 | 8076515E-05 | 56053756E-05 | 099986315 | ||||||||||||
TCGA-GBM | TCGA-06-0240 | 0 | 09989956 | 00010044163 | 0 | 099948466 | 00005152657 | 4 | 00016040986 | 021931975 | 077907616 | ||||||||||||
TCGA-GBM | TCGA-06-0241 | 0 | 099959785 | 000040211933 | 0 | 099910825 | 00008917038 | 4 | 00023411359 | 0007850656 | 098980826 | ||||||||||||
TCGA-GBM | TCGA-06-0644 | 0 | 09871044 | 0012895588 | 0 | 09859228 | 00140771745 | 4 | 0013671214 | 009819665 | 088813215 | ||||||||||||
TCGA-GBM | TCGA-06-0646 | 0 | 099959 | 00004100472 | 0 | 099936503 | 000063495064 | 4 | 00019223108 | 0040443853 | 095763385 | ||||||||||||
TCGA-GBM | TCGA-06-0648 | 0 | 09999709 | 29083441E-05 | 0 | 099982435 | 000017571273 | 4 | 000077678583 | 000038868992 | 099883455 | ||||||||||||
TCGA-GBM | TCGA-06-0649 | 0 | 09997805 | 000021952427 | 0 | 099951684 | 000048311835 | 4 | 0042641632 | 00058432207 | 095151514 | ||||||||||||
TCGA-GBM | TCGA-06-1084 | 0 | 099985826 | 000014174655 | 0 | 099968565 | 00003144242 | 4 | 00002676724 | 020492287 | 079480946 | ||||||||||||
TCGA-GBM | TCGA-06-1802 | -1 | 09991928 | 00008072305 | 0 | 09956176 | 0004382337 | 4 | 000043478087 | 00019495043 | 09976157 | ||||||||||||
TCGA-GBM | TCGA-06-2570 | 1 | 096841115 | 0031588882 | 0 | 09842457 | 0015754245 | 4 | 0015369608 | 0030956635 | 09536738 | ||||||||||||
TCGA-GBM | TCGA-06-5408 | 0 | 099857306 | 00014269598 | 0 | 09962638 | 00037362208 | 4 | 00027690146 | 0016195394 | 098103565 | ||||||||||||
TCGA-GBM | TCGA-06-5412 | 0 | 099366105 | 0006338921 | 0 | 099193794 | 0008061992 | 4 | 0011476759 | 006606435 | 09224589 | ||||||||||||
TCGA-GBM | TCGA-06-5413 | 0 | 09994105 | 000058955856 | 0 | 09983026 | 00016974095 | 4 | 00027100197 | 0021083053 | 097620696 | ||||||||||||
TCGA-GBM | TCGA-06-5417 | 1 | 01521267 | 08478733 | -1 | 03064492 | 06935508 | 4 | 013736826 | 037757674 | 048505494 | ||||||||||||
TCGA-GBM | TCGA-06-6389 | 1 | 099987435 | 000012558252 | 0 | 09997017 | 000029827762 | 4 | 00014020519 | 00020044278 | 099659353 | ||||||||||||
TCGA-GBM | TCGA-08-0350 | 0 | 019229275 | 08077072 | 0 | 0033211168 | 09667888 | 4 | 0051619414 | 022280572 | 072557485 | ||||||||||||
TCGA-GBM | TCGA-08-0352 | 0 | 099997497 | 25071595E-05 | 0 | 099992514 | 74846226E-05 | 4 | 000024192198 | 000048111935 | 099927694 | ||||||||||||
TCGA-GBM | TCGA-08-0353 | 0 | 09901496 | 0009850325 | 0 | 09967775 | 0003222484 | 4 | 00053748637 | 0004291497 | 09903336 | ||||||||||||
TCGA-GBM | TCGA-08-0354 | 0 | 076413894 | 023586108 | 0 | 07554566 | 024454337 | 4 | 008784444 | 02004897 | 071166587 | ||||||||||||
TCGA-GBM | TCGA-08-0355 | 0 | 09998349 | 000016506859 | 0 | 099984336 | 000015659066 | 4 | 000076689845 | 0023648744 | 09755844 | ||||||||||||
TCGA-GBM | TCGA-08-0356 | 0 | 097673583 | 0023264103 | 0 | 097773504 | 0022264915 | 4 | 001175834 | 0031075679 | 095716596 | ||||||||||||
TCGA-GBM | TCGA-08-0357 | 0 | 099509466 | 0004905406 | 0 | 099300176 | 00069982093 | 4 | 0005191745 | 0038681854 | 095612645 | ||||||||||||
TCGA-GBM | TCGA-08-0358 | 0 | 099999785 | 2199356E-06 | 0 | 099999034 | 9628425E-06 | 4 | 6113315E-06 | 00011283219 | 09988656 | ||||||||||||
TCGA-GBM | TCGA-08-0359 | 0 | 097885466 | 0021145396 | 0 | 09956006 | 00043994132 | 4 | 0009885523 | 0066605434 | 092350906 | ||||||||||||
TCGA-GBM | TCGA-08-0360 | 0 | 09922444 | 00077555366 | -1 | 09948704 | 00051296344 | 4 | 0013318472 | 003317344 | 095350814 | ||||||||||||
TCGA-GBM | TCGA-08-0385 | 0 | 099605453 | 00039454065 | -1 | 099686414 | 0003135836 | 4 | 00050293226 | 0029977333 | 096499336 | ||||||||||||
TCGA-GBM | TCGA-08-0389 | 0 | 099964714 | 000035281325 | 0 | 09991272 | 000087276706 | 4 | 00017554013 | 00024730961 | 099577147 | ||||||||||||
TCGA-GBM | TCGA-08-0390 | 0 | 099945146 | 000054847915 | 0 | 099936 | 00006399274 | 4 | 00036811908 | 00050958768 | 0991223 | ||||||||||||
TCGA-GBM | TCGA-08-0392 | 0 | 099962366 | 000037629317 | 0 | 09993575 | 00006424303 | 4 | 000036593352 | 0010291994 | 09893421 | ||||||||||||
TCGA-GBM | TCGA-08-0512 | -1 | 09982893 | 00017106998 | 0 | 099193794 | 0008061992 | 4 | 00016200381 | 00027773918 | 09956026 | ||||||||||||
TCGA-GBM | TCGA-08-0520 | -1 | 099603915 | 00039607873 | 0 | 09981933 | 00018066854 | 4 | 00007140295 | 0019064669 | 09802213 | ||||||||||||
TCGA-GBM | TCGA-08-0521 | -1 | 09975274 | 0002472623 | 0 | 099490017 | 00050998176 | 4 | 0001514669 | 0020103427 | 09783819 | ||||||||||||
TCGA-GBM | TCGA-08-0522 | -1 | 099960107 | 000039899128 | -1 | 09992053 | 00007947255 | 4 | 0000269389 | 0006173321 | 09935573 | ||||||||||||
TCGA-GBM | TCGA-08-0524 | -1 | 09964619 | 0003538086 | 0 | 099620515 | 0003794834 | 4 | 000019140428 | 0010096702 | 09897119 | ||||||||||||
TCGA-GBM | TCGA-08-0529 | -1 | 09996567 | 000034329997 | 0 | 099952066 | 00004793605 | 4 | 000032077235 | 0035970636 | 09637086 | ||||||||||||
TCGA-GBM | TCGA-12-0616 | 0 | 098521465 | 0014785408 | 0 | 098704207 | 001295789 | 4 | 001592791 | 012875569 | 08553164 | ||||||||||||
TCGA-GBM | TCGA-12-0776 | -1 | 099899167 | 00010083434 | 0 | 09987031 | 00012968953 | 4 | 0019219175 | 00637484 | 09170324 | ||||||||||||
TCGA-GBM | TCGA-12-0829 | 0 | 099913067 | 00008693674 | 0 | 099821776 | 00017821962 | 4 | 00021031094 | 0055067167 | 09428297 | ||||||||||||
TCGA-GBM | TCGA-12-1093 | 0 | 099992585 | 7411892E-05 | 0 | 09999448 | 5518923E-05 | 4 | 000046803855 | 0012115157 | 098741674 | ||||||||||||
TCGA-GBM | TCGA-12-1094 | -1 | 09980045 | 00019955388 | 0 | 09866105 | 0013389497 | 4 | 00053194338 | 001599471 | 097868586 | ||||||||||||
TCGA-GBM | TCGA-12-1098 | -1 | 09998406 | 000015936712 | 0 | 09977216 | 00022783307 | 4 | 000010218692 | 0035607774 | 09642901 | ||||||||||||
TCGA-GBM | TCGA-12-1598 | 0 | 096309197 | 0036908068 | 0 | 097933435 | 0020665688 | 4 | 0012952217 | 052912676 | 045792103 | ||||||||||||
TCGA-GBM | TCGA-12-1601 | 0 | 09875683 | 0012431651 | -1 | 0991891 | 0008108984 | -1 | 00118053425 | 0105477065 | 088271755 | ||||||||||||
TCGA-GBM | TCGA-12-1602 | 0 | 099830914 | 00016908031 | 0 | 099858415 | 00014158705 | 4 | 0008427611 | 0025996923 | 09655755 | ||||||||||||
TCGA-GBM | TCGA-12-3650 | 0 | 09761519 | 0023848088 | 0 | 097467697 | 0025323058 | 4 | 0010450666 | 043705726 | 05524921 | ||||||||||||
TCGA-GBM | TCGA-14-0789 | 0 | 099856466 | 00014353332 | 0 | 099666256 | 00033374047 | 4 | 0001406897 | 0008273975 | 099031913 | ||||||||||||
TCGA-GBM | TCGA-14-1456 | 1 | 006299064 | 093700933 | 0 | 08656222 | 013437784 | 4 | 016490369 | 047177824 | 036331803 | ||||||||||||
TCGA-GBM | TCGA-14-1794 | 0 | 08579393 | 014206071 | 0 | 09850429 | 0014957087 | 4 | 0023009384 | 009868736 | 08783033 | ||||||||||||
TCGA-GBM | TCGA-14-1825 | 0 | 099960107 | 000039899128 | 0 | 099968123 | 00003187511 | 4 | 0008552247 | 0010156045 | 09812918 | ||||||||||||
TCGA-GBM | TCGA-14-1829 | 0 | 090690076 | 009309922 | 0 | 09907856 | 0009214366 | 4 | 0008461936 | 0102735735 | 088880235 | ||||||||||||
TCGA-GBM | TCGA-14-3477 | 0 | 099796116 | 0002038787 | 0 | 09990728 | 00009271923 | 4 | 00032272525 | 0021644868 | 09751279 | ||||||||||||
TCGA-GBM | TCGA-19-0963 | -1 | 099876726 | 00012327607 | 0 | 09983612 | 00016388679 | 4 | 00031698826 | 013153598 | 086529416 | ||||||||||||
TCGA-GBM | TCGA-19-1390 | 0 | 099913234 | 00008676725 | 0 | 099703634 | 00029636684 | 4 | 00015592943 | 0026028048 | 097241265 | ||||||||||||
TCGA-GBM | TCGA-19-1789 | 0 | 09809491 | 00190509 | 0 | 09915216 | 0008478402 | 4 | 0038703684 | 014341596 | 08178804 | ||||||||||||
TCGA-GBM | TCGA-19-2624 | 0 | 07535573 | 024644265 | 0 | 09816127 | 0018387254 | 4 | 012311598 | 012769651 | 07491875 | ||||||||||||
TCGA-GBM | TCGA-19-2631 | 0 | 099860877 | 0001391234 | 0 | 09981178 | 00018821858 | 4 | 00009839778 | 001843531 | 09805807 | ||||||||||||
TCGA-GBM | TCGA-19-5951 | 0 | 09999031 | 9685608E-05 | 0 | 099977034 | 000022960825 | 4 | 00020246736 | 0004014765 | 09939606 | ||||||||||||
TCGA-GBM | TCGA-19-5954 | 0 | 099456257 | 00054374957 | 0 | 09968273 | 00031726828 | 4 | 00073725334 | 006310084 | 09295266 | ||||||||||||
TCGA-GBM | TCGA-19-5958 | 0 | 099999475 | 5234907E-06 | 0 | 0999941 | 58978338E-05 | 4 | 35422294E-05 | 86819025E-05 | 09998777 | ||||||||||||
TCGA-GBM | TCGA-19-5960 | 0 | 09683962 | 003160382 | 0 | 09013577 | 009864227 | 4 | 0011394806 | 018114014 | 08074651 | ||||||||||||
TCGA-GBM | TCGA-27-1834 | 0 | 099998164 | 18342893E-05 | 0 | 09999685 | 31446623E-05 | 4 | 8611921E-05 | 000031686216 | 0999597 | ||||||||||||
TCGA-GBM | TCGA-27-1838 | 0 | 09993625 | 000063743413 | 0 | 09940428 | 0005957154 | 4 | 00006736379 | 0007191195 | 099213517 | ||||||||||||
TCGA-GBM | TCGA-27-2526 | 0 | 099983776 | 000016219281 | 0 | 09996898 | 000031015594 | 4 | 000016658282 | 00006323714 | 09992011 | ||||||||||||
TCGA-GBM | TCGA-76-4932 | 0 | 09867389 | 0013261103 | -1 | 09949397 | 0005060332 | 4 | 00007321126 | 0003016794 | 099625117 | ||||||||||||
TCGA-GBM | TCGA-76-4934 | 0 | 099318933 | 0006810731 | 0 | 09995073 | 000049264234 | 4 | 00061555947 | 00070025027 | 098684186 | ||||||||||||
TCGA-GBM | TCGA-76-4935 | 0 | 074562997 | 025437003 | 0 | 098242307 | 001757688 | 4 | 076535034 | 006437644 | 017027317 | ||||||||||||
TCGA-GBM | TCGA-76-6191 | 0 | 09981067 | 00018932257 | 0 | 09970879 | 00029121784 | 4 | 00044340584 | 00096095055 | 098595643 | ||||||||||||
TCGA-GBM | TCGA-76-6193 | 0 | 09966168 | 0003383191 | 0 | 099850464 | 00014953383 | 4 | 00037061477 | 007873953 | 09175543 | ||||||||||||
TCGA-GBM | TCGA-76-6280 | 0 | 099948776 | 00005122569 | 0 | 099908185 | 000091819017 | 4 | 00001475792 | 000846075 | 099139166 | ||||||||||||
TCGA-GBM | TCGA-76-6282 | 0 | 0995906 | 0004093958 | 0 | 099861956 | 00013804223 | 4 | 00006694951 | 0009437619 | 09898929 | ||||||||||||
TCGA-GBM | TCGA-76-6285 | 0 | 099949074 | 00005092657 | 0 | 09971661 | 00028338495 | 4 | 00031175872 | 004005614 | 095682627 | ||||||||||||
TCGA-GBM | TCGA-76-6656 | 0 | 09996917 | 000030834455 | 0 | 09983897 | 00016103574 | 4 | 002648366 | 00017969633 | 09717193 | ||||||||||||
TCGA-GBM | TCGA-76-6657 | 0 | 099987245 | 000012755992 | 0 | 099951494 | 00004850083 | 4 | 000096620515 | 0005599633 | 09934342 | ||||||||||||
TCGA-GBM | TCGA-76-6661 | 0 | 093211424 | 006788577 | 0 | 09640178 | 0035982177 | 4 | 003490037 | 0026863772 | 09382358 | ||||||||||||
TCGA-GBM | TCGA-76-6662 | 0 | 096425414 | 0035745807 | 0 | 09963924 | 0003607617 | 4 | 002845819 | 002544755 | 09460942 | ||||||||||||
TCGA-GBM | TCGA-76-6663 | 0 | 088664144 | 0113358565 | 0 | 09984207 | 00015792594 | 4 | 0010206689 | 043740335 | 05523899 | ||||||||||||
TCGA-GBM | TCGA-76-6664 | 0 | 011047115 | 08895289 | 0 | 09559813 | 004401865 | 4 | 00049677677 | 08806894 | 011434281 | ||||||||||||
TCGA-LGG | TCGA-CS-4941 | 0 | 088931274 | 011068726 | 0 | 087037706 | 012962292 | 3 | 002865127 | 0048591908 | 092275685 | ||||||||||||
TCGA-LGG | TCGA-CS-4942 | 1 | 00031327847 | 099686724 | 0 | 096309197 | 0036908068 | 3 | 096261597 | 00148612335 | 0022522787 | ||||||||||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0005265965 | 099473405 | 0 | 09940544 | 00059455987 | 3 | 09439103 | 0023049146 | 003304057 | ||||||||||||
TCGA-LGG | TCGA-CS-4944 | 1 | 009363656 | 09063635 | 0 | 08755211 | 0124478824 | 2 | 034047556 | 033881712 | 03207073 | ||||||||||||
TCGA-LGG | TCGA-CS-5393 | 1 | 009623762 | 09037624 | 0 | 098178816 | 001821182 | 3 | 014111634 | 042021698 | 043866673 | ||||||||||||
TCGA-LGG | TCGA-CS-5395 | 0 | 08502822 | 014971776 | 0 | 09932025 | 00067975316 | 2 | 0052374925 | 018397054 | 076365453 | ||||||||||||
TCGA-LGG | TCGA-CS-5396 | 1 | 099839586 | 00016040892 | 1 | 099967945 | 000032062363 | 3 | 00016345463 | 029090768 | 07074577 | ||||||||||||
TCGA-LGG | TCGA-CS-5397 | 0 | 049304244 | 050695753 | 0 | 08829839 | 0117016025 | 3 | 038702008 | 021211159 | 040086827 | ||||||||||||
TCGA-LGG | TCGA-CS-6186 | 0 | 099913234 | 00008676725 | 0 | 099956185 | 000043818905 | 3 | 00008662089 | 016898473 | 083014905 | ||||||||||||
TCGA-LGG | TCGA-CS-6188 | 0 | 052768165 | 047231838 | 0 | 08584221 | 014157787 | 3 | 019437431 | 047675493 | 03288707 | ||||||||||||
TCGA-LGG | TCGA-CS-6290 | 1 | 09102666 | 008973339 | 0 | 09462997 | 0053700306 | 3 | 0104100704 | 025633416 | 06395651 | ||||||||||||
TCGA-LGG | TCGA-CS-6665 | 1 | 099600047 | 0003999501 | 0 | 099756086 | 00024391294 | 3 | 0011873978 | 001634113 | 097178483 | ||||||||||||
TCGA-LGG | TCGA-CS-6666 | 1 | 021655986 | 07834402 | 0 | 09327296 | 0067270435 | 3 | 017667453 | 036334327 | 045998225 | ||||||||||||
TCGA-LGG | TCGA-CS-6667 | 1 | 012061995 | 087938 | 0 | 095699733 | 0043002643 | 2 | 063733935 | 019323014 | 016943048 | ||||||||||||
TCGA-LGG | TCGA-CS-6668 | 1 | 0076787576 | 09232124 | 1 | 04240933 | 057590663 | 2 | 06810894 | 013706882 | 018184178 | ||||||||||||
TCGA-LGG | TCGA-CS-6669 | 0 | 08488156 | 01511844 | 0 | 094018847 | 005981148 | 2 | 0037862387 | 002352077 | 09386168 | ||||||||||||
TCGA-LGG | TCGA-DU-5849 | 1 | 005773187 | 094226813 | 1 | 08664153 | 013358466 | 2 | 072753835 | 015028271 | 012217898 | ||||||||||||
TCGA-LGG | TCGA-DU-5851 | 1 | 09963994 | 0003600603 | 0 | 099808073 | 00019192374 | 3 | 00060602655 | 012558761 | 08683521 | ||||||||||||
TCGA-LGG | TCGA-DU-5852 | 0 | 09998591 | 000014091856 | 0 | 099954873 | 000045121062 | 3 | 0002267452 | 00038046916 | 099392784 | ||||||||||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0010986943 | 09890131 | 0 | 09549844 | 0045015533 | 2 | 08603989 | 0077804394 | 0061796777 | ||||||||||||
TCGA-LGG | TCGA-DU-5854 | 0 | 09567354 | 0043264627 | 0 | 098768765 | 0012312326 | 3 | 01194655 | 027027336 | 06102612 | ||||||||||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0009312956 | 09906871 | 0 | 046602532 | 053397465 | 3 | 0008289882 | 097042197 | 0021288157 | ||||||||||||
TCGA-LGG | TCGA-DU-5871 | 1 | 005623634 | 09437636 | 0 | 09449439 | 005505607 | 2 | 042517176 | 020180763 | 037302068 | ||||||||||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0062359583 | 09376405 | 0 | 015278916 | 08472108 | 2 | 012133307 | 048199505 | 039667192 | ||||||||||||
TCGA-LGG | TCGA-DU-5874 | 1 | 022858672 | 077141327 | 1 | 06457066 | 03542934 | 2 | 058503634 | 020639434 | 02085693 | ||||||||||||
TCGA-LGG | TCGA-DU-6397 | 1 | 097691274 | 002308724 | 1 | 09908213 | 0009178773 | 3 | 00048094327 | 00412339 | 09539566 | ||||||||||||
TCGA-LGG | TCGA-DU-6399 | 1 | 00023920655 | 099760795 | 0 | 09970073 | 00029926652 | 2 | 098691386 | 0007037292 | 0006048777 | ||||||||||||
TCGA-LGG | TCGA-DU-6400 | 1 | 0030923586 | 09690764 | 1 | 037771282 | 06222872 | 2 | 09710506 | 0015339471 | 001360994 | ||||||||||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0014545513 | 098545444 | 0 | 045332992 | 054667014 | 2 | 0878585 | 006398724 | 005742785 | ||||||||||||
TCGA-LGG | TCGA-DU-6404 | 0 | 08563024 | 014369765 | 0 | 09857318 | 00142681915 | 3 | 0012578745 | 08931047 | 009431658 | ||||||||||||
TCGA-LGG | TCGA-DU-6405 | 0 | 094122344 | 0058776554 | 0 | 09657707 | 0034229323 | 3 | 0015099723 | 0858934 | 012596628 | ||||||||||||
TCGA-LGG | TCGA-DU-6407 | 1 | 00046772743 | 099532276 | 0 | 095787287 | 0042127114 | 2 | 095650303 | 0019410672 | 0024086302 | ||||||||||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0032852467 | 09671475 | 0 | 02978783 | 070212173 | 3 | 046377006 | 04552443 | 008098562 | ||||||||||||
TCGA-LGG | TCGA-DU-6410 | 1 | 084198 | 015801999 | 1 | 09610981 | 0038901985 | 3 | 0029748935 | 0547783 | 042246798 | ||||||||||||
TCGA-LGG | TCGA-DU-6542 | 1 | 099541724 | 0004582765 | 0 | 099690056 | 00030994152 | 3 | 00036504513 | 0033356518 | 0962993 | ||||||||||||
TCGA-LGG | TCGA-DU-7008 | 1 | 00027017966 | 09972982 | 0 | 09924154 | 0007584589 | 2 | 0945233 | 0033200152 | 0021566862 | ||||||||||||
TCGA-LGG | TCGA-DU-7010 | 1 | 09090629 | 0090937115 | 0 | 083999664 | 016000335 | 3 | 0011747591 | 011156695 | 08766855 | ||||||||||||
TCGA-LGG | TCGA-DU-7014 | -1 | 00067384504 | 09932615 | 0 | 09144437 | 008555635 | 2 | 090214694 | 005846623 | 003938676 | ||||||||||||
TCGA-LGG | TCGA-DU-7015 | 1 | 011059116 | 08894088 | 0 | 09457512 | 005424881 | 2 | 04990067 | 023008518 | 027090812 | ||||||||||||
TCGA-LGG | TCGA-DU-7018 | 1 | 06190684 | 038093168 | 1 | 09720721 | 0027927874 | 3 | 002608347 | 03462771 | 06276394 | ||||||||||||
TCGA-LGG | TCGA-DU-7019 | 1 | 006866228 | 09313377 | 0 | 068647516 | 031352484 | 3 | 06280373 | 02546188 | 011734395 | ||||||||||||
TCGA-LGG | TCGA-DU-7294 | 1 | 039513415 | 06048658 | 1 | 04910898 | 05089102 | 2 | 044678423 | 011827048 | 04349453 | ||||||||||||
TCGA-LGG | TCGA-DU-7298 | 1 | 002178117 | 097821885 | 0 | 058896303 | 041103697 | 3 | 04621931 | 040058115 | 013722575 | ||||||||||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0050494254 | 094950575 | 0 | 09805993 | 0019400762 | 3 | 088520575 | 003754964 | 0077244624 | ||||||||||||
TCGA-LGG | TCGA-DU-7300 | 1 | 020334144 | 07966585 | 1 | 021174264 | 078825736 | 3 | 06957292 | 014594184 | 015832895 | ||||||||||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0028517082 | 09714829 | 0 | 07594931 | 024050693 | 2 | 07559878 | 013617343 | 010783881 | ||||||||||||
TCGA-LGG | TCGA-DU-7302 | 1 | 007878401 | 092121595 | 1 | 097414124 | 0025858777 | 3 | 059945434 | 013100924 | 02695364 | ||||||||||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0049359404 | 09506406 | 0 | 09947084 | 0005291605 | 3 | 05746174 | 017312215 | 025226048 | ||||||||||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0774658 | 022534202 | 0 | 09720191 | 0027980946 | 2 | 007909051 | 04979186 | 042299092 | ||||||||||||
TCGA-LGG | TCGA-DU-7309 | 1 | 002068546 | 097931457 | 0 | 091696864 | 008303132 | 3 | 091011685 | 0041825026 | 0048058107 | ||||||||||||
TCGA-LGG | TCGA-DU-8162 | 0 | 019030987 | 08096902 | 0 | 084344435 | 015655571 | 3 | 06724078 | 015660264 | 017098951 | ||||||||||||
TCGA-LGG | TCGA-DU-8164 | 1 | 0026989132 | 09730109 | 1 | 06119184 | 038808158 | 2 | 078654927 | 011851947 | 00949313 | ||||||||||||
TCGA-LGG | TCGA-DU-8165 | 0 | 099918324 | 000081673806 | 0 | 09982692 | 00017308301 | 3 | 00077142627 | 001586733 | 097641844 | ||||||||||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0062617026 | 093738294 | 0 | 052265906 | 047734097 | 2 | 0571523 | 027175376 | 015672325 | ||||||||||||
TCGA-LGG | TCGA-DU-8167 | 1 | 008068282 | 09193171 | 0 | 08626991 | 013730097 | 2 | 07117111 | 014616342 | 014212546 | ||||||||||||
TCGA-LGG | TCGA-DU-8168 | 1 | 04501781 | 05498219 | 1 | 09405718 | 005942822 | 3 | 028535154 | 039651006 | 031813842 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 013576113 | 08642388 | 0 | 098667485 | 0013325148 | 3 | 06805368 | 011191124 | 020755199 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0038810804 | 09611892 | 0 | 094154674 | 005845324 | 2 | 07418394 | 01198958 | 013826479 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 036534345 | 06346565 | 0 | 097664696 | 0023353029 | 2 | 0076500095 | 07058904 | 021760948 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 057493186 | 042506814 | 0 | 08586593 | 014134066 | 3 | 024835269 | 018135522 | 05702921 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 017411166 | 082588834 | 0 | 08903419 | 0109658085 | 2 | 026840523 | 031951824 | 041207647 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 00015382263 | 099846184 | 0 | 09784259 | 0021574067 | 3 | 099424005 | 00014788082 | 0004281163 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 099497885 | 000502115 | 0 | 09904406 | 0009559399 | 3 | 00076062134 | 003340487 | 09589889 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 01338958 | 08661042 | 1 | 010181248 | 08981875 | 2 | 08703488 | 0033631936 | 0096019216 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 007097701 | 092902297 | 1 | 0049773447 | 09502266 | 2 | 08236395 | 0043779366 | 013258114 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 00054852334 | 09945148 | 1 | 00052813343 | 09947187 | 2 | 095740056 | 0030734295 | 0011865048 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 00015218158 | 099847823 | 0 | 09977216 | 00022783307 | 3 | 097611564 | 0011231668 | 0012652792 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 090418625 | 009581377 | 1 | 09320215 | 006797852 | 3 | 015433969 | 006605734 | 0779603 | ||||||||||||
TCGA-LGG | TCGA-EZ-7265A | -1 | 001654544 | 09834546 | -1 | 092290026 | 0077099696 | -1 | 091443384 | 0045349486 | 0040216673 | ||||||||||||
TCGA-LGG | TCGA-FG-5964 | 1 | 095945925 | 004054074 | 1 | 09480585 | 0051941562 | 2 | 0052469887 | 018844457 | 075908554 | ||||||||||||
TCGA-LGG | TCGA-FG-6688 | 0 | 041685596 | 0583144 | 0 | 0400786 | 0599214 | 3 | 032869554 | 028211078 | 03891937 | ||||||||||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0040960647 | 09590394 | 0 | 088871056 | 01112895 | 2 | 078484637 | 01247657 | 009038795 | ||||||||||||
TCGA-LGG | TCGA-FG-6691 | 1 | 00066411127 | 09933589 | 0 | 09705485 | 002945148 | 2 | 082394814 | 01353293 | 004072261 | ||||||||||||
TCGA-LGG | TCGA-FG-6692 | 0 | 099044985 | 0009550158 | 0 | 098370695 | 0016293105 | 3 | 002482948 | 023811981 | 07370507 | ||||||||||||
TCGA-LGG | TCGA-FG-7643 | 0 | 067991304 | 032008696 | 0 | 094600123 | 0053998843 | 2 | 032237333 | 020420441 | 047342223 | ||||||||||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 00037180893 | 09962819 | 0 | 098237246 | 0017627545 | 2 | 09685786 | 0019877713 | 0011543726 | ||||||||||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 0023916386 | 097608364 | 1 | 003330537 | 096669465 | 2 | 016134319 | 0751304 | 0087352775 | ||||||||||||
TCGA-LGG | TCGA-FG-A713 | 1 | 020932822 | 07906717 | 1 | 053740746 | 046259254 | 2 | 068370515 | 013241291 | 018388201 | ||||||||||||
TCGA-LGG | TCGA-HT-7473 | 1 | 026437023 | 073562974 | 0 | 09914391 | 0008560891 | 2 | 009070598 | 05834457 | 032584828 | ||||||||||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0014885316 | 09851147 | 0 | 093397486 | 006602513 | 3 | 09713343 | 0009202645 | 0019462984 | ||||||||||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0078306936 | 09216931 | 0 | 044295275 | 055704725 | 2 | 06683338 | 024550638 | 00861598 | ||||||||||||
TCGA-LGG | TCGA-HT-7616 | 1 | 0994089 | 0005911069 | 1 | 08912444 | 010875558 | 3 | 00015109215 | 00081261955 | 09903628 | ||||||||||||
TCGA-LGG | TCGA-HT-7680 | 0 | 01775255 | 08224745 | 0 | 079779327 | 020220678 | 2 | 06160002 | 021518312 | 016881672 | ||||||||||||
TCGA-LGG | TCGA-HT-7684 | 1 | 099250317 | 00074968883 | 0 | 09977216 | 00022783307 | 3 | 0001585032 | 0011880362 | 09865346 | ||||||||||||
TCGA-LGG | TCGA-HT-7686 | 1 | 043986762 | 05601324 | 0 | 09985134 | 00014866153 | 3 | 08800356 | 0017893802 | 010207067 | ||||||||||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0508178 | 049182203 | 0 | 09986749 | 00013250223 | 3 | 007351807 | 06479417 | 027854022 | ||||||||||||
TCGA-LGG | TCGA-HT-7692 | 1 | 0006764646 | 09932354 | 1 | 00017718028 | 099822825 | 2 | 084003216 | 009709251 | 00628754 | ||||||||||||
TCGA-LGG | TCGA-HT-7693 | 1 | 08835126 | 011648734 | 0 | 098880965 | 0011190402 | 2 | 00389769 | 060259813 | 035842496 | ||||||||||||
TCGA-LGG | TCGA-HT-7694 | 1 | 006299064 | 093700933 | 1 | 06663645 | 03336355 | 3 | 06368097 | 02515797 | 011161056 | ||||||||||||
TCGA-LGG | TCGA-HT-7855 | 1 | 013434944 | 086565053 | 0 | 06805072 | 031949285 | 3 | 03900612 | 035394293 | 02559958 | ||||||||||||
TCGA-LGG | TCGA-HT-7856 | 1 | 0037151825 | 09628482 | 1 | 045108467 | 05489153 | 3 | 0024809493 | 094240344 | 0032787096 | ||||||||||||
TCGA-LGG | TCGA-HT-7860 | 0 | 09996338 | 000036614697 | 0 | 099890125 | 00010987312 | 3 | 00023981468 | 004139189 | 095620996 | ||||||||||||
TCGA-LGG | TCGA-HT-7874 | 1 | 027373514 | 07262649 | 1 | 061277324 | 03872268 | 3 | 030690825 | 04321765 | 026091516 | ||||||||||||
TCGA-LGG | TCGA-HT-7879 | 1 | 006545533 | 09345446 | 0 | 07643643 | 023563562 | 3 | 07316188 | 01349482 | 0133433 | ||||||||||||
TCGA-LGG | TCGA-HT-7882 | 0 | 099826247 | 00017375927 | 0 | 099920684 | 0000793176 | 3 | 00026636408 | 0011237644 | 098609877 | ||||||||||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0045437213 | 09545628 | 0 | 09804874 | 0019512545 | 2 | 067944294 | 020575646 | 011480064 | ||||||||||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0090937115 | 09090629 | 0 | 08061669 | 019383314 | 2 | 069696444 | 017501967 | 012801588 | ||||||||||||
TCGA-LGG | TCGA-HT-8105 | 1 | 09291196 | 0070880495 | 1 | 09865976 | 0013402403 | 3 | 036353382 | 007970196 | 055676425 | ||||||||||||
TCGA-LGG | TCGA-HT-8106 | 1 | 09987081 | 00012918457 | 0 | 099922514 | 00007748164 | 3 | 0019909225 | 0057560045 | 09225307 | ||||||||||||
TCGA-LGG | TCGA-HT-8107 | 0 | 006150854 | 09384914 | 0 | 018944609 | 08105539 | 2 | 071847403 | 015055439 | 013097167 | ||||||||||||
TCGA-LGG | TCGA-HT-8111 | 1 | 062096643 | 037903354 | 0 | 09220272 | 007797278 | 3 | 00015017459 | 090417147 | 009432678 | ||||||||||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0003941571 | 099605846 | 0 | 00025608707 | 099743915 | 2 | 088384247 | 009021307 | 002594447 | ||||||||||||
TCGA-LGG | TCGA-HT-8114 | 1 | 09404078 | 005959219 | 0 | 09970708 | 00029292419 | 3 | 0015292862 | 028022403 | 07044831 | ||||||||||||
TCGA-LGG | TCGA-HT-8563 | 1 | 099999154 | 843094E-06 | 0 | 099999607 | 39515203E-06 | 3 | 32918017E-06 | 031742522 | 068257153 | ||||||||||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 06915494 | 030845058 | 0 | 045883363 | 054116637 | 3 | 012257236 | 02777765 | 059965116 | ||||||||||||
TCGA-LGG | TCGA-HT_A614 | 1 | 08180474 | 018195263 | 0 | 09584989 | 00415011 | 2 | 0067164555 | 0059489973 | 087334543 | ||||||||||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0035779487 | 09642206 | 0 | 07277821 | 0272218 | 2 | 07607039 | 014429174 | 009500429 |
2 Results
21 Patient characteristics
We included a total of 1748 patients in our study 1508 as a train set and240 as an independent test set The patients in the train set originated fromnine different data collections and 16 different institutes and the test set wascollected from two different data collections and 13 different institutes Table 1provides a full overview of the patient characteristics in the train and test setand Figure 2 shows the inclusion flowchart and the distribution of the patientsover the different data collections in the train set and test set
Table 1 Patient characteristics for the train set and test set
Train set Test setN N
Patients 1508 240IDH status
Mutated 226 150 88 367Wildtype 440 292 129 537Unknown 842 558 23 96
1p19q co-deletion statusCo-deleted 103 68 26 108Intact 337 224 207 863Unknown 1068 708 7 29
GradeII 230 153 47 196III 114 76 59 246IV 830 550 132 550Unknown 334 221 2 08
WHO 2016 categorizationOligodendroglioma 96 64 26 108Astrocytoma IDH wildtype 31 21 22 92Astrocytoma IDH mutated 98 64 57 237GBM IDH wildtype 331 219 106 442GBM IDH mutated 16 11 5 21Unknown 936 621 24 100
SegmentationManual 716 475 240 100Automatic 792 525 0 0
IDH isocitrate dehydrogenase WHO World Health Organization GBMglioblastoma
5
Patient screening
Train set2181 Glioma patients
1241 Erasmus MC491 Haaglanden Medical Center168 BraTS130 REMBRANDT66 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set461 Glioma patients
199 TCGA-LGG262 TCGA-GBM
Patient inclusion
Train set1508 Patients in train set
816 Erasmus MC279 Haaglanden Medical Center168 BraTS109 REMBRANDT51 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set240 Patients in test set
107 TCGA-LGG133 TCGA-GBM
Patient exclusion
Train set673 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
425 Erasmus MC212 Haaglanden Medical Center
0 BraTS21 REMBRANDT15 CPTAC-GBM0 Ivy GAP0 Amsterdam UMC0 Brain-Tumor-Progression0 University Medical Center Utrecht
Test set221 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
92 TCGA-LGG129 TCGA-GBM
Figure 2 Inclusion flowchart of the train set and test set
6
22 Algorithm performance
We used 15 of the train set as a validation set and selected the model pa-rameters that achieved the best performance on this validation set where themodel achieved an area under receiver operating characteristic curve (AUC) of088 for the IDH mutation status prediction an AUC of 076 for the 1p19qco-deletion prediction an AUC of 075 for the grade prediction and a meansegmentation DICE score of 081 The selected model parameters are shown inAppendix F We then trained a model using these parameters and the full trainset and evaluated it on the independent test set
For the genetic and histological feature predictions we achieved an AUC of090 for the IDH mutation status prediction an AUC of 085 for the 1p19qco-deletion prediction and an AUC of 081 for the grade prediction in thetest set The full results are shown in Table 2 with the corresponding receiveroperating characteristic (ROC)-curves in Figure 3 Table 2 also shows the resultsin (clinically relevant) subgroups of patients This shows that we achieved anIDH-AUC of 081 in low grade glioma (LGG) (grade IIIII) an IDH-AUC of064 in high grade glioma (HGG) (grade IV) and a 1p19q-AUC of 073 in LGGWhen only predicting LGG vs HGG instead of predicting the individual gradeswe achieved an AUC of 091 In Appendix A we provide confusion matrices forthe IDH 1p19q and grade predictions as well as a confusion matrix for thefinal WHO 2016 subtype which shows that only one patient was predicted asa non-existing WHO 2016 subtype In Appendix C we provide the individualpredictions and ground truth labels for all patients in the test set to allow forthe calculation of additional metrics
For the automatic segmentation we achieved a mean DICE score of 084 amean Hausdorff distance of 189 mm and a mean volumetric similarity coeffi-cient of 090 Figure 4 shows boxplots of the DICE scores Hausdorff distancesand volumetric similarity coefficients for the different patients in the test set InAppendix B we show five patients that were randomly selected from both theTCGA-LGG and TCGA-GBM data collections to demonstrate the automaticsegmentations made by our method
23 Model interpretability
To provide insight into the behavior of our model we created saliency mapswhich show which parts of the scans contributed the most to the predictionThese saliency maps are shown in Figure 5 for two example patients from thetest set It can be seen that for the LGG the network focused on a bright rim inthe T2w-FLAIR scan whereas for the HGG it focused on the enhancement in thepost-contrast T1w scan To aid further interpretation we provide visualizationsof selected filter outputs in the network in Appendix D which also show thatthe network focuses on the tumor and these filters seem to recognize specificimaging features such as the contrast enhancement and T2w-FLAIR brightness
7
Table 2 Evaluation results of the final model on the test set
Patientgroup
Task AUC Accuracy Sensitivity Specificity
All IDH 090 084 072 0931p19q 085 089 039 095Grade (IIIIIIV) 081 071 NA NAGrade II 091 086 075 089Grade III 069 075 017 094Grade IV 091 082 095 066LGG vs HGG 091 084 072 093
LGG IDH 081 074 073 0771p19q 073 076 039 089
HGG IDH 064 094 040 096
Abbreviations AUC area under receiver operating characteristic curve IDHisocitrate dehydrogenase LGG low grade glioma HGG high grade glioma
Figure 3 Receiver operating characteristic (ROC)-curves of the genetic andhistological features evaluated on the test set The crosses indicate the locationof the decision threshold for the reported accuracy sensitivity and specificity
8
Figure 4 DICE scores Hausdorff distances and volumetric similarity coeffi-cients for all patients in the test set The DICE score is a measure of theoverlap between the ground truth and predicted segmentation (where 1 indi-cates perfect overlap) The Hausdorff distance is a measure of the agreementbetween the boundaries of the ground truth and predicted segmentation (loweris better) The volumetric similarity coefficient is a measure of the agreementin volume (where 1 indicates perfect agreement)
9
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Saliency maps of a low grade glioma patient (TCGA-DU-6400) This is an IDH mu-tated 1p19q co-deleted grade II tumor The network focuses on a rim of brightnessin the T2w-FLAIR scan
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) Saliency maps of a high grade glioma patient (TCGA-06-0238) This is an IDHwildtype grade IV tumor The network focuses on enhancing spots around the necrosison the post-contrast T1w scan
Figure 5 Saliency maps of two patients from the test set showing areas thatare relevant for the prediction
10
24 Model robustness
By not excluding scans from our train set based on radiological characteristicswe were able to make our model robust to low scan quality as can be seen in anexample from the test set in Figure 6 Even though this example scan containedimaging artifacts our method was able to properly segment the tumor (DICEscore of 087) and correctly predict the tumor as an IDH wildtype grade IVtumor
Figure 6 Example of a T2w-FLAIR scan containing imaging artifacts and theautomatic segmentation (red overlay) made by our method It was correctlypredicted as an IDH wildtype grade IV glioma This is patient TCGA-06-5408from the TCGA-GBM collection
Finally we considered two examples of scans that were incorrectly predictedby our method see Figure 7 These two examples were chosen because ournetwork assigned high prediction scores to the wrong classes for these casesFigure 7a shows an example of a grade II IDH mutated 1p19q co-deletedglioma that was predicted as grade IV IDH wildtype by our method Ourmethodrsquos prediction was most likely caused by the hyperintensities in the post-contrast T1w scan being interpreted as contrast enhancement Since these hy-perintensities are also present in the pre-contrast T1w scan they are most likelycalcifications and the radiological appearance of this tumor is indicative of anoligodendroglioma Figure 7b shows an example of a grade IV IDH wildtypeglioma that was predicted as a grade III IDH mutated glioma by our method
11
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) TCGA-DU-6410 from the TCGA-LGG collection The ground truth histopatho-logical analysis indicated this glioma was grade II IDH mutated 1p19q co-deletedbut our method predicted it as a grade IV IDH wildtype
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) TCGA-76-7664 from the TCGA-HGG collection Histopathologically this gliomawas grade IV IDH wildtype but our method predicted it as grade III IDH mutated
Figure 7 Examples of scans that were incorrectly predicted by our method
12
3 Discussion
We have developed a method that can predict the IDH mutation status 1p19qco-deletion status and grade of glioma while simultaneously providing the tu-mor segmentation based on pre-operative MRI scans For the genetic andhistological feature predictions we achieved an AUC of 090 for the IDH mu-tation status prediction an AUC of 085 for the 1p19q co-deletion predictionand an AUC of 081 for the grade prediction in the test set
In an independent test set which contained data from 13 different instituteswe demonstrated that our method predicts these features with good overall per-formance we achieved an AUC of 090 for the IDH mutation status predictionan AUC of 085 for the 1p19q co-deletion prediction and an AUC of 081 forthe grade prediction and a mean whole tumor DICE score of 084 This per-formance on unseen data that was only used during the final evaluation of thealgorithm and that was purposefully not used to guide any decisions regardingthe method design shows the true generalizability of our method Using thelatest GPU capabilities we were able to train a large model which uses thefull 3D scan as input Furthermore by using the largest most diverse patientcohort to date we were able to make our method robust to the heterogeneitythat is naturally present in clinical imaging data such that it generalizes forbroad application in clinical practice
By using a multi-task network our method could learn the context betweendifferent features For example IDH wildtype and 1p19q co-deletion are mu-tually exclusive [21] If two separate methods had been used one to predict theIDH status and one to predict the 1p19q co-deletion status an IDH wildtypeglioma might be predicted to be 1p19q co-deleted which does not stroke withthe clinical reality Since our method learns both of these genetic features si-multaneously it correctly learned not to predict 1p19q co-deletion in tumorsthat were IDH wildtype there was only one patient in which our algorithm pre-dicted a tumor to be both IDH wildtype and 1p19q co-deleted Furthermoreby predicting the genetic and histological features individually instead of onlypredicting the WHO 2016 category it is possible to adopt updated guidelinessuch as cIMPACT-NOW future-proofing our method [22]
Some previous studies also used multi-task networks to predict the geneticand histological features of glioma [23 24 25] Tang et al [23] used a multi-tasknetwork that predicts multiple genetic features as well as the overall survivalof glioblastoma Since their method only works for glioblastoma patients thetumor grade must be known in advance complicating the use of their methodin the pre-operative setting when tumor grade is not yet known Furthermoretheir method requires a tumor segmentation prior to application of their methodwhich is a time-consuming expert task In a study by Xue et al [24] a multi-task network was used with a structure similar to the one proposed in thispaper to segment the tumor and predict the grade (LGG or HGG) and IDHmutation status However they do not predict the 1p19q co-deletion statusneeded for the WHO 2016 categorization Lastly Decuyper et al [25] useda multi-task network that predicts the IDH mutation and 1p19q co-deletion
13
status and the tumor grade (LGG or HGG) Their method requires a tumorsegmentation as input which they obtain from a U-Net that is applied earlierin their pipeline thus their method requires two networks instead of the singlenetwork we use in our method These differences aside the most importantlimitation of each of these studies is the lack of an independent test set forevaluating their results It is now considered essential that an independent testset is used to prevent an overly optimistic estimate of a methodrsquos performance[15 26 27 28] Thus our study improves on this previous work by providinga single network that combines the different tasks being trained on a moreextensive and diverse dataset not requiring a tumor segmentation as an in-put providing all information needed for the WHO 2016 categorization andcrucially by being evaluated in an independent test set
An important genetic feature that is not predicted by our method is theO6-methylguanine-methyltransferase (MGMT) methylation status Althoughthe MGMT methylation status is not part of the WHO 2016 categorizationit is part of clinical management guidelines and is an important prognosticmarker in glioblastoma [4] In the initial stages of this study we attemptedto predict the MGMT methylation status however the performance of thisprediction was poor Furthermore the methylation cutoff level which is usedto determine whether a tumor is MGMT methylated shows a wide varietybetween institutes leading to inconsistent results [29] We therefore opted not toinclude the MGMT prediction at all rather than to provide a poor prediction ofan unsharply defined parameter Although some methods attempted to predictthe MGMT status with varying degrees of success there is still an ongoingdiscussion on the validity of MR imaging features of the MGMT status [23 3031 32 33]
Our method shows good overall performance but there are noticeable per-formance differences between tumor categories For example when our methodpredicts a tumor as an IDH wildtype glioblastoma it is correct almost all of thetime On the other hand it has some difficulty differentiating IDH mutated1p19q co-deleted low-grade glioma from other low-grade glioma The sensitiv-ity for the prediction of grade III glioma was low which might be caused bythe lack of a central pathology review Because of this there were differences inmolecular testing and histological analysis and it is known that distinguishingbetween grade II and grade III has a poor observer reliability [34] Althoughour method can be relevant for certain subgroups our methodrsquos performancestill needs to be improved to ensure relevancy for the full patient population
In future work we aim to increase the performance of our method by includ-ing perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI)since there has been an increasing amount of evidence that these physiologi-cal imaging modalities contain additional information that correlates with thetumorrsquos genetic status and aggressiveness [35 36] They were not included inthis study since PWI and to a lesser extent DWI are not as ingrained in theclinical imaging routine as the structural scans used in this work [19 20] Thusincluding these modalities would limit our methodrsquos clinical applicability andsubstantially reduce the number of patients in the train and test set However
14
PWI and DWI are increasingly becoming more commonplace which will allowincluding these in future research and which might improve performance
In conclusion we have developed a non-invasive method that can predict theIDH mutation status 1p19q co-deletion status and grade of glioma while atthe same time segmenting the tumor based on pre-operative MRI scans withhigh overall performance Although the performance of our method might needto be improved before it will find widespread clinical acceptance we believe thatthis research is an important step forward in the field of radiomics Predict-ing multiple clinical features simultaneously steps away from the conventionalsingle-task methods and is more in line with the clinical practice where multipleclinical features are considered simultaneously and may even be related Fur-thermore by not limiting the patient population used to develop our method toa selection based on clinical or radiological characteristics we alleviate the needfor a priori (expert) knowledge which may not always be available Althoughsteps still have to be taken before radiomics will find its way into the clinicespecially in terms of performance our work provides a crucial step forward byresolving some of the hurdles of clinical implementation now and paving theway for a full transition in the future
4 Methods
41 Patient population
The train set was collected from four in-house datasets and five publicly avail-able datasets In-house datasets were collected from four different institutesErasmus MC (EMC) Haaglanden Medical Center (HMC) Amsterdam UMC(AUMC) [37] and University Medical Center Utrecht (UMCU) Four of the fivepublic datasets were collected from The Cancer Imaging Archive (TCIA) [38]the Repository of Molecular Brain Neoplasia Data (REMBRANDT) collection[39] the Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multi-forme (CPTAC-GBM) collection [40] the Ivy Glioblastoma Atlas Project (IvyGAP) collection [41 42] and the Brain-Tumor-Progression collection [43] Thefifth dataset was the 2019 Brain Tumor Segmentation challenge (BraTS) chal-lenge dataset [44 45 46] from which we excluded the patients that were alsoavailable in the TCGA-LGG and TCGA-GBM collections [47 48]
For the internal datasets from the EMC and the HMC manual segmentationswere available which were made by four different clinical experts For patientswhere segmentations from more than one observer were available we randomlypicked one of the segmentations to use in the train set The segmentationsfrom the AUMC data were made by a single observer of the study by Visseret al [37] From the public datasets only the BraTS dataset and the Brain-Tumor-Progression dataset provided manual segmentations Segmentations ofthe BraTS dataset as provided in the 2019 training and validation set wereused For the Brain-Tumor-Progression dataset the segmentations as providedin the TCIA data collection were used
15
Patients were included if pre-operative pre- and post-contrast T1w T2wand T2w-FLAIR scans were available no further inclusion criteria were setFor example patients were not excluded based on the radiological characteristicsof the scan such as low imaging quality or imaging artifacts or the gliomarsquosclinical characteristics such as the grade If multiple scans of the same contrasttype were available in a single scan session (eg multiple T2w scans) the scanupon which the segmentation was made was selected If no segmentation wasavailable or the segmentation was not made based on that scan contrast thescan with the highest axial resolution was used where a 3D acquisition waspreferred over a 2D acquisition
For the in-house data genetic and histological data were available for theEMC HMC and UMCU dataset which were obtained from analysis of tumortissue after biopsy or resection Genetic and histological data of the publicdatasets were also available for the REMBRANDT CPTAC-GBM and IvyGAP collections Data for the REMBRANDT and CPTAC-GBM collectionswas collected from the clinical data available at the TCIA [40 39] For theIvy GAP collection the genetic and histological data were obtained from theSwedish Institute at httpsivygapswedishorghome
As a test set we used the TCGA-LGG and TCGA-GBM collections from theTCIA [47 48] Genetic and histological labels were obtained from the clinicaldata available at the TCIA Segmentations were used as available from theTCIA based on the 2018 BraTS challenge [45 49 50] The inclusion criteriafor the patients included in the BraTS challenge were the same as our inclusioncriteria the presence of a pre-operative pre- and post-contrast T1w T2w andT2w-FLAIR scan Thus patients from the TCGA-LGG and TCGA-GBM wereincluded if a segmentation from the BraTS challenge was available Howeverfor three patients we found that although they did have manual segmentationsthey did not meet our inclusion requirements TCGA-08-0509 and TCGA-08-0510 from TCGA-GBM because they did not have a pre-contrast T1w scan andTCGA-FG-7634 from TCGA-LGG because there was no post-contrast T1wscan
42 Automatic segmentation in the train set
To present our method with a large diversity in scans we wanted to include asmany patients in the train set as possible from the different datasets Thereforewe performed automatic segmentation in patients that did not have manualsegmentations To this end we used an initial version of our network (presentedin Section 44) without the additional layers that were needed for the predictionof the genetic and histological features This network was initially trained usingall patients in the train set for whom a manual segmentation was availableand this trained network was then applied to all patients for which a manualsegmentation was not available The resulting automatic segmentations wereinspected and if their quality was acceptable they were added to the trainset The network was then trained again using this increased dataset and wasapplied to scans that did not yet have a segmentation of acceptable quality
16
This process was repeated until an acceptable segmentation was available forall patients which constituted our final complete train set
43 Pre-processing
For all datasets except for the BraTS dataset for which the scans were alreadyprovided in NIfTI format the scans were converted from DICOM format toNIfTI format using dcm2niix version v1020190410 [51] We then registeredall scans to the MNI152 T1w and T2w atlases version ICBM 2009a whichhad a resolution of 1x1x1 mm3 and a size of 197x233x189 voxels [52 53] Thescans were affinely registered using Elastix 50 [54 55] The pre- and post-contrast T1w scans were registered to the T1w atlas the T2w and T2w-FLAIRscans were registered to the T2w atlas When a manual segmentation wasavailable for patients from the in-house datasets the registration parametersthat resulted from registering the scan used during the segmentation were usedto transform the segmentation to the atlas In the case of the public datasetswe used the registration parameters of the T2w-FLAIR scans to transform thesegmentations
After the registration all scans were N4 bias field corrected using SimpleITKversion 124 [56] A brain mask was made for the atlas using HD-BET both forthe T1w atlas and the T2w atlas [57] This brain mask was used to skull stripall registered scans and crop them to a bounding box around the brain maskreducing the amount of background present in the scans resulting in a scan sizeof 152x182x145 voxels Subsequently the scans were normalized such that foreach scan the average image intensity was 0 and the standard deviation of theimage intensity was 1 within the brain mask Finally the background outsidethe brain mask was set to the minimum intensity value within the brain mask
Since the segmentation could sometimes be rugged at the edges after regis-tration especially when the segmentations were initially made on low-resolutionscans we smoothed the segmentation using a 3x3x3 median filter (this was onlydone in the train set) For segmentations that contained more than one la-bel eg when the tumor necrosis and enhancement were separately segmentedall labels were collapsed into a single label to obtain a single segmentation ofthe whole tumor The genetic and histological labels and the segmentations ofeach patient were one-hot encoded The four scans ground truth labels andsegmentation of each patient were then used as the input to the network
44 Model
We based the architecture of our model on the U-Net architecture with someadaptations made to allow for a full 3D input and the auxiliary tasks [58] Ournetwork architecture which we have named PrognosAIs Structure-Net or PS-Net for short can be seen in Figure 8
To use the full 3D scan as an input to the network we replaced the firstpooling layer that is usually present in the U-Net with a strided convolutionwith a kernel size of 9x9x9 and a stride of 3x3x3 In the upsampling branch of
17
32
32 64
128
256
512 256
7x8x7256 128
128 64
64 32
32 2
Segmentation
145x182x152
49x61x51
25x31x26
13x16x13
1472
512 2IDH
512 2
1p19q
512 3Grade
Batch normalization Concatenation Convolution amp ReLU3x3x3
Convolution amp Softmax1x1x1
(De)convolution amp ReLU9x9x9
stride 3x3x3
Dense amp ReLU Dense amp Softmax Dropout
Max pooling2x2x2
Up-convolution amp ReLU2x2x2
Global maxpooling
Figure 8 Overview of the PrognosAIs Structure-Net (PS-Net) architecture usedfor our model The numbers below the different layers indicate the number offilters dense units or features at that layer We have also indicated the featuremap size at the different depths of the network
the network the last up-convolution is replaced by a deconvolution with thesame kernel size and stride
At each depth of the network we have added global max-pooling layersdirectly after the dropout layer to obtain imaging features that can be used topredict the genetic and histological features We chose global pooling layers asthey do not introduce any additional parameters that need to be trained thuskeeping the memory required by our model manageable The features from thedifferent depths of the network were concatenated and fed into three differentdense layers one for each of the genetic and histological outputs
l2 kernel regularization was used in all convolutional layers except for thelast convolutional layer used for the output of the segmentation In total thismodel contained 27042473 trainable an 2944 non-trainable parameters
18
45 Model training
Training of the model was done on eight NVidia RTX2080Tirsquos with 11GB ofmemory using TensorFlow 220 [59] To be able to use the full 3D scan asinput to the network without running into memory issues we had to optimizethe memory efficiency of the model training Most importantly we used mixed-precision training which means that most of the variables of the model (such asthe weights) were stored in float16 which requires half the memory of float32which is typically used to store these variables [60] Only the last softmaxactivation layers of each output were stored as float32 We also stored ourpre-processed scans as float16 to further reduce memory usage
However even with these settings we could not use a batch size larger than1 It is known that a larger batch size is preferable as it increases the stabilityof the gradient updates and allows for a better estimation of the normalizationparameters in batch normalization layers [61] Therefore we distributed thetraining over the eight GPUs using the NCCL AllReduce algorithm whichcombines the gradients calculated on each GPU before calculating the updateto the model parameters [62] We also used synchronized batch normalizationlayers which synchronize the updates of their parameters over the distributedmodels In this way our model had a virtual batch size of eight for the gradientupdates and the batch normalization layers parameters
To provide more samples to the algorithm and prevent potential overtrain-ing we applied four types of data augmentation during training croppingrotation brightness shifts and contrast shifts Each augmentation was appliedwith a certain augmentation probability which determined the probability ofthat augmentation type being applied to a specific sample When an image wascropped a random number of voxels between 0 and 20 was cropped from eachdimension and filled with zeros For the random rotation an angle betweenminus30 and 30 degrees was selected from a uniform distribution for each dimen-sion The brightness shift was applied with a delta uniformly drawn between0 and 02 and the contrast shift factor was randomly drawn between 085 and115 We also introduced an augmentation factor which determines how ofteneach sample was parsed as an input sample during a single epoch where eachtime it could be augmented differently
For the IDH 1p19q and grade output we used a masked categorical cross-entropy loss and for the segmentation we used a DICE loss see Appendix E fordetails We used AdamW as an optimizer which has shown improved general-ization performance over Adam by introducing the weight decay parameter as aseparate parameter from the learning rate [63] The learning rate was automat-ically reduced by a factor of 025 if the loss did not improve during the last fiveepochs with a minimum learning rate of 1 middot 10minus11 The model could train for amaximum of 150 epochs and training was stopped early if the average loss overthe last five epochs did not improve Once the model was finished training theweights from the epoch with the lowest loss were restored
19
46 Hyperparameter tuning
Hyperparameters involved in the training of the model needed to be tuned toachieve the best performance We tuned a total of six hyper parameters the l2-norm the dropout rate the augmentation factor the augmentation probabilitythe optimizerrsquos initial learning rate and the optimizerrsquos weight decay A fulloverview of the trained parameters and the values tested for the different settingsis presented in Appendix F
To tune these hyperparameters we split the train set into a hyperparametertraining set (851282 patients of the full train data) and a hyperparametervalidation set (15226 patients of the full train data) Models were trained fordifferent hyperparameter settings via an exhaustive search using the hyperpa-rameter train set and then evaluated on the hyperparameter validation set Nodata augmentation was applied to the hyperparameter validation to ensure thatresults between trained models were comparable The hyperparameters that ledto the lowest overall loss in the hyperparameter validation set were chosen asthe optimal hyperparameters We trained the final model using these optimalhyperparameters and the full train set
47 Post-processing
The predictions of the network were post-processed to obtain the final predictedlabels and segmentations for the samples Since softmax activations were usedfor the genetic and histological outputs a prediction between 0 and 1 was out-putted for each class where the individual predictions summed to 1 The finalpredicted label was then considered as the class with the highest prediction scoreFor the prediction of LGG (grade IIIII) vs HGG (grade IV) the predictionscores of grade II and grade III were combined to obtain the prediction scorefor LGG the prediction score of grade IV was used as the prediction score forHGG If a segmentation contained multiple unconnected components we onlyretained the largest component to obtain a single whole tumor segmentation
48 Model evaluation
The performance of the final trained model was evaluated on the independenttest set comparing the predicted labels with the ground truth labels For thegenetic and histological features we evaluated the AUC the accuracy the sen-sitivity and the specificity using scikit-learn version 0231 for details see Ap-pendix G [64] We evaluated these metrics on the full test set and in subcate-gories relevant to the WHO 2016 guidelines We evaluated the IDH performanceseparately in the LGG (grade IIIII) and HGG (grade IV) subgroups the 1p19qperformance in LGG and we also evaluated the performance of distinguishingbetween LGG and HGG instead of predicting the individual grades
To evaluate the performance of the segmentation we calculated the DICEscores Hausdorff distances and volumetric similarity coefficient comparing theautomatic segmentation of our method and the manual ground truth segmen-
20
tations for all patients in the test set These metrics were calculated usingthe EvaluateSegmentation toolbox version 20170425 [65] for details see Ap-pendix G
To prevent an overly optimistic estimation of our modelrsquos predictive valuewe only evaluated our model on the test set once all hyperparameters werechosen and the final model was trained In this way the performance in thetest set did not influence decisions made during the development of the modelpreventing possible overfitting by fine-tuning to the test set
To gain insight into the model we made saliency maps that show whichparts of the scan contribute the most to the prediction of the CNN [66] Saliencymaps were made using tf-keras-vis 052 changing the activation function of alloutput layers from softmax to linear activations using SmoothGrad to reducethe noisiness of the saliency maps [66]
Another way to gain insight into the networkrsquos behavior is to visualize thefilter outputs of the convolutional layers as they can give some idea as to whatoperations the network applies to the scans We visualized the filter outputs ofthe last convolutional layers in the downsample and upsample path at the firstdepth (at an image size of 49x61x51) of our network These filter outputs werevisualized by passing a sample through the network and showing the convolu-tional layersrsquo outputs replacing the ReLU activation with linear activations
49 Data availability
An overview of the patients included from the public datasets used in the train-ing and testing of the algorithm and their ground truth label is available inAppendix H The data from the public datasets are available in TCIA un-der DOIs 107937K9TCIA2015588OZUZB 107937k9tcia20183rje41q1107937K9TCIA2016XLwaN6nL and 107937K9TCIA201815quzvnb Datafrom the BraTS are available at httpbraintumorsegmentationorg Datafrom the in-house datasets are not publicly available due to participant privacyand consent
410 Code availability
The code used in this paper is available on GitHub under an Apache 2 license athttpsgithubcomSvdvoortPrognosAIs_glioma This code includes thefull pipeline from registration of the patients to the final post-processing of thepredictions The trained model is also available on GitHub along with code toapply it to new patients
21
Appendices
A Confusion matrices
Tables 3 4 and 5 show the confusion matrices for the IDH 1p19q and gradepredictions and Table 6 shows the confusion matrix for the WHO 2016 subtypes
Table 4 shows that the algorithm mainly has difficulty recognizing 1p19qco-deleted tumors which are mostly predicted as 1p19q intact Table 5 showsthat most of the incorrectly predicted grade III tumors are predicted as gradeIV tumors
Table 6 shows that our algorithm often incorrectly predicts IDH-wildtypeastrocytoma as IDH-wildtype glioblastoma The latest cIMPACT-NOW guide-lines propose a new categorization in which IDH-wildtype astrocytoma thatshow either TERT promoter methylation or EFGR gene amplification or chro-mosome 7 gainchromsome 10 loss are classified as IDH-wildtype glioblastoma[22] This new categorization is proposed since the survival of patients withthose IDH-wildtype astrocytoma is similar to the survival of patients with IDH-wildtype glioblastoma [22] From the 13 IDH-wildtype astrocytoma that werewrongly predicted as IDH-wildtype glioblastoma 12 would actually be cate-gorized as IDH-wildtype glioblastoma under this new categorization Thusalthough our method wrongly predicted the WHO 2016 subtype it might ac-tually have picked up on imaging features related to the aggressiveness of thetumor which might lead to a better categorization
Table 3 Confusion matrix of the IDH predictions
Predicted
Wildtype Mutated
Actu
al
Wildtype 120 9
Mutated 25 63
Table 4 Confusion matrix of the 1p19q predictions
Predicted
Intact Co-deleted
Actu
al
Intact 197 10
Co-deleted 16 10
22
Table 5 Confusion matrix of the grade predictions
Predicted
Grade II Grade III Grade IV
Actu
al Grade II 35 6 6
Grade III 19 10 30
Grade IV 2 5 125
Table 6 Confusion matrix of the WHO 2016 predictions The rsquootherrsquo categoryindicates patients that were predicted as a non-existing WHO 2016 subtype forexample IDH wildtype 1p19q co-deleted tumors Only one patient (TCGA-HT-A5RC) was predicted as a non-existing category It was predicted as anIDH wildtype 1p19q co-deleted grade IV tumor
Predicted
Oligodendrogliom
a
IDH-m
utated
astrocytoma
IDH-w
ildtype
astrocytoma
IDH-m
utated
glioblastoma
IDH-w
ildtype
glioblastoma
Other
Actu
al
Oligodendroglioma 10 8 1 0 7 0
IDH-mutatedastrocytoma 6 34 4 3 10 0
IDH-wildtypeastrocytoma 1 2 3 2 13 1
IDH-mutatedglioblastoma 0 1 0 0 3 0
IDH-wildtypeglioblastoma 0 3 3 1 96 0
Oligodendroglioma are IDH-mutated 1p19q co-deleted grade IIIII giomaIDH-mutated astrocytoma are IDH-mutated 1p19q intact grade IIIII gliomaIDH-wildtype astrocytoma are IDH-wildtype 1p19q intact grade IIIII gliomaIDH-mutated glioblastoma are IDH-mutated grade IV gliomaIDH-wildtype glioblastoma are IDH-wildtype grade IV glioma
23
B Segmentation examples
To demonstrate the automatic segmentations made by our method we randomlyselected five patients from both the TCGA-LGG and the TCGA-GBM datasetThe scans and segmentations of the five patients from the TCGA-LGG datasetand the TCGA-GBM dataset are shown in Figures 9 and 10 respectively TheDICE score Hausdorff distance and volumetric similarity coefficient for thesepatients are given in Table 7 The method seems to mostly focus on the hyper-intensities of the T2w-FLAIR scan Despite the registrations issues that can beseen for the T2w scan in Figure 10d the tumor was still properly segmenteddemonstrating the robustness of our method
Patient DICE HD (mm) VSC
TCGA-LGG
TCGA-DU-7301 089 103 095TCGA-FG-5964 080 58 082TCGA-FG-A713 073 78 088TCGA-HT-7475 087 149 090TCGA-HT-8106 088 112 099
TCGA-GBM
TCGA-02-0037 082 226 099TCGA-08-0353 091 130 098TCGA-12-1094 090 73 093TCGA-14-3477 090 165 099TCGA-19-5951 073 197 073
Table 7 The DICE score Hausdorff distance (HD) and volumetric similaritycoefficient (VSC) for the randomly selected patients from the TCGA-LGG andTCGA-GBM data collections
24
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-DU-7301 from the TCGA-LGG data collection
(b) Patient TCGA-FG-5964 from the TCGA-LGG data collection
(c) Patient TCGA-FG-A713 from the TCGA-LGG data collection
(d) Patient TCGA-HT-7475 from the TCGA-LGG data collection
(e) Patient TCGA-HT-8106 from the TCGA-LGG data collection
Figure 9 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-LGG data collection
25
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-02-0037 from the TCGA-GBM data collection
(b) Patient TCGA-08-0353 from the TCGA-GBM data collection
(c) Patient TCGA-12-1094 from the TCGA-GBM data collection
(d) Patient TCGA-14-3477 from the TCGA-GBM data collection
(e) Patient TCGA-19-5951 from the TCGA-GBM data collection
Figure 10 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-GBM data collection
26
C Prediction results in the test set
27
D Filter output visualizations
Figures 11 and 12 show the output of the convolution filters for the same LGGpatient as shown in Figure 5a and Figures 13 and 14 show the output of theconvolution filters for the same HGG patient as shown in Figure 5b Figures 11and 13 show the outputs of the last convolution layer in the downsample pathat the feature size of 49x61x51 (the fourth convolutional layer in the network)Figures 12 and 14 show the outputs of the last convolution layer in the upsamplepath at the feature size of 49x61x51 (the nineteenth convolutional layer in thenetwork)
Comparing Figure 11 to Figure 12 and Figure 13 to Figure 14 we can seethat the convolutional layers in the upsample path do not keep a lot of detailfor the healthy part of the brain as this region seems blurred However withinthe tumor different regions can still be distinguished The different parts ofthe tumor from the scans can also be seen such as the contrast-enhancing partand the high signal intensity on the T2w-FLAIR For the grade IV glioma inFigure 14 some filters such as filter 26 also seem to focus on the necrotic partof the tumor
28
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 11 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
29
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 12 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
30
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 13 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-06-0238 This is an IDH wildtype grade IV glioma
31
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 14 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-06-0238 This is an IDH wildtype grade IV glioma
32
E Training losses
During the training of the network we used masked categorical cross-entropy lossfor the IDH 1p19q and grade outputs The normal categorical cross-entropyloss is defined as
LCEbatch = minus 1
Nbatch
sumj
sumiisinC
yij log (yij) (1)
where LCEbatch is the total cross-entropy loss over a batch yij is the ground truth
label of sample j for class i yij is the prediction score for sample j for class iC is the set of classes and Nbatch is the number of samples in the batch Here itis assumed that the ground truth labels are one-hot-encoded thus yij is either0 or 1 for each class In our case the ground truth is not known for all sampleswhich can be incorporated in Equation (1) by setting yij to 0 for all classesfor a sample for which the ground truth is not known That sample wouldthen not contribute to the overall loss and would not contribute to the gradientupdate However this can skew the total loss over a batch since the loss is stillaveraged over the total number of samples in a batch regardless of whether theground truth is known resulting in a lower loss for batches that contained moresamples with unknown ground truth Therefore we used a masked categoricalcross-entropy loss
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
yij log (yij) (2)
where
microbatchj =
Nbatchsumij yij
sumi
yij (3)
is the batch weight for sample j In this way the total batch loss is only averagedover the samples that actually have a ground truth
Since there was an imbalance between the number of ground truth samplesfor each class we used class weights to compensate for this imbalance Thusthe loss becomes
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
microclassi yij log (yij) (4)
where
microclassi =
N
Ni |C|(5)
is the class weight for class i N is the total number of samples with knownground truth Ni is the number of samples of class i and |C| is the number ofclasses By determining the class weight in this way we ensured that
microclassi Ni =
N
|C|= constant (6)
33
Thus each class would have the same contribution to the overall loss Theseclass weights were (individually) determined for the IDH output the 1p19qoutput and the grade output
For the segmentation output we used the DICE loss
LDICEbatch =
sumj
1minus 2 middotsumvoxels
k yjk middot yjksumvoxelsk yjk + yjk
(7)
where yjk is the ground truth label in voxel k of sample j and yjk is theprediction score outputted for voxel k of sample j
The total loss that was optimized for the model was a weighted sum of thefour individual losses
Ltotal =summ
micromLm (8)
with
microm =1
Xm (9)
where Lm is the loss for output m microm is the loss weight for loss m (either theIDH 1p19q grade or segmentation loss) and Xm is the number of sampleswith known ground truth for output m In this way we could counteract theeffect of certain outputs having more known labels than other outputs
34
F Parameter tuning
Table 8 Hyperparameters that were tuned and the values that were testedValues in bold show the selected values used in the final model
Tuning parameter Tested values
Dropout rate 015 02 025 030 035 040l2-norm 00001 000001 0000001Learning rate 001 0001 00001 000001 00000001Weight decay 0001 00001 000001Augmentation factor 1 2 3Augmentation probability 025 030 035 040 045
35
G Evaluation metrics
We calculated the AUC accuracy sensitivity and specificity metrics for thegenetic and histological features for the definitions of these metrics see [67]
For the IDH and 1p19q co-deletion outputs the IDH mutated and the1p19q co-deleted samples were regarded as the positive class respectively Sincethe grade was a multi-class problem no single positive class could be determinedFor the prediction of the individual grades that grade was seen as the positiveclass and all other grades as the negative class (eg in the case of the gradeIII prediction grade III was regarded as the positive class and grade II and IVwere regarded as the negative class) For the LGG vs HGG prediction LGGwas considered as the positive class and HGG as the negative class For theevaluation of these metrics for the genetic and histological features only thesubjects with known ground truth were taken into account
The overall AUC for the grade was a multi-class AUC determined in a one-vs-one approach comparing each class against the others in this way thismetric was insensitive to class imbalance [68] A multi-class accuracy was usedto determine the overall accuracy for the grade predictions [67]
To evaluate the performance of the automated segmentation we evaluatedthe DICE score the Hausdorff distance and the volumetric similarity coefficientThe DICE score is a measure of overlap between two segmentations where avalue of 1 indicates perfect overlap and the Hausdorff distance is a measureof the closeness of the borders of the segmentations The volumetric similaritycoefficient is a measure of the agreement between the volumes of two segmen-tations without taking account the actual location of the tumor where a valueof 1 indicates perfect agreement See [65] for the definitions of these metrics
36
H Ground truth labels of patients included frompublic datasets
Acknowledgments
Sebastian van der Voort and Fatih Incekara acknowledge funding by the DutchCancer Society (KWF project number EMCR 2015-7859)
Data used in this publication were generated by the National Cancer Insti-tute Clinical Proteomic Tumor Analysis Consortium (CPTAC)
The results published here are in whole or part based upon data generatedby the TCGA Research Network httpcancergenomenihgov
Author contributions
SRvdV FI WJN MS and SK contrived the study and designed theexperiments FI MMJW JWS RNT GJL PCDWH RSEAJPEV MJvdB and MS included patients in the different studiesSRvdV FI MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV MJvdB and MS collected the dataSRvdV carried out the experiments SRvdV FI MS and SK inter-preted the results SRvdV FI MJvdB MS and SK created the initialdraft of the paper MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV WJN and MJvdB revised the paper
References
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[2] Hendrikus J Dubbink Peggy N Atmodimedjo Johan M Kros Pim JFrench Marc Sanson Ahmed Idbaih Pieter Wesseling Roelien EntingWim Spliet Cees Tijssen Winand N M Dinjens Thierry Gorlia andMartin J van den Bent Molecular classification of anaplastic oligoden-droglioma using next-generation sequencing a report of the prospectiverandomized EORTC brain tumor group 26951 phase III trial Neuro-Oncology 18(3)388ndash400 March 2016 ISSN 1522-8517 URL https
doiorg101093neuoncnov182
[3] Jeanette E Eckel-Passow Daniel H Lachance Annette M Molinaro Kyle MWalsh Paul A Decker Hugues Sicotte Melike Pekmezci Terri Rice Matt LKosel Ivan V Smirnov Gobinda Sarkar Alissa A Caron Thomas M
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Kollmeyer Corinne E Praska Anisha R Chada Chandralekha Halder He-len M Hansen Lucie S McCoy Paige M Bracci Roxanne Marshall ShichunZheng Gerald F Reis Alexander R Pico Brian P OrsquoNeill Jan C BucknerCaterina Giannini Jason T Huse Arie Perry Tarik Tihan Mitchell SBerger Susan M Chang Michael D Prados Joseph Wiemels John KWiencke Margaret R Wrensch and Robert B Jenkins Glioma groupsbased on 1p19q IDH and TERT promoter mutations in tumors NewEngland Journal of Medicine 372(26)2499ndash2508 June 2015 ISSN 0028-4793 URL httpsdoiorg101056NEJMoa1407279
[4] David N Louis Arie Perry Guido Reifenberger Andreas von Deimling Do-minique Figarella-Branger Webster K Cavenee Hiroko Ohgaki Otmar DWiestler Paul Kleihues and David W Ellison The 2016 world health orga-nization classification of tumors of the central nervous system a summaryActa Neuropathologica 131(6)803ndash820 June 2016 ISSN 0001-6322 URLhttpsdoiorg101007s00401-016-1545-1
[5] Ching-Chang Chen Peng-Wei Hsu Tai-Wei Erich Wu Shih-Tseng LeeChen-Nen Chang Kuo-chen Wei Chih-Cheng Chuang Chieh-Tsai WuTai-Ngar Lui Yung-Hsin Hsu Tzu-Kang Lin Sai-Cheung Lee and Yin-Cheng Huang Stereotactic brain biopsy Single center retrospective anal-ysis of complications Clinical Neurology and Neurosurgery 111(10)835ndash839 December 2009 ISSN 0303-8467 URL httpsdoiorg101016
jclineuro200908013
[6] Robert J Jackson Gregory N Fuller Dima Abi-Said Frederick F LangZiya L Gokaslan Wei Ming Shi David M Wildrick and Raymond SawayaLimitations of stereotactic biopsy in the initial management of gliomasNeuro-Oncology 3(3)193ndash200 July 2001 ISSN 1522-8517 URL https
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[7] M Zhou J Scott B Chaudhury L Hall D Goldgof KW Yeom M IvY Ou J Kalpathy-Cramer S Napel R Gillies O Gevaert and R GatenbyRadiomics in brain tumor Image assessment quantitative feature de-scriptors and machine-learning approaches American Journal of Neu-roradiology 39(2)208ndash216 February 2018 ISSN 0195-6108 URL https
doiorg103174ajnrA5391
[8] Wenya Linda Bi Ahmed Hosny Matthew B Schabath Maryellen LGiger Nicolai J Birkbak Alireza Mehrtash Tavis Allison Omar ArnaoutChristopher Abbosh Ian F Dunn Raymond H Mak Rulla M TamimiClare M Tempany Charles Swanton Udo Hoffmann Lawrence H SchwartzRobert J Gillies Raymond Y Huang and Hugo J W L Aerts Artificialintelligence in cancer imaging Clinical challenges and applications CAA Cancer Journal for Clinicians 69(2)caac21552 February 2019 ISSN0007-9235 URL httpsdoiorg103322caac21552
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[9] Ahmad Chaddad Michael Jonathan Kucharczyk Paul Daniel SihamSabri Bertrand J Jean-Claude Tamim Niazi and Bassam AbdulkarimRadiomics in glioblastoma Current status and challenges facing clinicalimplementation Frontiers in Oncology 9374ndash374 May 2019 ISSN 2234-943X URL httpsdoiorg103389fonc201900374
[10] Marion Smits Imaging of oligodendroglioma The British Journal of Ra-diology 89(1060)20150857 April 2016 ISSN 0007-1285 URL https
doiorg101259bjr20150857
[11] Rachel L Delfanti David E Piccioni Jason Handwerker Naeim BahramiAnithaPriya Krishnan Roshan Karunamuni Jona A Hattangadi-GluthTyler M Seibert Ashwin Srikant Karra A Jones Vivian S Snyder An-ders M Dale Nathan S White Carrie R McDonald and Nikdokht FaridImaging correlates for the 2016 update on WHO classification of gradeIIIII gliomas implications for IDH 1p19q and ATRX status Journal ofNeuro-Oncology 135(3)601ndash609 December 2017 ISSN 0167-594X URLhttpsdoiorg101007s11060-017-2613-7
[12] Sonal Gore Tanay Chougule Jayant Jagtap Jitender Saini and MadhuraIngalhalikar A review of radiomics and deep predictive modeling in gliomacharacterization Academic Radiology July 2020 ISSN 1076-6332 URLhttpsdoiorg101016jacra202006016
[13] Hugo J W L Aerts Emmanuel Rios Velazquez Ralph T H LeijenaarChintan Parmar Patrick Grossmann Sara Carvalho Johan BussinkRene Monshouwer Benjamin Haibe-Kains Derek Rietveld Frank HoebersMichelle M Rietbergen C Rene Leemans Andre Dekker John Quacken-bush Robert J Gillies and Philippe Lambin Decoding tumour pheno-type by noninvasive imaging using a quantitative radiomics approach Na-ture Communications 5(1)4006 September 2014 ISSN 2041-1723 URLhttpsdoiorg101038ncomms5006
[14] Sarah Chihati and Djamel Gaceb A review of recent progress in deeplearning-based methods for MRI brain tumor segmentation In 202011th International Conference on Information and Communication Sys-tems (ICICS) pages 149ndash154 Institute of Electrical and Electronics Engi-neers (IEEE) April 2020 ISBN 9781728162270 URL httpsdoiorg
101109icics494692020239550
[15] Robert J Gillies Paul E Kinahan and Hedvig Hricak Radiomics Imagesare more than pictures they are data Radiology 278(2)563ndash577 Febru-ary 2016 ISSN 0033-8419 URL httpsdoiorg101148radiol
2015151169
[16] James H Thrall Xiang Li Quanzheng Li Cinthia Cruz Synho Do KeithDreyer and James Brink Artificial intelligence and machine learning in ra-diology Opportunities challenges pitfalls and criteria for success Journal
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of the American College of Radiology 15(3 Part B)504ndash508 March 2018ISSN 1546-1440 URL httpsdoiorg101016jjacr201712026
[17] Ahmed Hosny Chintan Parmar John Quackenbush Lawrence H Schwartzand Hugo J W L Aerts Artificial intelligence in radiology Nature ReviewsCancer 18(8)500ndash510 August 2018 ISSN 1474-175X URL https
doiorg101038s41568-018-0016-5
[18] Okan Kopuklu Neslihan Kose Ahmet Gunduz and Gerhard Rigoll Re-source efficient 3D convolutional neural networks In 2019 IEEECVFInternational Conference on Computer Vision Workshop (ICCVW) Insti-tute of Electrical and Electronics Engineers (IEEE) October 2019 ISBN9781728150239 URL httpsdoiorg101109iccvw201900240
[19] S C Thust S Heiland A Falini H R Jager A D Waldman P C SundgrenC Godi V K Katsaros A Ramos N Bargallo M W Vernooij T YousryM Bendszus and M Smits Glioma imaging in europe A survey of 220centres and recommendations for best clinical practice European Radiology28(8)3306ndash3317 August 2018 ISSN 0938-7994 URL httpsdoiorg
101007s00330-018-5314-5
[20] Christian F Freyschlag Sandro M Krieg Johannes Kerschbaumer DanielPinggera Marie-Therese Forster Dominik Cordier Marco Rossi GabrieleMiceli Alexandre Roux Andres Reyes Silvio Sarubbo Anja Smits JoannaSierpowska Pierre A Robe Geert-Jan Rutten Thomas Santarius TomaszMatys Marc Zanello Fabien Almairac Lydiane Mondot Asgeir S JakolaMaria Zetterling Adria Rofes Gord von Campe Remy Guillevin DanieleBagatto Vincent Lubrano Marion Rapp John Goodden Philip C De WittHamer Johan Pallud Lorenzo Bello Claudius Thome Hugues Duffauand Emmanuel Mandonnet Imaging practice in low-grade gliomas amongeuropean specialized centers and proposal for a minimum core of imagingJournal of Neuro-Oncology 139(3)699ndash711 September 2018 ISSN 0167-594X URL httpsdoiorg101007s11060-018-2916-3
[21] M Labussiere A Idbaih X-W Wang Y Marie B Boisselier C FaletS Paris J Laffaire C Carpentier E Criniere F Ducray S El HallaniK Mokhtari K Hoang-Xuan J-Y Delattre and M Sanson All the 1p19qcodeleted gliomas are mutated on IDH1 or IDH2 Neurology 74(23)1886ndash1890 June 2010 ISSN 0028-3878 URL httpsdoiorg101212WNL
0b013e3181e1cf3a
[22] David N Louis Pieter Wesseling Kenneth Aldape Daniel J Brat DavidCapper Ian A Cree Charles Eberhart Dominique Figarella-BrangerMaryam Fouladi Gregory N Fuller Caterina Giannini Christine Haber-ler Cynthia Hawkins Takashi Komori Johan M Kros HK Ng Brent AOrr Sung-Hye Park Werner Paulus Arie Perry Torsten Pietsch GuidoReifenberger Marc Rosenblum Brian Rous Felix Sahm Chitra SarkarDavid A Solomon Uri Tabori Martin J Bent Andreas Deimling Michael
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Weller Valerie A White and David W Ellison cIMPACT-NOW up-date 6 new entity and diagnostic principle recommendations of thecIMPACT-Utrecht meeting on future CNS tumor classification and grad-ing Brain Pathology 30(4)844ndash856 July 2020 ISSN 1015-6305 URLhttpsdoiorg101111bpa12832
[23] Zhenyu Tang Yuyun Xu Zhicheng Jiao Junfeng Lu Lei Jin Abudumi-jiti Aibaidula Jinsong Wu Qian Wang Han Zhang and Dinggang ShenPre-operative overall survival time prediction for glioblastoma patients us-ing deep learning on both imaging phenotype and genotype In Ding-gang Shen Tianming Liu Terry M Peters Lawrence H Staib CarolineEssert Sean Zhou Pew-Thian Yap and Ali Khan editors Lecture Notesin Computer Science pages 415ndash422 Springer Science and Business Me-dia LLC 2019 ISBN 9783030322380 URL httpsdoiorg101007
978-3-030-32239-7_46
[24] Zhiyuan Xue Bowen Xin Dingqian Wang and Xiuying Wang Radiomics-enhanced multi-task neural network for non-invasive glioma subtypingand segmentation In Hassan Mohy-ud Din and Saima Rathore editorsRadiomics and Radiogenomics in Neuro-oncology pages 81ndash90 SpringerScience and Business Media LLC 2020 ISBN 9783030401238 URLhttpsdoiorg101007978-3-030-40124-5_9
[25] Milan Decuyper Stijn Bonte Karel Deblaere and Roel Van Holen Au-tomated MRI based pipeline for glioma segmentation and prediction ofgrade IDH mutation and 1p19q co-deletion 2020 Preprint at https
arxivorgabs200511965
[26] Stefania Rizzo Francesca Botta Sara Raimondi Daniela Origgi Cris-tiana Fanciullo Alessio Giuseppe Morganti and Massimo Bellomi Ra-diomics the facts and the challenges of image analysis European Ra-diology Experimental 2(1)36 December 2018 ISSN 2509-9280 URLhttpsdoiorg101186s41747-018-0068-z
[27] Philipp Lohmann Norbert Galldiks Martin Kocher Alexander HeinzelChristian P Filss Carina Stegmayr Felix M Mottaghy Gereon R FinkN Jon Shah and Karl-Josef Langen Radiomics in neuro-oncology Basicsworkflow and applications Methods June 2020 ISSN 1046-2023 URLhttpsdoiorg101016jymeth202006003
[28] Stephen S F Yip and Hugo J W L Aerts Applications and limitationsof radiomics Physics in Medicine and Biology 61(13)R150ndashR166 July2016 ISSN 0031-9155 URL httpsdoiorg1010880031-915561
13r150
[29] Annika Malmstrom Ma lgorzata Lysiak Bjarne Winther Kristensen Eliz-abeth Hovey Roger Henriksson and Peter Soderkvist Do we really knowwho has an MGMT methylated glioma Results of an international survey
41
regarding use of MGMT analyses for glioma Neuro-Oncology Practice 7(1)68ndash76 09 2019 ISSN 2054-2577 URL httpsdoiorg101093
nopnpz039
[30] Takahiro Sasaki Manabu Kinoshita Koji Fujita Junya Fukai NobuhideHayashi Yuji Uematsu Yoshiko Okita Masahiro Nonaka Shusuke Mo-riuchi Takehiro Uda Naohiro Tsuyuguchi Hideyuki Arita Kanji MoriKenichi Ishibashi Koji Takano Namiko Nishida Tomoko Shofuda EmaYoshioka Daisuke Kanematsu Yoshinori Kodama Masayuki ManoNaoyuki Nakao and Yonehiro Kanemura Radiomics and MGMT pro-moter methylation for prognostication of newly diagnosed glioblastomaScientific Reports 9(1)14435 December 2019 ISSN 2045-2322 URLhttpsdoiorg101038s41598-019-50849-y
[31] A Gupta A Prager RJ Young W Shi AMP Omuro and JJ GraberDiffusion-weighted MR imaging and MGMT methylation status in glioblas-toma A reappraisal of the role of preoperative quantitative ADC measure-ments American Journal of Neuroradiology 34(1)E10ndashE11 January 2013ISSN 0195-6108 URL httpsdoiorg103174ajnrA3467
[32] JA Carrillo A Lai PL Nghiemphu HJ Kim HS Phillips S KharbandaP Moftakhar S Lalaezari W Yong BM Ellingson TF Cloughesy andWB Pope Relationship between tumor enhancement edema IDH1 muta-tional status MGMT promoter methylation and survival in glioblastomaAmerican Journal of Neuroradiology 33(7)1349ndash1355 August 2012 ISSN0195-6108 URL httpsdoiorg103174ajnrA2950
[33] Vilde Elisabeth Mikkelsen Hong Yan Dai Anne Line Stensjoslashen Erik Mag-nus Berntsen Oslashyvind Salvesen Ole Solheim and Sverre Helge TorpMGMT promoter methylation status is not related to histological or radio-logical features in IDH wild-type glioblastomas Journal of Neuropathologyamp Experimental Neurology 79(8)855ndash862 August 2020 ISSN 0022-3069URL httpsdoiorg101093jnennlaa060
[34] Martin J van den Bent Interobserver variation of the histopathologicaldiagnosis in clinical trials on glioma a clinicianrsquos perspective Acta Neu-ropathologica 120(3)297ndash304 September 2010 ISSN 1432-0533 URLhttpsdoiorg101007s00401-010-0725-7
[35] Ji Eun Park Ho Sung Kim Youngheun Jo Roh-Eul Yoo Seung Hong ChoiSoo Jung Nam and Jeong Hoon Kim Radiomics prognostication modelin glioblastoma using diffusion- and perfusion-weighted MRI ScientificReports 10(1)4250 December 2020 ISSN 2045-2322 URL httpsdoi
org101038s41598-020-61178-w
[36] Minjae Kim So Yeong Jung Ji Eun Park Yeongheun Jo Seo YoungPark Soo Jung Nam Jeong Hoon Kim and Ho Sung Kim Diffusion-and perfusion-weighted MRI radiomics model may predict isocitrate dehy-drogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade
42
glioma European Radiology 30(4)2142ndash2151 April 2020 ISSN 0938-7994URL httpsdoiorg101007s00330-019-06548-3
[37] M Visser DMJ Muller RJM van Duijn M Smits N Verburg EJ HendriksRJA Nabuurs JCJ Bot RS Eijgelaar M Witte MB van Herk F BarkhofPC de WittHamer and JC de Munck Inter-rater agreement in glioma seg-mentations on longitudinal MRI NeuroImage Clinical 22101727 2019ISSN 2213-1582 URL httpsdoiorg101016jnicl2019101727
[38] Kenneth Clark Bruce Vendt Kirk Smith John Freymann Justin KirbyPaul Koppel Stephen Moore Stanley Phillips David Maffitt MichaelPringle Lawrence Tarbox and Fred Prior The cancer imaging archive(TCIA) Maintaining and operating a public information repository Jour-nal of Digital Imaging 26(6)1045ndash1057 December 2013 ISSN 0897-1889URL httpsdoiorg101007s10278-013-9622-7
[39] Lisa Scarpace Adam E Flanders Rajan Jain Tom Mikkelsen and David WAndrews Data from REMBRANDT The Cancer Imaging Archive 2015URL httpsdoiorg107937K9TCIA2015588OZUZB
[40] National Cancer Institute Clinical Proteomic Tumor Analysis Consortium(CPTAC) Radiology data from the clinical proteomic tumor analysisconsortium glioblastoma multiforme CPTAC-GBM collection The CancerImaging Archive 2018 URL httpsdoiorg107937k9tcia2018
3rje41q1
[41] Nameeta Shah Xu Feng Michael Lankerovich Ralph B Puchalski andBart Keogh Data from Ivy GAP The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016XLwaN6nL
[42] Ralph B Puchalski Nameeta Shah Jeremy Miller Rachel Dalley Steve RNomura Jae-Guen Yoon Kimberly A Smith Michael Lankerovich DarrenBertagnolli Kris Bickley Andrew F Boe Krissy Brouner Stephanie But-ler Shiella Caldejon Mike Chapin Suvro Datta Nick Dee Tsega DestaTim Dolbeare Nadezhda Dotson Amanda Ebbert David Feng Xu FengMichael Fisher Garrett Gee Jeff Goldy Lindsey Gourley Benjamin WGregor Guangyu Gu Nika Hejazinia John Hohmann Parvinder HothiRobert Howard Kevin Joines Ali Kriedberg Leonard Kuan Chris LauFelix Lee Hwahyung Lee Tracy Lemon Fuhui Long Naveed Mastan ErikaMott Chantal Murthy Kiet Ngo Eric Olson Melissa Reding Zack RileyDavid Rosen David Sandman Nadiya Shapovalova Clifford R Slaughter-beck Andrew Sodt Graham Stockdale Aaron Szafer Wayne WakemanPaul E Wohnoutka Steven J White Don Marsh Robert C RostomilyLydia Ng Chinh Dang Allan Jones Bart Keogh Haley R GittlemanJill S Barnholtz-Sloan Patrick J Cimino Megha S Uppin C Dirk KeeneFarrokh R Farrokhi Justin D Lathia Michael E Berens Antonio IavaroneAmy Bernard Ed Lein John W Phillips Steven W Rostad Charles Cobbs
43
Michael J Hawrylycz and Greg D Foltz An anatomic transcriptional at-las of human glioblastoma Science 360(6389)660ndash663 May 2018 ISSN0036-8075 URL httpsdoiorg101126scienceaaf2666
[43] Kathleen Schmainda and Melissa Prah Data from Brain-Tumor-Progression The Cancer Imaging Archive 2018 URL httpsdoiorg
107937K9TCIA201815quzvnb
[44] B H Menze A Jakab S Bauer J Kalpathy-Cramer K FarahaniJ Kirby Y Burren N Porz J Slotboom R Wiest L Lanczi E GerstnerM Weber T Arbel B B Avants N Ayache P Buendia D L CollinsN Cordier J J Corso A Criminisi T Das H Delingette C Demi-ralp C R Durst M Dojat S Doyle J Festa F Forbes E GeremiaB Glocker P Golland X Guo A Hamamci K M IftekharuddinR Jena N M John E Konukoglu D Lashkari J A Mariz R MeierS Pereira D Precup S J Price T R Raviv S M S Reza M RyanD Sarikaya L Schwartz H Shin J Shotton C A Silva N SousaN K Subbanna G Szekely T J Taylor O M Thomas N J Tusti-son G Unal F Vasseur M Wintermark D H Ye L Zhao B ZhaoD Zikic M Prastawa M Reyes and K Van Leemput The mul-timodal brain tumor image segmentation benchmark (BRATS) IEEETransactions on Medical Imaging 34(10)1993ndash2024 2015 URL https
doiorg101109TMI20142377694
[45] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin S Kirby John B Freymann Keyvan Farahani and ChristosDavatzikos Advancing the cancer genome atlas glioma MRI collectionswith expert segmentation labels and radiomic features Scientific Data 4(1)170117 December 2017 ISSN 2052-4463 URL httpsdoiorg10
1038sdata2017117
[46] Spyridon Bakas Mauricio Reyes Andras Jakab Stefan Bauer MarkusRempfler Alessandro Crimi Russell Takeshi Shinohara Christoph BergerSung Min Ha Martin Rozycki Marcel Prastawa Esther Alberts JanaLipkova John Freymann Justin Kirby Michel Bilello Hassan Fathallah-Shaykh Roland Wiest Jan Kirschke Benedikt Wiestler Rivka ColenAikaterini Kotrotsou Pamela Lamontagne Daniel Marcus MikhailMilchenko Arash Nazeri Marc-Andre Weber Abhishek Mahajan UjjwalBaid Elizabeth Gerstner Dongjin Kwon Gagan Acharya Manu Agar-wal Mahbubul Alam Alberto Albiol Antonio Albiol Francisco J AlbiolVarghese Alex Nigel Allinson Pedro H A Amorim Abhijit AmrutkarGanesh Anand Simon Andermatt Tal Arbel Pablo Arbelaez Aaron Av-ery Muneeza Azmat Pranjal B W Bai Subhashis Banerjee Bill BarthThomas Batchelder Kayhan Batmanghelich Enzo Battistella AndrewBeers Mikhail Belyaev Martin Bendszus Eze Benson Jose Bernal Ha-landur Nagaraja Bharath George Biros Sotirios Bisdas James BrownMariano Cabezas Shilei Cao Jorge M Cardoso Eric N Carver Adria
44
Casamitjana Laura Silvana Castillo Marcel Cata Philippe Cattin Al-bert Cerigues Vinicius S Chagas Siddhartha Chandra Yi-Ju ChangShiyu Chang Ken Chang Joseph Chazalon Shengcong Chen Wei ChenJefferson W Chen Zhaolin Chen Kun Cheng Ahana Roy ChoudhuryRoger Chylla Albert Clerigues Steven Colleman Ramiro German Ro-driguez Colmeiro Marc Combalia Anthony Costa Xiaomeng Cui Zhen-zhen Dai Lutao Dai Laura Alexandra Daza Eric Deutsch ChangxingDing Chao Dong Shidu Dong Wojciech Dudzik Zach Eaton-Rosen GaryEgan Guilherme Escudero Theo Estienne Richard Everson JonathanFabrizio Yong Fan Longwei Fang Xue Feng Enzo Ferrante Lucas Fi-don Martin Fischer Andrew P French Naomi Fridman Huan Fu DavidFuentes Yaozong Gao Evan Gates David Gering Amir Gholami WilliGierke Ben Glocker Mingming Gong Sandra Gonzalez-Villa T Gros-ges Yuanfang Guan Sheng Guo Sudeep Gupta Woo-Sup Han Il SongHan Konstantin Harmuth Huiguang He Aura Hernandez-Sabate EvelynHerrmann Naveen Himthani Winston Hsu Cheyu Hsu Xiaojun Hu Xi-aobin Hu Yan Hu Yifan Hu Rui Hua Teng-Yi Huang Weilin HuangSabine Van Huffel Quan Huo Vivek HV Khan M Iftekharuddin FabianIsensee Mobarakol Islam Aaron S Jackson Sachin R Jambawalikar An-drew Jesson Weijian Jian Peter Jin V Jeya Maria Jose Alain JungoB Kainz Konstantinos Kamnitsas Po-Yu Kao Ayush Karnawat ThomasKellermeier Adel Kermi Kurt Keutzer Mohamed Tarek Khadir Mahen-dra Khened Philipp Kickingereder Geena Kim Nik King Haley KnappUrspeter Knecht Lisa Kohli Deren Kong Xiangmao Kong Simon Kop-pers Avinash Kori Ganapathy Krishnamurthi Egor Krivov Piyush Ku-mar Kaisar Kushibar Dmitrii Lachinov Tryphon Lambrou Joon LeeChengen Lee Yuehchou Lee M Lee Szidonia Lefkovits Laszlo LefkovitsJames Levitt Tengfei Li Hongwei Li Wenqi Li Hongyang Li XiaochuanLi Yuexiang Li Heng Li Zhenye Li Xiaoyu Li Zeju Li XiaoGang LiWenqi Li Zheng-Shen Lin Fengming Lin Pietro Lio Chang Liu Bo-qiang Liu Xiang Liu Mingyuan Liu Ju Liu Luyan Liu Xavier LladoMarc Moreno Lopez Pablo Ribalta Lorenzo Zhentai Lu Lin Luo ZhigangLuo Jun Ma Kai Ma Thomas Mackie Anant Madabushi Issam Mah-moudi Klaus H Maier-Hein Pradipta Maji CP Mammen Andreas MangB S Manjunath Michal Marcinkiewicz S McDonagh Stephen McKennaRichard McKinley Miriam Mehl Sachin Mehta Raghav Mehta RaphaelMeier Christoph Meinel Dorit Merhof Craig Meyer Robert Miller Sush-mita Mitra Aliasgar Moiyadi David Molina-Garcia Miguel A B Mon-teiro Grzegorz Mrukwa Andriy Myronenko Jakub Nalepa Thuyen NgoDong Nie Holly Ning Chen Niu Nicholas K Nuechterlein Eric Oer-mann Arlindo Oliveira Diego D C Oliveira Arnau Oliver AlexanderF I Osman Yu-Nian Ou Sebastien Ourselin Nikos Paragios Moo SungPark Brad Paschke J Gregory Pauloski Kamlesh Pawar Nick PawlowskiLinmin Pei Suting Peng Silvio M Pereira Julian Perez-Beteta Vic-tor M Perez-Garcia Simon Pezold Bao Pham Ashish Phophalia GemmaPiella G N Pillai Marie Piraud Maxim Pisov Anmol Popli Michael P
45
Pound Reza Pourreza Prateek Prasanna Vesna Prkovska Tony P Prid-more Santi Puch Elodie Puybareau Buyue Qian Xu Qiao MartinRajchl Swapnil Rane Michael Rebsamen Hongliang Ren Xuhua RenKarthik Revanuru Mina Rezaei Oliver Rippel Luis Carlos Rivera Char-lotte Robert Bruce Rosen Daniel Rueckert Mohammed Safwan MostafaSalem Joaquim Salvi Irina Sanchez Irina Sanchez Heitor M Santos Em-mett Sartor Dawid Schellingerhout Klaudius Scheufele Matthew R ScottArtur A Scussel Sara Sedlar Juan Pablo Serrano-Rubio N Jon ShahNameetha Shah Mazhar Shaikh B Uma Shankar Zeina Shboul HaipengShen Dinggang Shen Linlin Shen Haocheng Shen Varun Shenoy FengShi Hyung Eun Shin Hai Shu Diana Sima M Sinclair Orjan SmedbyJames M Snyder Mohammadreza Soltaninejad Guidong Song MehulSoni Jean Stawiaski Shashank Subramanian Li Sun Roger Sun JiaweiSun Kay Sun Yu Sun Guoxia Sun Shuang Sun Yannick R Suter Las-zlo Szilagyi Sanjay Talbar Dacheng Tao Dacheng Tao Zhongzhao TengSiddhesh Thakur Meenakshi H Thakur Sameer Tharakan Pallavi Ti-wari Guillaume Tochon Tuan Tran Yuhsiang M Tsai Kuan-Lun TsengTran Anh Tuan Vadim Turlapov Nicholas Tustison Maria VakalopoulouSergi Valverde Rami Vanguri Evgeny Vasiliev Jonathan Ventura LuisVera Tom Vercauteren C A Verrastro Lasitha Vidyaratne Veronica Vi-laplana Ajeet Vivekanandan Guotai Wang Qian Wang Chiatse J WangWeichung Wang Duo Wang Ruixuan Wang Yuanyuan Wang ChunliangWang Guotai Wang Ning Wen Xin Wen Leon Weninger Wolfgang WickShaocheng Wu Qiang Wu Yihong Wu Yong Xia Yanwu Xu XiaowenXu Peiyuan Xu Tsai-Ling Yang Xiaoping Yang Hao-Yu Yang JunlinYang Haojin Yang Guang Yang Hongdou Yao Xujiong Ye ChangchangYin Brett Young-Moxon Jinhua Yu Xiangyu Yue Songtao Zhang AngelaZhang Kun Zhang Xuejie Zhang Lichi Zhang Xiaoyue Zhang YazhuoZhang Lei Zhang Jianguo Zhang Xiang Zhang Tianhao Zhang SichengZhao Yu Zhao Xiaomei Zhao Liang Zhao Yefeng Zheng Liming ZhongChenhong Zhou Xiaobing Zhou Fan Zhou Hongtu Zhu Jin Zhu YingZhuge Weiwei Zong Jayashree Kalpathy-Cramer Keyvan Farahani Chris-tos Davatzikos Koen van Leemput and Bjoern Menze Identifying the bestmachine learning algorithms for brain tumor segmentation progression as-sessment and overall survival prediction in the BRATS challenge 2018Preprint at httpsarxivorgabs181102629
[47] Nancy Pedano Adam E Flanders Lisa Scarpace Tom Mikkelsen Jen-nifer M Eschbacher Beth Hermes Victor Sisneros Jill Barnholtz-Sloanand Quinn Ostrom Radiology data from the cancer genome atlas lowgrade glioma [TCGA-LGG] collection The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016L4LTD3TK
[48] Lisa Scarpace Tom Mikkelsen Soonmee Cha Sujaya Rao SangeetaTekchandani David Gutman Joel H Saltz Bradley J Erickson NancyPedano Adam E Flanders Jill Barnholtz-Sloan Quinn Ostrom DanielBarboriak and Laura J Pierce Radiology data from the cancer genome
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atlas glioblastoma multiforme [TCGA-GBM] collection The Cancer Imag-ing Archive 2016 URL httpsdoiorg107937K9TCIA2016
RNYFUYE9
[49] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin Kirby John Freymann Keyvan Farahani and ChristosDavatzikos Segmentation labels and radiomic features for the pre-operativescans of the TCGA-LGG collection [data set] The Cancer Imaging Archive2017 URL httpsdoiorg107937K9TCIA2017GJQ7R0EF
[50] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello Mar-tin Rozycki Justin Kirby John Freymann Keyvan Farahani and Chris-tos Davatzikos Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [data set] The CancerImaging Archive 2017 URL httpsdoiorg107937K9TCIA2017
KLXWJJ1Q
[51] Xiangrui Li Paul S Morgan John Ashburner Jolinda Smith and Christo-pher Rorden The first step for neuroimaging data analysis DICOM toNIfTI conversion Journal of Neuroscience Methods 26447ndash56 May 2016ISSN 0165-0270 URL httpsdoiorg101016jjneumeth201603
001
[52] Vladimir Fonov Alan C Evans Kelly Botteron C Robert Almli Robert CMcKinstry and D Louis Collins Unbiased average age-appropriate at-lases for pediatric studies NeuroImage 54(1)313ndash327 January 2011ISSN 1053-8119 URL httpsdoiorg101016jneuroimage2010
07033
[53] VS Fonov AC Evans RC McKinstry CR Almli and DL Collins Unbiasednonlinear average age-appropriate brain templates from birth to adulthoodNeuroImage 47S102 July 2009 ISSN 1053-8119 URL httpsdoi
org101016S1053-8119(09)70884-5 Organization for Human BrainMapping 2009 Annual Meeting
[54] S Klein M Staring K Murphy MA Viergever and J Pluim elastix Atoolbox for intensity-based medical image registration IEEE Transactionson Medical Imaging 29(1)196ndash205 January 2010 ISSN 0278-0062 URLhttpsdoiorg101109TMI20092035616
[55] Denis Shamonin Fast parallel image registration on CPU and GPU fordiagnostic classification of alzheimerrsquos disease Frontiers in Neuroinfor-matics 750 2013 ISSN 1662-5196 URL httpsdoiorg103389
fninf201300050
[56] Bradley C Lowekamp David T Chen Luis Ibanez and Daniel Blezek Thedesign of SimpleITK Frontiers in Neuroinformatics 745 2013 ISSN1662-5196 URL httpsdoiorg103389fninf201300045
47
[57] Fabian Isensee Marianne Schell Irada Pflueger Gianluca Brugnara DavidBonekamp Ulf Neuberger Antje Wick Heinz-Peter Schlemmer SabineHeiland Wolfgang Wick Martin Bendszus Klaus H Maier-Hein andPhilipp Kickingereder Automated brain extraction of multisequence MRIusing artificial neural networks Human Brain Mapping 40(17)4952ndash4964December 2019 ISSN 1065-9471 URL httpsdoiorg101002hbm
24750
[58] Olaf Ronneberger Invited talk U-net convolutional networks for biomed-ical image segmentation In geb Fritzsche K Maier-Hein geb Lehmann TDeserno Heinz Handels and Thomas Tolxdorff editors Bildverarbeitungfur die Medizin 2017 Informatik aktuell pages 3ndash3 Springer Scienceand Business Media LLC 2017 ISBN 9783662543443 URL https
doiorg101007978-3-662-54345-0_3
[59] Martın Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy DavisJeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving MichaelIsard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry MooreDerek G Murray Benoit Steiner Paul Tucker Vijay Vasudevan PeteWarden Martin Wicke Yuan Yu and Xiaoqiang Zheng Tensor-Flow A system for large-scale machine learning In 12th USENIXSymposium on Operating Systems Design and Implementation (OSDI16) pages 265ndash283 USENIX Association November 2016 ISBN978-1-931971-33-1 URL httpswwwusenixorgconferenceosdi16
technical-sessionspresentationabadi
[60] Dipankar Das Naveen Mellempudi Dheevatsa Mudigere Dhiraj KalamkarSasikanth Avancha Kunal Banerjee Srinivas Sridharan KarthikVaidyanathan Bharat Kaul Evangelos Georganas Alexander HeineckePradeep Dubey Jesus Corbal Nikita Shustrov Roma Dubtsov EvaristFomenko and Vadim Pirogov Mixed precision training of convolutionalneural networks using integer operations In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=H135uzZ0-
[61] Samuel L Smith Pieter-Jan Kindermans and Quoc V Le Donrsquot decaythe learning rate increase the batch size In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=B1Yy1BxCZ
[62] Cliff Woolley NCCL accelarated multi-GPU collective communicationsURL httpsimagesnvidiacomeventssc15pdfsNCCL-Woolley
pdf Accessed on 2020-09-30
[63] Ilya Loshchilov and Frank Hutter Decoupled weight decay regularization2017 Preprint at httpsarxivorgabs171105101
48
[64] F Pedregosa G Varoquaux A Gramfort V Michel B Thirion O GriselM Blondel P Prettenhofer R Weiss V Dubourg J Vanderplas A Pas-sos D Cournapeau M Brucher M Perrot and E Duchesnay Scikit-learn Machine learning in python Journal of Machine Learning Re-search 122825ndash2830 2011 URL httpswwwjmlrorgpapersv12
pedregosa11ahtml
[65] Abdel Aziz Taha and Allan Hanbury Metrics for evaluating 3d medicalimage segmentation analysis selection and tool BMC Medical Imaging15(1)29 August 2015 ISSN 1471-2342 URL httpsdoiorg101186
s12880-015-0068-x
[66] Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viegas and MartinWattenberg SmoothGrad removing noise by adding noise 2017 Preprintat httpsarxivorgabs170603825
[67] Alaa Tharwat Classification assessment methods Applied Computing andInformatics 2018 ISSN 2210-8327 URL httpsdoiorg101016j
aci201808003
[68] David J Hand and Robert J Till A simple generalisation of the areaunder the ROC curve for multiple class classification problems MachineLearning 45(2)171ndash186 November 2001 ISSN 1573-0565 URL https
doiorg101023A1010920819831
49
Data Collection | Patient | IDH_mutated | 1p19q_codeleted | Grade | |||||
BTumorP | PGBM-001 | -1 | -1 | -1 | |||||
BTumorP | PGBM-002 | -1 | -1 | -1 | |||||
BTumorP | PGBM-003 | -1 | -1 | -1 | |||||
BTumorP | PGBM-004 | -1 | -1 | -1 | |||||
BTumorP | PGBM-005 | -1 | -1 | -1 | |||||
BTumorP | PGBM-006 | -1 | -1 | -1 | |||||
BTumorP | PGBM-007 | -1 | -1 | -1 | |||||
BTumorP | PGBM-008 | -1 | -1 | -1 | |||||
BTumorP | PGBM-009 | -1 | -1 | -1 | |||||
BTumorP | PGBM-010 | -1 | -1 | -1 | |||||
BTumorP | PGBM-011 | -1 | -1 | -1 | |||||
BTumorP | PGBM-012 | -1 | -1 | -1 | |||||
BTumorP | PGBM-013 | -1 | -1 | -1 | |||||
BTumorP | PGBM-014 | -1 | -1 | -1 | |||||
BTumorP | PGBM-015 | -1 | -1 | -1 | |||||
BTumorP | PGBM-016 | -1 | -1 | -1 | |||||
BTumorP | PGBM-017 | -1 | -1 | -1 | |||||
BTumorP | PGBM-018 | -1 | -1 | -1 | |||||
BTumorP | PGBM-019 | -1 | -1 | -1 | |||||
BTumorP | PGBM-020 | -1 | -1 | -1 | |||||
BraTS | 2013_0 | -1 | -1 | -1 | |||||
BraTS | 2013_10 | -1 | -1 | -1 | |||||
BraTS | 2013_11 | -1 | -1 | -1 | |||||
BraTS | 2013_12 | -1 | -1 | -1 | |||||
BraTS | 2013_13 | -1 | -1 | -1 | |||||
BraTS | 2013_14 | -1 | -1 | -1 | |||||
BraTS | 2013_15 | -1 | -1 | -1 | |||||
BraTS | 2013_16 | -1 | -1 | -1 | |||||
BraTS | 2013_17 | -1 | -1 | -1 | |||||
BraTS | 2013_18 | -1 | -1 | -1 | |||||
BraTS | 2013_19 | -1 | -1 | -1 | |||||
BraTS | 2013_1 | -1 | -1 | -1 | |||||
BraTS | 2013_20 | -1 | -1 | -1 | |||||
BraTS | 2013_21 | -1 | -1 | -1 | |||||
BraTS | 2013_22 | -1 | -1 | -1 | |||||
BraTS | 2013_23 | -1 | -1 | -1 | |||||
BraTS | 2013_24 | -1 | -1 | -1 | |||||
BraTS | 2013_25 | -1 | -1 | -1 | |||||
BraTS | 2013_26 | -1 | -1 | -1 | |||||
BraTS | 2013_27 | -1 | -1 | -1 | |||||
BraTS | 2013_28 | -1 | -1 | -1 | |||||
BraTS | 2013_29 | -1 | -1 | -1 | |||||
BraTS | 2013_2 | -1 | -1 | -1 | |||||
BraTS | 2013_3 | -1 | -1 | -1 | |||||
BraTS | 2013_4 | -1 | -1 | -1 | |||||
BraTS | 2013_5 | -1 | -1 | -1 | |||||
BraTS | 2013_6 | -1 | -1 | -1 | |||||
BraTS | 2013_7 | -1 | -1 | -1 | |||||
BraTS | 2013_8 | -1 | -1 | -1 | |||||
BraTS | 2013_9 | -1 | -1 | -1 | |||||
BraTS | CBICA_AAB | -1 | -1 | -1 | |||||
BraTS | CBICA_AAG | -1 | -1 | -1 | |||||
BraTS | CBICA_AAL | -1 | -1 | -1 | |||||
BraTS | CBICA_AAP | -1 | -1 | -1 | |||||
BraTS | CBICA_ABB | -1 | -1 | -1 | |||||
BraTS | CBICA_ABE | -1 | -1 | -1 | |||||
BraTS | CBICA_ABM | -1 | -1 | -1 | |||||
BraTS | CBICA_ABN | -1 | -1 | -1 | |||||
BraTS | CBICA_ABO | -1 | -1 | -1 | |||||
BraTS | CBICA_ABY | -1 | -1 | -1 | |||||
BraTS | CBICA_ALN | -1 | -1 | -1 | |||||
BraTS | CBICA_ALU | -1 | -1 | -1 | |||||
BraTS | CBICA_ALX | -1 | -1 | -1 | |||||
BraTS | CBICA_AME | -1 | -1 | -1 | |||||
BraTS | CBICA_AMH | -1 | -1 | -1 | |||||
BraTS | CBICA_ANG | -1 | -1 | -1 | |||||
BraTS | CBICA_ANI | -1 | -1 | -1 | |||||
BraTS | CBICA_ANP | -1 | -1 | -1 | |||||
BraTS | CBICA_ANV | -1 | -1 | -1 | |||||
BraTS | CBICA_ANZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AOC | -1 | -1 | -1 | |||||
BraTS | CBICA_AOD | -1 | -1 | -1 | |||||
BraTS | CBICA_AOH | -1 | -1 | -1 | |||||
BraTS | CBICA_AOO | -1 | -1 | -1 | |||||
BraTS | CBICA_AOP | -1 | -1 | -1 | |||||
BraTS | CBICA_AOS | -1 | -1 | -1 | |||||
BraTS | CBICA_AOZ | -1 | -1 | -1 | |||||
BraTS | CBICA_APK | -1 | -1 | -1 | |||||
BraTS | CBICA_APR | -1 | -1 | -1 | |||||
BraTS | CBICA_APY | -1 | -1 | -1 | |||||
BraTS | CBICA_APZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQA | -1 | -1 | -1 | |||||
BraTS | CBICA_AQD | -1 | -1 | -1 | |||||
BraTS | CBICA_AQG | -1 | -1 | -1 | |||||
BraTS | CBICA_AQJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQN | -1 | -1 | -1 | |||||
BraTS | CBICA_AQO | -1 | -1 | -1 | |||||
BraTS | CBICA_AQP | -1 | -1 | -1 | |||||
BraTS | CBICA_AQQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQR | -1 | -1 | -1 | |||||
BraTS | CBICA_AQT | -1 | -1 | -1 | |||||
BraTS | CBICA_AQU | -1 | -1 | -1 | |||||
BraTS | CBICA_AQV | -1 | -1 | -1 | |||||
BraTS | CBICA_AQY | -1 | -1 | -1 | |||||
BraTS | CBICA_AQZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ARF | -1 | -1 | -1 | |||||
BraTS | CBICA_ARW | -1 | -1 | -1 | |||||
BraTS | CBICA_ARZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ASA | -1 | -1 | -1 | |||||
BraTS | CBICA_ASE | -1 | -1 | -1 | |||||
BraTS | CBICA_ASF | -1 | -1 | -1 | |||||
BraTS | CBICA_ASG | -1 | -1 | -1 | |||||
BraTS | CBICA_ASH | -1 | -1 | -1 | |||||
BraTS | CBICA_ASK | -1 | -1 | -1 | |||||
BraTS | CBICA_ASN | -1 | -1 | -1 | |||||
BraTS | CBICA_ASO | -1 | -1 | -1 | |||||
BraTS | CBICA_ASR | -1 | -1 | -1 | |||||
BraTS | CBICA_ASU | -1 | -1 | -1 | |||||
BraTS | CBICA_ASV | -1 | -1 | -1 | |||||
BraTS | CBICA_ASW | -1 | -1 | -1 | |||||
BraTS | CBICA_ASY | -1 | -1 | -1 | |||||
BraTS | CBICA_ATB | -1 | -1 | -1 | |||||
BraTS | CBICA_ATD | -1 | -1 | -1 | |||||
BraTS | CBICA_ATF | -1 | -1 | -1 | |||||
BraTS | CBICA_ATN | -1 | -1 | -1 | |||||
BraTS | CBICA_ATP | -1 | -1 | -1 | |||||
BraTS | CBICA_ATV | -1 | -1 | -1 | |||||
BraTS | CBICA_ATX | -1 | -1 | -1 | |||||
BraTS | CBICA_AUA | -1 | -1 | -1 | |||||
BraTS | CBICA_AUN | -1 | -1 | -1 | |||||
BraTS | CBICA_AUQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AUR | -1 | -1 | -1 | |||||
BraTS | CBICA_AUW | -1 | -1 | -1 | |||||
BraTS | CBICA_AUX | -1 | -1 | -1 | |||||
BraTS | CBICA_AVB | -1 | -1 | -1 | |||||
BraTS | CBICA_AVF | -1 | -1 | -1 | |||||
BraTS | CBICA_AVG | -1 | -1 | -1 | |||||
BraTS | CBICA_AVJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AVT | -1 | -1 | -1 | |||||
BraTS | CBICA_AVV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWG | -1 | -1 | -1 | |||||
BraTS | CBICA_AWH | -1 | -1 | -1 | |||||
BraTS | CBICA_AWI | -1 | -1 | -1 | |||||
BraTS | CBICA_AWV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWX | -1 | -1 | -1 | |||||
BraTS | CBICA_AXJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXL | -1 | -1 | -1 | |||||
BraTS | CBICA_AXM | -1 | -1 | -1 | |||||
BraTS | CBICA_AXN | -1 | -1 | -1 | |||||
BraTS | CBICA_AXO | -1 | -1 | -1 | |||||
BraTS | CBICA_AXQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXW | -1 | -1 | -1 | |||||
BraTS | CBICA_AYA | -1 | -1 | -1 | |||||
BraTS | CBICA_AYC | -1 | -1 | -1 | |||||
BraTS | CBICA_AYG | -1 | -1 | -1 | |||||
BraTS | CBICA_AYI | -1 | -1 | -1 | |||||
BraTS | CBICA_AYU | -1 | -1 | -1 | |||||
BraTS | CBICA_AYW | -1 | -1 | -1 | |||||
BraTS | CBICA_AZD | -1 | -1 | -1 | |||||
BraTS | CBICA_AZH | -1 | -1 | -1 | |||||
BraTS | CBICA_BAN | -1 | -1 | -1 | |||||
BraTS | CBICA_BAP | -1 | -1 | -1 | |||||
BraTS | CBICA_BAX | -1 | -1 | -1 | |||||
BraTS | CBICA_BBG | -1 | -1 | -1 | |||||
BraTS | CBICA_BCF | -1 | -1 | -1 | |||||
BraTS | CBICA_BCL | -1 | -1 | -1 | |||||
BraTS | CBICA_BDK | -1 | -1 | -1 | |||||
BraTS | CBICA_BEM | -1 | -1 | -1 | |||||
BraTS | CBICA_BFB | -1 | -1 | -1 | |||||
BraTS | CBICA_BFP | -1 | -1 | -1 | |||||
BraTS | CBICA_BGE | -1 | -1 | -1 | |||||
BraTS | CBICA_BGG | -1 | -1 | -1 | |||||
BraTS | CBICA_BGN | -1 | -1 | -1 | |||||
BraTS | CBICA_BGO | -1 | -1 | -1 | |||||
BraTS | CBICA_BGR | -1 | -1 | -1 | |||||
BraTS | CBICA_BGT | -1 | -1 | -1 | |||||
BraTS | CBICA_BGW | -1 | -1 | -1 | |||||
BraTS | CBICA_BGX | -1 | -1 | -1 | |||||
BraTS | CBICA_BHB | -1 | -1 | -1 | |||||
BraTS | CBICA_BHK | -1 | -1 | -1 | |||||
BraTS | CBICA_BHM | -1 | -1 | -1 | |||||
BraTS | CBICA_BHQ | -1 | -1 | -1 | |||||
BraTS | CBICA_BHV | -1 | -1 | -1 | |||||
BraTS | CBICA_BHZ | -1 | -1 | -1 | |||||
BraTS | CBICA_BIC | -1 | -1 | -1 | |||||
BraTS | CBICA_BJY | -1 | -1 | -1 | |||||
BraTS | CBICA_BKV | -1 | -1 | -1 | |||||
BraTS | CBICA_BLJ | -1 | -1 | -1 | |||||
BraTS | CBICA_BNR | -1 | -1 | -1 | |||||
BraTS | TMC_6290 | -1 | -1 | -1 | |||||
BraTS | TMC_6643 | -1 | -1 | -1 | |||||
BraTS | TMC_9043 | -1 | -1 | -1 | |||||
BraTS | TMC_11964 | -1 | -1 | -1 | |||||
BraTS | TMC_12866 | -1 | -1 | -1 | |||||
BraTS | TMC_15477 | -1 | -1 | -1 | |||||
BraTS | TMC_21360 | -1 | -1 | -1 | |||||
BraTS | TMC_27374 | -1 | -1 | -1 | |||||
BraTS | TMC_30014 | -1 | -1 | -1 | |||||
CPTAC-GBM | C3L-00016 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00019 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00265 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00278 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00349 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00424 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00429 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00506 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00528 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00591 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00631 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00636 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00671 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00674 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00677 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01045 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01046 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01142 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01156 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01327 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02041 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02465 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02504 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02704 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02706 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02707 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02708 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03260 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03266 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03727 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03728 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03747 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03748 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-04084 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00661 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00662 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00663 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00665 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01192 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01196 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01505 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01849 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01851 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01852 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02255 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02256 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02286 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03001 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03003 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03755 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-04686 | -1 | -1 | 4 | |||||
IvyGAP | W10 | 1 | 1 | 4 | |||||
IvyGAP | W11 | 0 | 0 | 4 | |||||
IvyGAP | W12 | 0 | 0 | 4 | |||||
IvyGAP | W13 | 0 | 0 | 4 | |||||
IvyGAP | W16 | 0 | 0 | 4 | |||||
IvyGAP | W18 | 0 | 0 | 4 | |||||
IvyGAP | W19 | 0 | 0 | 4 | |||||
IvyGAP | W1 | 0 | 0 | 4 | |||||
IvyGAP | W20 | 0 | 0 | 4 | |||||
IvyGAP | W21 | 0 | 0 | 4 | |||||
IvyGAP | W22 | 0 | 0 | -1 | |||||
IvyGAP | W26 | 0 | -1 | 4 | |||||
IvyGAP | W29 | 0 | 0 | 4 | |||||
IvyGAP | W2 | 0 | 1 | 4 | |||||
IvyGAP | W30 | 0 | 0 | 4 | |||||
IvyGAP | W31 | 1 | 1 | 4 | |||||
IvyGAP | W32 | 0 | 0 | 4 | |||||
IvyGAP | W33 | 0 | 0 | 4 | |||||
IvyGAP | W34 | 0 | 0 | 4 | |||||
IvyGAP | W35 | 1 | 0 | 3 | |||||
IvyGAP | W36 | 0 | 0 | 4 | |||||
IvyGAP | W38 | 0 | 0 | 4 | |||||
IvyGAP | W39 | 0 | 0 | 4 | |||||
IvyGAP | W3 | 1 | 0 | 4 | |||||
IvyGAP | W40 | 0 | 0 | 4 | |||||
IvyGAP | W42 | 0 | -1 | 4 | |||||
IvyGAP | W43 | 0 | -1 | 4 | |||||
IvyGAP | W45 | -1 | -1 | 4 | |||||
IvyGAP | W48 | 0 | -1 | 4 | |||||
IvyGAP | W4 | 1 | 0 | 4 | |||||
IvyGAP | W50 | 0 | -1 | 3 | |||||
IvyGAP | W53 | 1 | -1 | 4 | |||||
IvyGAP | W54 | 0 | -1 | 4 | |||||
IvyGAP | W55 | 0 | -1 | 4 | |||||
IvyGAP | W5 | 0 | 0 | 4 | |||||
IvyGAP | W6 | 0 | 0 | 4 | |||||
IvyGAP | W7 | 0 | 0 | 4 | |||||
IvyGAP | W8 | 0 | 0 | 4 | |||||
IvyGAP | W9 | 0 | 0 | 4 | |||||
REMBRANDT | 900-00-5299 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5303 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5308 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5316 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5317 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5332 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5339 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5341 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5342 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5346 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5380 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5381 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5382 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5385 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5396 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5404 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5412 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5414 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5458 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5459 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5462 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5468 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5476 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5477 | -1 | -1 | 2 | |||||
REMBRANDT | HF0763 | -1 | -1 | -1 | |||||
REMBRANDT | HF0828 | -1 | -1 | 3 | |||||
REMBRANDT | HF0835 | -1 | -1 | 2 | |||||
REMBRANDT | HF0855 | -1 | -1 | 2 | |||||
REMBRANDT | HF0868 | -1 | -1 | -1 | |||||
REMBRANDT | HF0883 | -1 | -1 | -1 | |||||
REMBRANDT | HF0899 | -1 | -1 | 2 | |||||
REMBRANDT | HF0920 | -1 | -1 | 2 | |||||
REMBRANDT | HF0931 | -1 | -1 | 2 | |||||
REMBRANDT | HF0953 | -1 | -1 | 2 | |||||
REMBRANDT | HF0960 | -1 | -1 | 2 | |||||
REMBRANDT | HF0966 | -1 | -1 | 3 | |||||
REMBRANDT | HF0986 | -1 | -1 | 4 | |||||
REMBRANDT | HF0990 | -1 | -1 | 4 | |||||
REMBRANDT | HF1000 | -1 | -1 | 2 | |||||
REMBRANDT | HF1058 | -1 | -1 | 4 | |||||
REMBRANDT | HF1059 | -1 | -1 | 3 | |||||
REMBRANDT | HF1071 | -1 | -1 | 4 | |||||
REMBRANDT | HF1077 | -1 | -1 | 4 | |||||
REMBRANDT | HF1078 | -1 | -1 | 4 | |||||
REMBRANDT | HF1097 | -1 | -1 | 4 | |||||
REMBRANDT | HF1113 | -1 | -1 | -1 | |||||
REMBRANDT | HF1122 | -1 | -1 | 4 | |||||
REMBRANDT | HF1136 | -1 | -1 | 3 | |||||
REMBRANDT | HF1139 | -1 | -1 | 4 | |||||
REMBRANDT | HF1150 | -1 | -1 | 3 | |||||
REMBRANDT | HF1156 | -1 | -1 | 2 | |||||
REMBRANDT | HF1167 | -1 | -1 | 2 | |||||
REMBRANDT | HF1185 | -1 | -1 | 3 | |||||
REMBRANDT | HF1191 | -1 | -1 | 4 | |||||
REMBRANDT | HF1199 | -1 | -1 | -1 | |||||
REMBRANDT | HF1219 | -1 | -1 | 3 | |||||
REMBRANDT | HF1227 | -1 | -1 | 2 | |||||
REMBRANDT | HF1232 | -1 | -1 | 3 | |||||
REMBRANDT | HF1235 | -1 | -1 | 2 | |||||
REMBRANDT | HF1242 | -1 | -1 | 3 | |||||
REMBRANDT | HF1246 | -1 | -1 | 2 | |||||
REMBRANDT | HF1264 | -1 | -1 | 2 | |||||
REMBRANDT | HF1269 | -1 | -1 | 4 | |||||
REMBRANDT | HF1280 | -1 | -1 | 3 | |||||
REMBRANDT | HF1292 | -1 | -1 | 4 | |||||
REMBRANDT | HF1293 | -1 | -1 | -1 | |||||
REMBRANDT | HF1297 | -1 | -1 | 4 | |||||
REMBRANDT | HF1300 | -1 | -1 | -1 | |||||
REMBRANDT | HF1307 | -1 | -1 | -1 | |||||
REMBRANDT | HF1316 | -1 | -1 | 2 | |||||
REMBRANDT | HF1318 | -1 | -1 | -1 | |||||
REMBRANDT | HF1325 | -1 | -1 | 2 | |||||
REMBRANDT | HF1331 | -1 | -1 | -1 | |||||
REMBRANDT | HF1334 | -1 | -1 | 2 | |||||
REMBRANDT | HF1344 | -1 | -1 | 2 | |||||
REMBRANDT | HF1345 | -1 | -1 | 2 | |||||
REMBRANDT | HF1357 | -1 | -1 | 3 | |||||
REMBRANDT | HF1381 | -1 | -1 | 2 | |||||
REMBRANDT | HF1397 | -1 | -1 | 4 | |||||
REMBRANDT | HF1398 | -1 | -1 | 3 | |||||
REMBRANDT | HF1407 | -1 | -1 | 2 | |||||
REMBRANDT | HF1409 | -1 | -1 | 3 | |||||
REMBRANDT | HF1420 | -1 | -1 | -1 | |||||
REMBRANDT | HF1429 | -1 | -1 | -1 | |||||
REMBRANDT | HF1433 | -1 | -1 | 2 | |||||
REMBRANDT | HF1437 | -1 | -1 | -1 | |||||
REMBRANDT | HF1442 | -1 | -1 | 2 | |||||
REMBRANDT | HF1458 | -1 | -1 | 3 | |||||
REMBRANDT | HF1463 | -1 | -1 | 2 | |||||
REMBRANDT | HF1489 | -1 | -1 | 2 | |||||
REMBRANDT | HF1490 | -1 | -1 | 3 | |||||
REMBRANDT | HF1493 | -1 | -1 | -1 | |||||
REMBRANDT | HF1510 | -1 | -1 | -1 | |||||
REMBRANDT | HF1511 | -1 | -1 | 2 | |||||
REMBRANDT | HF1517 | -1 | -1 | 4 | |||||
REMBRANDT | HF1538 | -1 | -1 | 4 | |||||
REMBRANDT | HF1551 | -1 | -1 | 2 | |||||
REMBRANDT | HF1553 | -1 | -1 | 2 | |||||
REMBRANDT | HF1560 | -1 | -1 | 4 | |||||
REMBRANDT | HF1568 | -1 | -1 | 2 | |||||
REMBRANDT | HF1587 | -1 | -1 | 3 | |||||
REMBRANDT | HF1588 | -1 | -1 | 2 | |||||
REMBRANDT | HF1606 | -1 | -1 | 2 | |||||
REMBRANDT | HF1613 | -1 | -1 | 3 | |||||
REMBRANDT | HF1628 | -1 | -1 | 4 | |||||
REMBRANDT | HF1652 | -1 | -1 | -1 | |||||
REMBRANDT | HF1677 | -1 | -1 | 2 | |||||
REMBRANDT | HF1702 | -1 | -1 | 3 | |||||
REMBRANDT | HF1708 | -1 | -1 | 2 | |||||
TCGA-GBM | TCGA-02-0003 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0006 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0009 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0011 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0027 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0033 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0034 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0037 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0046 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0047 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0048 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0054 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0059 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0060 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0064 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0068 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0069 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0070 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0075 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0085 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0086 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0087 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0102 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0106 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0116 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0119 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0122 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0128 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0130 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0132 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0133 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0137 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0138 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0139 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0142 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0145 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0149 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0154 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0158 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0162 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0164 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0166 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0168 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0175 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0176 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0177 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0179 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0182 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0184 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0185 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0187 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0188 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0189 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0190 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0192 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0213 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0238 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0240 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0241 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0644 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0646 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0648 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0649 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1084 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1802 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-2570 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5408 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5412 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5413 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5417 | 1 | -1 | 4 | |||||
TCGA-GBM | TCGA-06-6389 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0350 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0352 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0353 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0354 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0355 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0356 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0357 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0358 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0359 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0360 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0385 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0389 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0392 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0512 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0520 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0521 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0522 | -1 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0524 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0529 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0616 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0776 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1093 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1094 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1098 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1598 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1601 | 0 | -1 | -1 | |||||
TCGA-GBM | TCGA-12-1602 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-3650 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-0789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1456 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1794 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1825 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-3477 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-0963 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2624 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2631 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5951 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5954 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5958 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5960 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1834 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1838 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-2526 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4932 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-76-4934 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4935 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6191 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6193 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6280 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6282 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6285 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6656 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6657 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6661 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6662 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6663 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6664 | 0 | 0 | 4 | |||||
TCGA-LGG | TCGA-CS-4941 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4942 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4944 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5393 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-5395 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5396 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-CS-5397 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6186 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6188 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6290 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6665 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6666 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6667 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-6668 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-CS-6669 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5849 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-5851 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5852 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5854 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5871 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5874 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6397 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6399 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6400 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6404 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6405 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6407 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6410 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6542 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7008 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7010 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7014 | -1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7015 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7018 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7019 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7294 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-7298 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7300 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7302 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7309 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8162 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8164 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-8165 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8167 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8168 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-EZ-7265A | -1 | -1 | -1 | |||||
TCGA-LGG | TCGA-FG-5964 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-6688 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6691 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6692 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-7643 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-A713 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7473 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7616 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7680 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7684 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7686 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7692 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7693 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7694 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7856 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7860 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7874 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7879 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7882 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8105 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-8106 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8107 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8111 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8114 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8563 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A614 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0 | 2 |
Data_collection | Patient | IDH_mutated | Prediction_score_IDH_wildtype | Prediction_score_IDH_mutated | 1p19q_codeleted | Prediction_score_1p19q_codeleted | Prediction_score_1p19q_intact | Grade | Prediction_score_grade_2 | Prediction_score_grade_3 | Prediction_score_grade_4 | ||||||||||||
TCGA-GBM | TCGA-02-0003 | 0 | 099998915 | 10867886E-05 | 0 | 099996686 | 3308471E-05 | 4 | 7377526E-05 | 000074111245 | 099918514 | ||||||||||||
TCGA-GBM | TCGA-02-0006 | 0 | 042321962 | 05767803 | 0 | 068791837 | 031208166 | 4 | 060229343 | 026596427 | 013174225 | ||||||||||||
TCGA-GBM | TCGA-02-0009 | 0 | 099306935 | 0006930672 | 0 | 09906961 | 0009303949 | 4 | 0056565534 | 010282235 | 08406121 | ||||||||||||
TCGA-GBM | TCGA-02-0011 | 0 | 013531776 | 08646823 | 0 | 085318035 | 01468197 | 4 | 0015055533 | 092510724 | 005983725 | ||||||||||||
TCGA-GBM | TCGA-02-0027 | 0 | 09997279 | 000027212297 | 0 | 09986827 | 00013172914 | 4 | 00016104137 | 00038575265 | 0994532 | ||||||||||||
TCGA-GBM | TCGA-02-0033 | 0 | 099974436 | 000025564007 | 0 | 099940693 | 0000593021 | 4 | 00020670628 | 0003761288 | 09941717 | ||||||||||||
TCGA-GBM | TCGA-02-0034 | 0 | 091404164 | 008595832 | 0 | 089209336 | 01079066 | 4 | 00116944825 | 0061110377 | 092719513 | ||||||||||||
TCGA-GBM | TCGA-02-0037 | 0 | 09999577 | 42315594E-05 | 0 | 099992716 | 72827526E-05 | 4 | 82080274E-05 | 0009249337 | 09906686 | ||||||||||||
TCGA-GBM | TCGA-02-0046 | 0 | 0999129 | 00008710656 | 0 | 09989637 | 00010362669 | 4 | 0004290756 | 0022799779 | 097290945 | ||||||||||||
TCGA-GBM | TCGA-02-0047 | 0 | 099991703 | 83008505E-05 | 0 | 09999292 | 70863265E-05 | 4 | 000016252015 | 0040118434 | 095971906 | ||||||||||||
TCGA-GBM | TCGA-02-0048 | 0 | 09998785 | 000012148175 | 0 | 099959475 | 000040527192 | 4 | 00002215901 | 000039696065 | 09993814 | ||||||||||||
TCGA-GBM | TCGA-02-0054 | 0 | 09999831 | 1689829E-05 | 0 | 09999442 | 5583975E-05 | 4 | 00010063206 | 0060579527 | 093841416 | ||||||||||||
TCGA-GBM | TCGA-02-0059 | -1 | 09993749 | 000062511285 | 0 | 09996424 | 00003576683 | 4 | 00007046657 | 0010920537 | 09883748 | ||||||||||||
TCGA-GBM | TCGA-02-0060 | 0 | 07197039 | 028029615 | 0 | 09016612 | 009833879 | 4 | 017739706 | 03728545 | 04497484 | ||||||||||||
TCGA-GBM | TCGA-02-0064 | 0 | 09999083 | 9170197E-05 | 0 | 09995073 | 000049264234 | 4 | 000043781495 | 00028024286 | 099675983 | ||||||||||||
TCGA-GBM | TCGA-02-0068 | 0 | 099187535 | 0008124709 | 0 | 099528164 | 00047183693 | 4 | 00030539853 | 059695286 | 039999318 | ||||||||||||
TCGA-GBM | TCGA-02-0069 | 0 | 09890871 | 0010912909 | 0 | 099704784 | 0002952148 | 4 | 00057067247 | 0061368063 | 09329252 | ||||||||||||
TCGA-GBM | TCGA-02-0070 | 0 | 09940659 | 00059340666 | 0 | 0957794 | 0042206 | 4 | 0008216515 | 003556913 | 09562143 | ||||||||||||
TCGA-GBM | TCGA-02-0075 | 0 | 099933076 | 00006693099 | 0 | 099735296 | 00026470982 | 4 | 000044697264 | 00035736929 | 09959793 | ||||||||||||
TCGA-GBM | TCGA-02-0085 | 0 | 099114406 | 0008855922 | 0 | 09756698 | 002433019 | 4 | 00065203947 | 0035171553 | 095830804 | ||||||||||||
TCGA-GBM | TCGA-02-0086 | 0 | 099965334 | 000034666777 | 0 | 0998698 | 00013019645 | 4 | 000032699382 | 00018025768 | 099787045 | ||||||||||||
TCGA-GBM | TCGA-02-0087 | -1 | 09974885 | 00025114634 | 0 | 09990638 | 000093628286 | 4 | 0007505083 | 0008562708 | 098393226 | ||||||||||||
TCGA-GBM | TCGA-02-0102 | 0 | 09797647 | 0020235319 | 0 | 098292196 | 0017078074 | 4 | 003512482 | 03901857 | 05746895 | ||||||||||||
TCGA-GBM | TCGA-02-0106 | -1 | 099993694 | 6302759E-05 | 0 | 099980897 | 000019110431 | 4 | 60797247E-05 | 00008735659 | 09990657 | ||||||||||||
TCGA-GBM | TCGA-02-0116 | 0 | 09999778 | 22125667E-05 | 0 | 09996886 | 00003113695 | 4 | 000015498884 | 000051770627 | 09993273 | ||||||||||||
TCGA-GBM | TCGA-06-0119 | 0 | 09999362 | 63770494E-05 | 0 | 09999355 | 6452215E-05 | 4 | 000013225728 | 00028902534 | 099697745 | ||||||||||||
TCGA-GBM | TCGA-06-0122 | 0 | 09915298 | 0008470196 | 0 | 09859093 | 00140907345 | 4 | 00121390615 | 027333176 | 071452916 | ||||||||||||
TCGA-GBM | TCGA-06-0128 | 1 | 099988174 | 000011820537 | 0 | 099980634 | 000019373452 | 4 | 000016409029 | 0007865882 | 099197 | ||||||||||||
TCGA-GBM | TCGA-06-0130 | 0 | 099998784 | 12123987E-05 | 0 | 09999323 | 6775062E-05 | 4 | 80872844E-05 | 00026260202 | 099729306 | ||||||||||||
TCGA-GBM | TCGA-06-0132 | 0 | 09998566 | 000014341719 | 0 | 099988496 | 000011501736 | 4 | 000072843547 | 0005115947 | 099415565 | ||||||||||||
TCGA-GBM | TCGA-06-0133 | 0 | 097782 | 002218004 | 0 | 0993807 | 00061929906 | 4 | 0026753133 | 004659919 | 09266477 | ||||||||||||
TCGA-GBM | TCGA-06-0137 | 0 | 096448904 | 003551094 | 0 | 099403125 | 0005968731 | 4 | 000649511 | 038909483 | 060441005 | ||||||||||||
TCGA-GBM | TCGA-06-0138 | 0 | 09977743 | 00022256707 | 0 | 099736834 | 0002631674 | 4 | 00032954598 | 0011606657 | 09850979 | ||||||||||||
TCGA-GBM | TCGA-06-0139 | 0 | 09992649 | 00007350447 | 0 | 099898964 | 00010103129 | 4 | 00021781863 | 00069256434 | 099089617 | ||||||||||||
TCGA-GBM | TCGA-06-0142 | 0 | 099909425 | 00009057334 | 0 | 09985896 | 00014103584 | 4 | 0002598974 | 0046451908 | 09509491 | ||||||||||||
TCGA-GBM | TCGA-06-0145 | 0 | 099964654 | 000035350278 | 0 | 0999652 | 000034802416 | 4 | 00009068022 | 0021991275 | 0977102 | ||||||||||||
TCGA-GBM | TCGA-06-0149 | -1 | 09992161 | 00007839425 | 0 | 09981067 | 00018932257 | 4 | 00057726577 | 0013888515 | 09803388 | ||||||||||||
TCGA-GBM | TCGA-06-0154 | 0 | 099968064 | 000031937403 | 0 | 0999729 | 000027106237 | 4 | 000041507537 | 023430935 | 07652756 | ||||||||||||
TCGA-GBM | TCGA-06-0158 | 0 | 09999199 | 8014118E-05 | 0 | 099992514 | 74846226E-05 | 4 | 00026547876 | 020762624 | 0789719 | ||||||||||||
TCGA-GBM | TCGA-06-0162 | -1 | 099964297 | 00003569706 | 0 | 09997459 | 0000254147 | 4 | 000033955855 | 004318936 | 09564711 | ||||||||||||
TCGA-GBM | TCGA-06-0164 | -1 | 09983991 | 00016009645 | 0 | 09873262 | 0012673735 | 4 | 00016517473 | 00048346478 | 09935136 | ||||||||||||
TCGA-GBM | TCGA-06-0166 | 0 | 099991715 | 82846556E-05 | 0 | 0999554 | 00004459562 | 4 | 000013499439 | 0011635037 | 098823 | ||||||||||||
TCGA-GBM | TCGA-06-0168 | 0 | 09975561 | 00024438864 | 0 | 09964825 | 00035174883 | 4 | 0004766434 | 010448053 | 089075303 | ||||||||||||
TCGA-GBM | TCGA-06-0175 | -1 | 09996252 | 000037482675 | 0 | 09988098 | 00011902251 | 4 | 00026097735 | 004992068 | 094746953 | ||||||||||||
TCGA-GBM | TCGA-06-0176 | 0 | 099550986 | 00044901576 | 0 | 09998872 | 000011279297 | 4 | 0032868527 | 036690876 | 06002227 | ||||||||||||
TCGA-GBM | TCGA-06-0177 | -1 | 081774735 | 018225263 | 0 | 09946464 | 00053536464 | 4 | 0026683953 | 013013016 | 08431859 | ||||||||||||
TCGA-GBM | TCGA-06-0179 | -1 | 09997508 | 000024923254 | 0 | 09989778 | 00010222099 | 4 | 0002628482 | 0004127114 | 099324447 | ||||||||||||
TCGA-GBM | TCGA-06-0182 | -1 | 099999547 | 45838406E-06 | 0 | 099998736 | 12656287E-05 | 4 | 00002591103 | 000018499703 | 09995559 | ||||||||||||
TCGA-GBM | TCGA-06-0184 | 0 | 09935369 | 00064631375 | 0 | 099458355 | 00054164114 | 4 | 0023110552 | 0017436244 | 09594532 | ||||||||||||
TCGA-GBM | TCGA-06-0185 | 0 | 09999337 | 66310655E-05 | 0 | 099986255 | 000013738607 | 4 | 7657532E-05 | 0016089642 | 098383385 | ||||||||||||
TCGA-GBM | TCGA-06-0187 | 0 | 09991689 | 00008312097 | 0 | 099700147 | 00029984985 | 4 | 00020616595 | 0033111423 | 096482694 | ||||||||||||
TCGA-GBM | TCGA-06-0188 | 0 | 09883802 | 0011619771 | 0 | 09826743 | 0017325714 | 4 | 0013776424 | 0112841725 | 087338185 | ||||||||||||
TCGA-GBM | TCGA-06-0189 | 0 | 099906737 | 0000932636 | 0 | 09983865 | 00016135005 | 4 | 00022760795 | 00106745735 | 09870494 | ||||||||||||
TCGA-GBM | TCGA-06-0190 | 0 | 099954176 | 000045831292 | 0 | 09967013 | 00032986512 | 4 | 000040555766 | 0001246768 | 099834764 | ||||||||||||
TCGA-GBM | TCGA-06-0192 | 0 | 09997876 | 00002123566 | 0 | 09992735 | 00007264875 | 4 | 00004505576 | 00014473333 | 09981021 | ||||||||||||
TCGA-GBM | TCGA-06-0213 | 0 | 099986935 | 00001305845 | 0 | 099971646 | 000028351307 | 4 | 8755587E-05 | 00013480412 | 09985644 | ||||||||||||
TCGA-GBM | TCGA-06-0238 | 0 | 09999982 | 17603431E-06 | 0 | 09999894 | 10616134E-05 | 4 | 8076515E-05 | 56053756E-05 | 099986315 | ||||||||||||
TCGA-GBM | TCGA-06-0240 | 0 | 09989956 | 00010044163 | 0 | 099948466 | 00005152657 | 4 | 00016040986 | 021931975 | 077907616 | ||||||||||||
TCGA-GBM | TCGA-06-0241 | 0 | 099959785 | 000040211933 | 0 | 099910825 | 00008917038 | 4 | 00023411359 | 0007850656 | 098980826 | ||||||||||||
TCGA-GBM | TCGA-06-0644 | 0 | 09871044 | 0012895588 | 0 | 09859228 | 00140771745 | 4 | 0013671214 | 009819665 | 088813215 | ||||||||||||
TCGA-GBM | TCGA-06-0646 | 0 | 099959 | 00004100472 | 0 | 099936503 | 000063495064 | 4 | 00019223108 | 0040443853 | 095763385 | ||||||||||||
TCGA-GBM | TCGA-06-0648 | 0 | 09999709 | 29083441E-05 | 0 | 099982435 | 000017571273 | 4 | 000077678583 | 000038868992 | 099883455 | ||||||||||||
TCGA-GBM | TCGA-06-0649 | 0 | 09997805 | 000021952427 | 0 | 099951684 | 000048311835 | 4 | 0042641632 | 00058432207 | 095151514 | ||||||||||||
TCGA-GBM | TCGA-06-1084 | 0 | 099985826 | 000014174655 | 0 | 099968565 | 00003144242 | 4 | 00002676724 | 020492287 | 079480946 | ||||||||||||
TCGA-GBM | TCGA-06-1802 | -1 | 09991928 | 00008072305 | 0 | 09956176 | 0004382337 | 4 | 000043478087 | 00019495043 | 09976157 | ||||||||||||
TCGA-GBM | TCGA-06-2570 | 1 | 096841115 | 0031588882 | 0 | 09842457 | 0015754245 | 4 | 0015369608 | 0030956635 | 09536738 | ||||||||||||
TCGA-GBM | TCGA-06-5408 | 0 | 099857306 | 00014269598 | 0 | 09962638 | 00037362208 | 4 | 00027690146 | 0016195394 | 098103565 | ||||||||||||
TCGA-GBM | TCGA-06-5412 | 0 | 099366105 | 0006338921 | 0 | 099193794 | 0008061992 | 4 | 0011476759 | 006606435 | 09224589 | ||||||||||||
TCGA-GBM | TCGA-06-5413 | 0 | 09994105 | 000058955856 | 0 | 09983026 | 00016974095 | 4 | 00027100197 | 0021083053 | 097620696 | ||||||||||||
TCGA-GBM | TCGA-06-5417 | 1 | 01521267 | 08478733 | -1 | 03064492 | 06935508 | 4 | 013736826 | 037757674 | 048505494 | ||||||||||||
TCGA-GBM | TCGA-06-6389 | 1 | 099987435 | 000012558252 | 0 | 09997017 | 000029827762 | 4 | 00014020519 | 00020044278 | 099659353 | ||||||||||||
TCGA-GBM | TCGA-08-0350 | 0 | 019229275 | 08077072 | 0 | 0033211168 | 09667888 | 4 | 0051619414 | 022280572 | 072557485 | ||||||||||||
TCGA-GBM | TCGA-08-0352 | 0 | 099997497 | 25071595E-05 | 0 | 099992514 | 74846226E-05 | 4 | 000024192198 | 000048111935 | 099927694 | ||||||||||||
TCGA-GBM | TCGA-08-0353 | 0 | 09901496 | 0009850325 | 0 | 09967775 | 0003222484 | 4 | 00053748637 | 0004291497 | 09903336 | ||||||||||||
TCGA-GBM | TCGA-08-0354 | 0 | 076413894 | 023586108 | 0 | 07554566 | 024454337 | 4 | 008784444 | 02004897 | 071166587 | ||||||||||||
TCGA-GBM | TCGA-08-0355 | 0 | 09998349 | 000016506859 | 0 | 099984336 | 000015659066 | 4 | 000076689845 | 0023648744 | 09755844 | ||||||||||||
TCGA-GBM | TCGA-08-0356 | 0 | 097673583 | 0023264103 | 0 | 097773504 | 0022264915 | 4 | 001175834 | 0031075679 | 095716596 | ||||||||||||
TCGA-GBM | TCGA-08-0357 | 0 | 099509466 | 0004905406 | 0 | 099300176 | 00069982093 | 4 | 0005191745 | 0038681854 | 095612645 | ||||||||||||
TCGA-GBM | TCGA-08-0358 | 0 | 099999785 | 2199356E-06 | 0 | 099999034 | 9628425E-06 | 4 | 6113315E-06 | 00011283219 | 09988656 | ||||||||||||
TCGA-GBM | TCGA-08-0359 | 0 | 097885466 | 0021145396 | 0 | 09956006 | 00043994132 | 4 | 0009885523 | 0066605434 | 092350906 | ||||||||||||
TCGA-GBM | TCGA-08-0360 | 0 | 09922444 | 00077555366 | -1 | 09948704 | 00051296344 | 4 | 0013318472 | 003317344 | 095350814 | ||||||||||||
TCGA-GBM | TCGA-08-0385 | 0 | 099605453 | 00039454065 | -1 | 099686414 | 0003135836 | 4 | 00050293226 | 0029977333 | 096499336 | ||||||||||||
TCGA-GBM | TCGA-08-0389 | 0 | 099964714 | 000035281325 | 0 | 09991272 | 000087276706 | 4 | 00017554013 | 00024730961 | 099577147 | ||||||||||||
TCGA-GBM | TCGA-08-0390 | 0 | 099945146 | 000054847915 | 0 | 099936 | 00006399274 | 4 | 00036811908 | 00050958768 | 0991223 | ||||||||||||
TCGA-GBM | TCGA-08-0392 | 0 | 099962366 | 000037629317 | 0 | 09993575 | 00006424303 | 4 | 000036593352 | 0010291994 | 09893421 | ||||||||||||
TCGA-GBM | TCGA-08-0512 | -1 | 09982893 | 00017106998 | 0 | 099193794 | 0008061992 | 4 | 00016200381 | 00027773918 | 09956026 | ||||||||||||
TCGA-GBM | TCGA-08-0520 | -1 | 099603915 | 00039607873 | 0 | 09981933 | 00018066854 | 4 | 00007140295 | 0019064669 | 09802213 | ||||||||||||
TCGA-GBM | TCGA-08-0521 | -1 | 09975274 | 0002472623 | 0 | 099490017 | 00050998176 | 4 | 0001514669 | 0020103427 | 09783819 | ||||||||||||
TCGA-GBM | TCGA-08-0522 | -1 | 099960107 | 000039899128 | -1 | 09992053 | 00007947255 | 4 | 0000269389 | 0006173321 | 09935573 | ||||||||||||
TCGA-GBM | TCGA-08-0524 | -1 | 09964619 | 0003538086 | 0 | 099620515 | 0003794834 | 4 | 000019140428 | 0010096702 | 09897119 | ||||||||||||
TCGA-GBM | TCGA-08-0529 | -1 | 09996567 | 000034329997 | 0 | 099952066 | 00004793605 | 4 | 000032077235 | 0035970636 | 09637086 | ||||||||||||
TCGA-GBM | TCGA-12-0616 | 0 | 098521465 | 0014785408 | 0 | 098704207 | 001295789 | 4 | 001592791 | 012875569 | 08553164 | ||||||||||||
TCGA-GBM | TCGA-12-0776 | -1 | 099899167 | 00010083434 | 0 | 09987031 | 00012968953 | 4 | 0019219175 | 00637484 | 09170324 | ||||||||||||
TCGA-GBM | TCGA-12-0829 | 0 | 099913067 | 00008693674 | 0 | 099821776 | 00017821962 | 4 | 00021031094 | 0055067167 | 09428297 | ||||||||||||
TCGA-GBM | TCGA-12-1093 | 0 | 099992585 | 7411892E-05 | 0 | 09999448 | 5518923E-05 | 4 | 000046803855 | 0012115157 | 098741674 | ||||||||||||
TCGA-GBM | TCGA-12-1094 | -1 | 09980045 | 00019955388 | 0 | 09866105 | 0013389497 | 4 | 00053194338 | 001599471 | 097868586 | ||||||||||||
TCGA-GBM | TCGA-12-1098 | -1 | 09998406 | 000015936712 | 0 | 09977216 | 00022783307 | 4 | 000010218692 | 0035607774 | 09642901 | ||||||||||||
TCGA-GBM | TCGA-12-1598 | 0 | 096309197 | 0036908068 | 0 | 097933435 | 0020665688 | 4 | 0012952217 | 052912676 | 045792103 | ||||||||||||
TCGA-GBM | TCGA-12-1601 | 0 | 09875683 | 0012431651 | -1 | 0991891 | 0008108984 | -1 | 00118053425 | 0105477065 | 088271755 | ||||||||||||
TCGA-GBM | TCGA-12-1602 | 0 | 099830914 | 00016908031 | 0 | 099858415 | 00014158705 | 4 | 0008427611 | 0025996923 | 09655755 | ||||||||||||
TCGA-GBM | TCGA-12-3650 | 0 | 09761519 | 0023848088 | 0 | 097467697 | 0025323058 | 4 | 0010450666 | 043705726 | 05524921 | ||||||||||||
TCGA-GBM | TCGA-14-0789 | 0 | 099856466 | 00014353332 | 0 | 099666256 | 00033374047 | 4 | 0001406897 | 0008273975 | 099031913 | ||||||||||||
TCGA-GBM | TCGA-14-1456 | 1 | 006299064 | 093700933 | 0 | 08656222 | 013437784 | 4 | 016490369 | 047177824 | 036331803 | ||||||||||||
TCGA-GBM | TCGA-14-1794 | 0 | 08579393 | 014206071 | 0 | 09850429 | 0014957087 | 4 | 0023009384 | 009868736 | 08783033 | ||||||||||||
TCGA-GBM | TCGA-14-1825 | 0 | 099960107 | 000039899128 | 0 | 099968123 | 00003187511 | 4 | 0008552247 | 0010156045 | 09812918 | ||||||||||||
TCGA-GBM | TCGA-14-1829 | 0 | 090690076 | 009309922 | 0 | 09907856 | 0009214366 | 4 | 0008461936 | 0102735735 | 088880235 | ||||||||||||
TCGA-GBM | TCGA-14-3477 | 0 | 099796116 | 0002038787 | 0 | 09990728 | 00009271923 | 4 | 00032272525 | 0021644868 | 09751279 | ||||||||||||
TCGA-GBM | TCGA-19-0963 | -1 | 099876726 | 00012327607 | 0 | 09983612 | 00016388679 | 4 | 00031698826 | 013153598 | 086529416 | ||||||||||||
TCGA-GBM | TCGA-19-1390 | 0 | 099913234 | 00008676725 | 0 | 099703634 | 00029636684 | 4 | 00015592943 | 0026028048 | 097241265 | ||||||||||||
TCGA-GBM | TCGA-19-1789 | 0 | 09809491 | 00190509 | 0 | 09915216 | 0008478402 | 4 | 0038703684 | 014341596 | 08178804 | ||||||||||||
TCGA-GBM | TCGA-19-2624 | 0 | 07535573 | 024644265 | 0 | 09816127 | 0018387254 | 4 | 012311598 | 012769651 | 07491875 | ||||||||||||
TCGA-GBM | TCGA-19-2631 | 0 | 099860877 | 0001391234 | 0 | 09981178 | 00018821858 | 4 | 00009839778 | 001843531 | 09805807 | ||||||||||||
TCGA-GBM | TCGA-19-5951 | 0 | 09999031 | 9685608E-05 | 0 | 099977034 | 000022960825 | 4 | 00020246736 | 0004014765 | 09939606 | ||||||||||||
TCGA-GBM | TCGA-19-5954 | 0 | 099456257 | 00054374957 | 0 | 09968273 | 00031726828 | 4 | 00073725334 | 006310084 | 09295266 | ||||||||||||
TCGA-GBM | TCGA-19-5958 | 0 | 099999475 | 5234907E-06 | 0 | 0999941 | 58978338E-05 | 4 | 35422294E-05 | 86819025E-05 | 09998777 | ||||||||||||
TCGA-GBM | TCGA-19-5960 | 0 | 09683962 | 003160382 | 0 | 09013577 | 009864227 | 4 | 0011394806 | 018114014 | 08074651 | ||||||||||||
TCGA-GBM | TCGA-27-1834 | 0 | 099998164 | 18342893E-05 | 0 | 09999685 | 31446623E-05 | 4 | 8611921E-05 | 000031686216 | 0999597 | ||||||||||||
TCGA-GBM | TCGA-27-1838 | 0 | 09993625 | 000063743413 | 0 | 09940428 | 0005957154 | 4 | 00006736379 | 0007191195 | 099213517 | ||||||||||||
TCGA-GBM | TCGA-27-2526 | 0 | 099983776 | 000016219281 | 0 | 09996898 | 000031015594 | 4 | 000016658282 | 00006323714 | 09992011 | ||||||||||||
TCGA-GBM | TCGA-76-4932 | 0 | 09867389 | 0013261103 | -1 | 09949397 | 0005060332 | 4 | 00007321126 | 0003016794 | 099625117 | ||||||||||||
TCGA-GBM | TCGA-76-4934 | 0 | 099318933 | 0006810731 | 0 | 09995073 | 000049264234 | 4 | 00061555947 | 00070025027 | 098684186 | ||||||||||||
TCGA-GBM | TCGA-76-4935 | 0 | 074562997 | 025437003 | 0 | 098242307 | 001757688 | 4 | 076535034 | 006437644 | 017027317 | ||||||||||||
TCGA-GBM | TCGA-76-6191 | 0 | 09981067 | 00018932257 | 0 | 09970879 | 00029121784 | 4 | 00044340584 | 00096095055 | 098595643 | ||||||||||||
TCGA-GBM | TCGA-76-6193 | 0 | 09966168 | 0003383191 | 0 | 099850464 | 00014953383 | 4 | 00037061477 | 007873953 | 09175543 | ||||||||||||
TCGA-GBM | TCGA-76-6280 | 0 | 099948776 | 00005122569 | 0 | 099908185 | 000091819017 | 4 | 00001475792 | 000846075 | 099139166 | ||||||||||||
TCGA-GBM | TCGA-76-6282 | 0 | 0995906 | 0004093958 | 0 | 099861956 | 00013804223 | 4 | 00006694951 | 0009437619 | 09898929 | ||||||||||||
TCGA-GBM | TCGA-76-6285 | 0 | 099949074 | 00005092657 | 0 | 09971661 | 00028338495 | 4 | 00031175872 | 004005614 | 095682627 | ||||||||||||
TCGA-GBM | TCGA-76-6656 | 0 | 09996917 | 000030834455 | 0 | 09983897 | 00016103574 | 4 | 002648366 | 00017969633 | 09717193 | ||||||||||||
TCGA-GBM | TCGA-76-6657 | 0 | 099987245 | 000012755992 | 0 | 099951494 | 00004850083 | 4 | 000096620515 | 0005599633 | 09934342 | ||||||||||||
TCGA-GBM | TCGA-76-6661 | 0 | 093211424 | 006788577 | 0 | 09640178 | 0035982177 | 4 | 003490037 | 0026863772 | 09382358 | ||||||||||||
TCGA-GBM | TCGA-76-6662 | 0 | 096425414 | 0035745807 | 0 | 09963924 | 0003607617 | 4 | 002845819 | 002544755 | 09460942 | ||||||||||||
TCGA-GBM | TCGA-76-6663 | 0 | 088664144 | 0113358565 | 0 | 09984207 | 00015792594 | 4 | 0010206689 | 043740335 | 05523899 | ||||||||||||
TCGA-GBM | TCGA-76-6664 | 0 | 011047115 | 08895289 | 0 | 09559813 | 004401865 | 4 | 00049677677 | 08806894 | 011434281 | ||||||||||||
TCGA-LGG | TCGA-CS-4941 | 0 | 088931274 | 011068726 | 0 | 087037706 | 012962292 | 3 | 002865127 | 0048591908 | 092275685 | ||||||||||||
TCGA-LGG | TCGA-CS-4942 | 1 | 00031327847 | 099686724 | 0 | 096309197 | 0036908068 | 3 | 096261597 | 00148612335 | 0022522787 | ||||||||||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0005265965 | 099473405 | 0 | 09940544 | 00059455987 | 3 | 09439103 | 0023049146 | 003304057 | ||||||||||||
TCGA-LGG | TCGA-CS-4944 | 1 | 009363656 | 09063635 | 0 | 08755211 | 0124478824 | 2 | 034047556 | 033881712 | 03207073 | ||||||||||||
TCGA-LGG | TCGA-CS-5393 | 1 | 009623762 | 09037624 | 0 | 098178816 | 001821182 | 3 | 014111634 | 042021698 | 043866673 | ||||||||||||
TCGA-LGG | TCGA-CS-5395 | 0 | 08502822 | 014971776 | 0 | 09932025 | 00067975316 | 2 | 0052374925 | 018397054 | 076365453 | ||||||||||||
TCGA-LGG | TCGA-CS-5396 | 1 | 099839586 | 00016040892 | 1 | 099967945 | 000032062363 | 3 | 00016345463 | 029090768 | 07074577 | ||||||||||||
TCGA-LGG | TCGA-CS-5397 | 0 | 049304244 | 050695753 | 0 | 08829839 | 0117016025 | 3 | 038702008 | 021211159 | 040086827 | ||||||||||||
TCGA-LGG | TCGA-CS-6186 | 0 | 099913234 | 00008676725 | 0 | 099956185 | 000043818905 | 3 | 00008662089 | 016898473 | 083014905 | ||||||||||||
TCGA-LGG | TCGA-CS-6188 | 0 | 052768165 | 047231838 | 0 | 08584221 | 014157787 | 3 | 019437431 | 047675493 | 03288707 | ||||||||||||
TCGA-LGG | TCGA-CS-6290 | 1 | 09102666 | 008973339 | 0 | 09462997 | 0053700306 | 3 | 0104100704 | 025633416 | 06395651 | ||||||||||||
TCGA-LGG | TCGA-CS-6665 | 1 | 099600047 | 0003999501 | 0 | 099756086 | 00024391294 | 3 | 0011873978 | 001634113 | 097178483 | ||||||||||||
TCGA-LGG | TCGA-CS-6666 | 1 | 021655986 | 07834402 | 0 | 09327296 | 0067270435 | 3 | 017667453 | 036334327 | 045998225 | ||||||||||||
TCGA-LGG | TCGA-CS-6667 | 1 | 012061995 | 087938 | 0 | 095699733 | 0043002643 | 2 | 063733935 | 019323014 | 016943048 | ||||||||||||
TCGA-LGG | TCGA-CS-6668 | 1 | 0076787576 | 09232124 | 1 | 04240933 | 057590663 | 2 | 06810894 | 013706882 | 018184178 | ||||||||||||
TCGA-LGG | TCGA-CS-6669 | 0 | 08488156 | 01511844 | 0 | 094018847 | 005981148 | 2 | 0037862387 | 002352077 | 09386168 | ||||||||||||
TCGA-LGG | TCGA-DU-5849 | 1 | 005773187 | 094226813 | 1 | 08664153 | 013358466 | 2 | 072753835 | 015028271 | 012217898 | ||||||||||||
TCGA-LGG | TCGA-DU-5851 | 1 | 09963994 | 0003600603 | 0 | 099808073 | 00019192374 | 3 | 00060602655 | 012558761 | 08683521 | ||||||||||||
TCGA-LGG | TCGA-DU-5852 | 0 | 09998591 | 000014091856 | 0 | 099954873 | 000045121062 | 3 | 0002267452 | 00038046916 | 099392784 | ||||||||||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0010986943 | 09890131 | 0 | 09549844 | 0045015533 | 2 | 08603989 | 0077804394 | 0061796777 | ||||||||||||
TCGA-LGG | TCGA-DU-5854 | 0 | 09567354 | 0043264627 | 0 | 098768765 | 0012312326 | 3 | 01194655 | 027027336 | 06102612 | ||||||||||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0009312956 | 09906871 | 0 | 046602532 | 053397465 | 3 | 0008289882 | 097042197 | 0021288157 | ||||||||||||
TCGA-LGG | TCGA-DU-5871 | 1 | 005623634 | 09437636 | 0 | 09449439 | 005505607 | 2 | 042517176 | 020180763 | 037302068 | ||||||||||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0062359583 | 09376405 | 0 | 015278916 | 08472108 | 2 | 012133307 | 048199505 | 039667192 | ||||||||||||
TCGA-LGG | TCGA-DU-5874 | 1 | 022858672 | 077141327 | 1 | 06457066 | 03542934 | 2 | 058503634 | 020639434 | 02085693 | ||||||||||||
TCGA-LGG | TCGA-DU-6397 | 1 | 097691274 | 002308724 | 1 | 09908213 | 0009178773 | 3 | 00048094327 | 00412339 | 09539566 | ||||||||||||
TCGA-LGG | TCGA-DU-6399 | 1 | 00023920655 | 099760795 | 0 | 09970073 | 00029926652 | 2 | 098691386 | 0007037292 | 0006048777 | ||||||||||||
TCGA-LGG | TCGA-DU-6400 | 1 | 0030923586 | 09690764 | 1 | 037771282 | 06222872 | 2 | 09710506 | 0015339471 | 001360994 | ||||||||||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0014545513 | 098545444 | 0 | 045332992 | 054667014 | 2 | 0878585 | 006398724 | 005742785 | ||||||||||||
TCGA-LGG | TCGA-DU-6404 | 0 | 08563024 | 014369765 | 0 | 09857318 | 00142681915 | 3 | 0012578745 | 08931047 | 009431658 | ||||||||||||
TCGA-LGG | TCGA-DU-6405 | 0 | 094122344 | 0058776554 | 0 | 09657707 | 0034229323 | 3 | 0015099723 | 0858934 | 012596628 | ||||||||||||
TCGA-LGG | TCGA-DU-6407 | 1 | 00046772743 | 099532276 | 0 | 095787287 | 0042127114 | 2 | 095650303 | 0019410672 | 0024086302 | ||||||||||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0032852467 | 09671475 | 0 | 02978783 | 070212173 | 3 | 046377006 | 04552443 | 008098562 | ||||||||||||
TCGA-LGG | TCGA-DU-6410 | 1 | 084198 | 015801999 | 1 | 09610981 | 0038901985 | 3 | 0029748935 | 0547783 | 042246798 | ||||||||||||
TCGA-LGG | TCGA-DU-6542 | 1 | 099541724 | 0004582765 | 0 | 099690056 | 00030994152 | 3 | 00036504513 | 0033356518 | 0962993 | ||||||||||||
TCGA-LGG | TCGA-DU-7008 | 1 | 00027017966 | 09972982 | 0 | 09924154 | 0007584589 | 2 | 0945233 | 0033200152 | 0021566862 | ||||||||||||
TCGA-LGG | TCGA-DU-7010 | 1 | 09090629 | 0090937115 | 0 | 083999664 | 016000335 | 3 | 0011747591 | 011156695 | 08766855 | ||||||||||||
TCGA-LGG | TCGA-DU-7014 | -1 | 00067384504 | 09932615 | 0 | 09144437 | 008555635 | 2 | 090214694 | 005846623 | 003938676 | ||||||||||||
TCGA-LGG | TCGA-DU-7015 | 1 | 011059116 | 08894088 | 0 | 09457512 | 005424881 | 2 | 04990067 | 023008518 | 027090812 | ||||||||||||
TCGA-LGG | TCGA-DU-7018 | 1 | 06190684 | 038093168 | 1 | 09720721 | 0027927874 | 3 | 002608347 | 03462771 | 06276394 | ||||||||||||
TCGA-LGG | TCGA-DU-7019 | 1 | 006866228 | 09313377 | 0 | 068647516 | 031352484 | 3 | 06280373 | 02546188 | 011734395 | ||||||||||||
TCGA-LGG | TCGA-DU-7294 | 1 | 039513415 | 06048658 | 1 | 04910898 | 05089102 | 2 | 044678423 | 011827048 | 04349453 | ||||||||||||
TCGA-LGG | TCGA-DU-7298 | 1 | 002178117 | 097821885 | 0 | 058896303 | 041103697 | 3 | 04621931 | 040058115 | 013722575 | ||||||||||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0050494254 | 094950575 | 0 | 09805993 | 0019400762 | 3 | 088520575 | 003754964 | 0077244624 | ||||||||||||
TCGA-LGG | TCGA-DU-7300 | 1 | 020334144 | 07966585 | 1 | 021174264 | 078825736 | 3 | 06957292 | 014594184 | 015832895 | ||||||||||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0028517082 | 09714829 | 0 | 07594931 | 024050693 | 2 | 07559878 | 013617343 | 010783881 | ||||||||||||
TCGA-LGG | TCGA-DU-7302 | 1 | 007878401 | 092121595 | 1 | 097414124 | 0025858777 | 3 | 059945434 | 013100924 | 02695364 | ||||||||||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0049359404 | 09506406 | 0 | 09947084 | 0005291605 | 3 | 05746174 | 017312215 | 025226048 | ||||||||||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0774658 | 022534202 | 0 | 09720191 | 0027980946 | 2 | 007909051 | 04979186 | 042299092 | ||||||||||||
TCGA-LGG | TCGA-DU-7309 | 1 | 002068546 | 097931457 | 0 | 091696864 | 008303132 | 3 | 091011685 | 0041825026 | 0048058107 | ||||||||||||
TCGA-LGG | TCGA-DU-8162 | 0 | 019030987 | 08096902 | 0 | 084344435 | 015655571 | 3 | 06724078 | 015660264 | 017098951 | ||||||||||||
TCGA-LGG | TCGA-DU-8164 | 1 | 0026989132 | 09730109 | 1 | 06119184 | 038808158 | 2 | 078654927 | 011851947 | 00949313 | ||||||||||||
TCGA-LGG | TCGA-DU-8165 | 0 | 099918324 | 000081673806 | 0 | 09982692 | 00017308301 | 3 | 00077142627 | 001586733 | 097641844 | ||||||||||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0062617026 | 093738294 | 0 | 052265906 | 047734097 | 2 | 0571523 | 027175376 | 015672325 | ||||||||||||
TCGA-LGG | TCGA-DU-8167 | 1 | 008068282 | 09193171 | 0 | 08626991 | 013730097 | 2 | 07117111 | 014616342 | 014212546 | ||||||||||||
TCGA-LGG | TCGA-DU-8168 | 1 | 04501781 | 05498219 | 1 | 09405718 | 005942822 | 3 | 028535154 | 039651006 | 031813842 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 013576113 | 08642388 | 0 | 098667485 | 0013325148 | 3 | 06805368 | 011191124 | 020755199 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0038810804 | 09611892 | 0 | 094154674 | 005845324 | 2 | 07418394 | 01198958 | 013826479 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 036534345 | 06346565 | 0 | 097664696 | 0023353029 | 2 | 0076500095 | 07058904 | 021760948 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 057493186 | 042506814 | 0 | 08586593 | 014134066 | 3 | 024835269 | 018135522 | 05702921 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 017411166 | 082588834 | 0 | 08903419 | 0109658085 | 2 | 026840523 | 031951824 | 041207647 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 00015382263 | 099846184 | 0 | 09784259 | 0021574067 | 3 | 099424005 | 00014788082 | 0004281163 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 099497885 | 000502115 | 0 | 09904406 | 0009559399 | 3 | 00076062134 | 003340487 | 09589889 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 01338958 | 08661042 | 1 | 010181248 | 08981875 | 2 | 08703488 | 0033631936 | 0096019216 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 007097701 | 092902297 | 1 | 0049773447 | 09502266 | 2 | 08236395 | 0043779366 | 013258114 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 00054852334 | 09945148 | 1 | 00052813343 | 09947187 | 2 | 095740056 | 0030734295 | 0011865048 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 00015218158 | 099847823 | 0 | 09977216 | 00022783307 | 3 | 097611564 | 0011231668 | 0012652792 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 090418625 | 009581377 | 1 | 09320215 | 006797852 | 3 | 015433969 | 006605734 | 0779603 | ||||||||||||
TCGA-LGG | TCGA-EZ-7265A | -1 | 001654544 | 09834546 | -1 | 092290026 | 0077099696 | -1 | 091443384 | 0045349486 | 0040216673 | ||||||||||||
TCGA-LGG | TCGA-FG-5964 | 1 | 095945925 | 004054074 | 1 | 09480585 | 0051941562 | 2 | 0052469887 | 018844457 | 075908554 | ||||||||||||
TCGA-LGG | TCGA-FG-6688 | 0 | 041685596 | 0583144 | 0 | 0400786 | 0599214 | 3 | 032869554 | 028211078 | 03891937 | ||||||||||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0040960647 | 09590394 | 0 | 088871056 | 01112895 | 2 | 078484637 | 01247657 | 009038795 | ||||||||||||
TCGA-LGG | TCGA-FG-6691 | 1 | 00066411127 | 09933589 | 0 | 09705485 | 002945148 | 2 | 082394814 | 01353293 | 004072261 | ||||||||||||
TCGA-LGG | TCGA-FG-6692 | 0 | 099044985 | 0009550158 | 0 | 098370695 | 0016293105 | 3 | 002482948 | 023811981 | 07370507 | ||||||||||||
TCGA-LGG | TCGA-FG-7643 | 0 | 067991304 | 032008696 | 0 | 094600123 | 0053998843 | 2 | 032237333 | 020420441 | 047342223 | ||||||||||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 00037180893 | 09962819 | 0 | 098237246 | 0017627545 | 2 | 09685786 | 0019877713 | 0011543726 | ||||||||||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 0023916386 | 097608364 | 1 | 003330537 | 096669465 | 2 | 016134319 | 0751304 | 0087352775 | ||||||||||||
TCGA-LGG | TCGA-FG-A713 | 1 | 020932822 | 07906717 | 1 | 053740746 | 046259254 | 2 | 068370515 | 013241291 | 018388201 | ||||||||||||
TCGA-LGG | TCGA-HT-7473 | 1 | 026437023 | 073562974 | 0 | 09914391 | 0008560891 | 2 | 009070598 | 05834457 | 032584828 | ||||||||||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0014885316 | 09851147 | 0 | 093397486 | 006602513 | 3 | 09713343 | 0009202645 | 0019462984 | ||||||||||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0078306936 | 09216931 | 0 | 044295275 | 055704725 | 2 | 06683338 | 024550638 | 00861598 | ||||||||||||
TCGA-LGG | TCGA-HT-7616 | 1 | 0994089 | 0005911069 | 1 | 08912444 | 010875558 | 3 | 00015109215 | 00081261955 | 09903628 | ||||||||||||
TCGA-LGG | TCGA-HT-7680 | 0 | 01775255 | 08224745 | 0 | 079779327 | 020220678 | 2 | 06160002 | 021518312 | 016881672 | ||||||||||||
TCGA-LGG | TCGA-HT-7684 | 1 | 099250317 | 00074968883 | 0 | 09977216 | 00022783307 | 3 | 0001585032 | 0011880362 | 09865346 | ||||||||||||
TCGA-LGG | TCGA-HT-7686 | 1 | 043986762 | 05601324 | 0 | 09985134 | 00014866153 | 3 | 08800356 | 0017893802 | 010207067 | ||||||||||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0508178 | 049182203 | 0 | 09986749 | 00013250223 | 3 | 007351807 | 06479417 | 027854022 | ||||||||||||
TCGA-LGG | TCGA-HT-7692 | 1 | 0006764646 | 09932354 | 1 | 00017718028 | 099822825 | 2 | 084003216 | 009709251 | 00628754 | ||||||||||||
TCGA-LGG | TCGA-HT-7693 | 1 | 08835126 | 011648734 | 0 | 098880965 | 0011190402 | 2 | 00389769 | 060259813 | 035842496 | ||||||||||||
TCGA-LGG | TCGA-HT-7694 | 1 | 006299064 | 093700933 | 1 | 06663645 | 03336355 | 3 | 06368097 | 02515797 | 011161056 | ||||||||||||
TCGA-LGG | TCGA-HT-7855 | 1 | 013434944 | 086565053 | 0 | 06805072 | 031949285 | 3 | 03900612 | 035394293 | 02559958 | ||||||||||||
TCGA-LGG | TCGA-HT-7856 | 1 | 0037151825 | 09628482 | 1 | 045108467 | 05489153 | 3 | 0024809493 | 094240344 | 0032787096 | ||||||||||||
TCGA-LGG | TCGA-HT-7860 | 0 | 09996338 | 000036614697 | 0 | 099890125 | 00010987312 | 3 | 00023981468 | 004139189 | 095620996 | ||||||||||||
TCGA-LGG | TCGA-HT-7874 | 1 | 027373514 | 07262649 | 1 | 061277324 | 03872268 | 3 | 030690825 | 04321765 | 026091516 | ||||||||||||
TCGA-LGG | TCGA-HT-7879 | 1 | 006545533 | 09345446 | 0 | 07643643 | 023563562 | 3 | 07316188 | 01349482 | 0133433 | ||||||||||||
TCGA-LGG | TCGA-HT-7882 | 0 | 099826247 | 00017375927 | 0 | 099920684 | 0000793176 | 3 | 00026636408 | 0011237644 | 098609877 | ||||||||||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0045437213 | 09545628 | 0 | 09804874 | 0019512545 | 2 | 067944294 | 020575646 | 011480064 | ||||||||||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0090937115 | 09090629 | 0 | 08061669 | 019383314 | 2 | 069696444 | 017501967 | 012801588 | ||||||||||||
TCGA-LGG | TCGA-HT-8105 | 1 | 09291196 | 0070880495 | 1 | 09865976 | 0013402403 | 3 | 036353382 | 007970196 | 055676425 | ||||||||||||
TCGA-LGG | TCGA-HT-8106 | 1 | 09987081 | 00012918457 | 0 | 099922514 | 00007748164 | 3 | 0019909225 | 0057560045 | 09225307 | ||||||||||||
TCGA-LGG | TCGA-HT-8107 | 0 | 006150854 | 09384914 | 0 | 018944609 | 08105539 | 2 | 071847403 | 015055439 | 013097167 | ||||||||||||
TCGA-LGG | TCGA-HT-8111 | 1 | 062096643 | 037903354 | 0 | 09220272 | 007797278 | 3 | 00015017459 | 090417147 | 009432678 | ||||||||||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0003941571 | 099605846 | 0 | 00025608707 | 099743915 | 2 | 088384247 | 009021307 | 002594447 | ||||||||||||
TCGA-LGG | TCGA-HT-8114 | 1 | 09404078 | 005959219 | 0 | 09970708 | 00029292419 | 3 | 0015292862 | 028022403 | 07044831 | ||||||||||||
TCGA-LGG | TCGA-HT-8563 | 1 | 099999154 | 843094E-06 | 0 | 099999607 | 39515203E-06 | 3 | 32918017E-06 | 031742522 | 068257153 | ||||||||||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 06915494 | 030845058 | 0 | 045883363 | 054116637 | 3 | 012257236 | 02777765 | 059965116 | ||||||||||||
TCGA-LGG | TCGA-HT_A614 | 1 | 08180474 | 018195263 | 0 | 09584989 | 00415011 | 2 | 0067164555 | 0059489973 | 087334543 | ||||||||||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0035779487 | 09642206 | 0 | 07277821 | 0272218 | 2 | 07607039 | 014429174 | 009500429 |
Patient screening
Train set2181 Glioma patients
1241 Erasmus MC491 Haaglanden Medical Center168 BraTS130 REMBRANDT66 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set461 Glioma patients
199 TCGA-LGG262 TCGA-GBM
Patient inclusion
Train set1508 Patients in train set
816 Erasmus MC279 Haaglanden Medical Center168 BraTS109 REMBRANDT51 CPTAC-GBM39 Ivy GAP20 Amsterdam UMC20 Brain-Tumor-Progression6 University Medical Center Utrecht
Test set240 Patients in test set
107 TCGA-LGG133 TCGA-GBM
Patient exclusion
Train set673 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
425 Erasmus MC212 Haaglanden Medical Center
0 BraTS21 REMBRANDT15 CPTAC-GBM0 Ivy GAP0 Amsterdam UMC0 Brain-Tumor-Progression0 University Medical Center Utrecht
Test set221 No pre-operative
pre- or post-contrast T1wT2w or T2w-FLAIR
92 TCGA-LGG129 TCGA-GBM
Figure 2 Inclusion flowchart of the train set and test set
6
22 Algorithm performance
We used 15 of the train set as a validation set and selected the model pa-rameters that achieved the best performance on this validation set where themodel achieved an area under receiver operating characteristic curve (AUC) of088 for the IDH mutation status prediction an AUC of 076 for the 1p19qco-deletion prediction an AUC of 075 for the grade prediction and a meansegmentation DICE score of 081 The selected model parameters are shown inAppendix F We then trained a model using these parameters and the full trainset and evaluated it on the independent test set
For the genetic and histological feature predictions we achieved an AUC of090 for the IDH mutation status prediction an AUC of 085 for the 1p19qco-deletion prediction and an AUC of 081 for the grade prediction in thetest set The full results are shown in Table 2 with the corresponding receiveroperating characteristic (ROC)-curves in Figure 3 Table 2 also shows the resultsin (clinically relevant) subgroups of patients This shows that we achieved anIDH-AUC of 081 in low grade glioma (LGG) (grade IIIII) an IDH-AUC of064 in high grade glioma (HGG) (grade IV) and a 1p19q-AUC of 073 in LGGWhen only predicting LGG vs HGG instead of predicting the individual gradeswe achieved an AUC of 091 In Appendix A we provide confusion matrices forthe IDH 1p19q and grade predictions as well as a confusion matrix for thefinal WHO 2016 subtype which shows that only one patient was predicted asa non-existing WHO 2016 subtype In Appendix C we provide the individualpredictions and ground truth labels for all patients in the test set to allow forthe calculation of additional metrics
For the automatic segmentation we achieved a mean DICE score of 084 amean Hausdorff distance of 189 mm and a mean volumetric similarity coeffi-cient of 090 Figure 4 shows boxplots of the DICE scores Hausdorff distancesand volumetric similarity coefficients for the different patients in the test set InAppendix B we show five patients that were randomly selected from both theTCGA-LGG and TCGA-GBM data collections to demonstrate the automaticsegmentations made by our method
23 Model interpretability
To provide insight into the behavior of our model we created saliency mapswhich show which parts of the scans contributed the most to the predictionThese saliency maps are shown in Figure 5 for two example patients from thetest set It can be seen that for the LGG the network focused on a bright rim inthe T2w-FLAIR scan whereas for the HGG it focused on the enhancement in thepost-contrast T1w scan To aid further interpretation we provide visualizationsof selected filter outputs in the network in Appendix D which also show thatthe network focuses on the tumor and these filters seem to recognize specificimaging features such as the contrast enhancement and T2w-FLAIR brightness
7
Table 2 Evaluation results of the final model on the test set
Patientgroup
Task AUC Accuracy Sensitivity Specificity
All IDH 090 084 072 0931p19q 085 089 039 095Grade (IIIIIIV) 081 071 NA NAGrade II 091 086 075 089Grade III 069 075 017 094Grade IV 091 082 095 066LGG vs HGG 091 084 072 093
LGG IDH 081 074 073 0771p19q 073 076 039 089
HGG IDH 064 094 040 096
Abbreviations AUC area under receiver operating characteristic curve IDHisocitrate dehydrogenase LGG low grade glioma HGG high grade glioma
Figure 3 Receiver operating characteristic (ROC)-curves of the genetic andhistological features evaluated on the test set The crosses indicate the locationof the decision threshold for the reported accuracy sensitivity and specificity
8
Figure 4 DICE scores Hausdorff distances and volumetric similarity coeffi-cients for all patients in the test set The DICE score is a measure of theoverlap between the ground truth and predicted segmentation (where 1 indi-cates perfect overlap) The Hausdorff distance is a measure of the agreementbetween the boundaries of the ground truth and predicted segmentation (loweris better) The volumetric similarity coefficient is a measure of the agreementin volume (where 1 indicates perfect agreement)
9
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Saliency maps of a low grade glioma patient (TCGA-DU-6400) This is an IDH mu-tated 1p19q co-deleted grade II tumor The network focuses on a rim of brightnessin the T2w-FLAIR scan
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) Saliency maps of a high grade glioma patient (TCGA-06-0238) This is an IDHwildtype grade IV tumor The network focuses on enhancing spots around the necrosison the post-contrast T1w scan
Figure 5 Saliency maps of two patients from the test set showing areas thatare relevant for the prediction
10
24 Model robustness
By not excluding scans from our train set based on radiological characteristicswe were able to make our model robust to low scan quality as can be seen in anexample from the test set in Figure 6 Even though this example scan containedimaging artifacts our method was able to properly segment the tumor (DICEscore of 087) and correctly predict the tumor as an IDH wildtype grade IVtumor
Figure 6 Example of a T2w-FLAIR scan containing imaging artifacts and theautomatic segmentation (red overlay) made by our method It was correctlypredicted as an IDH wildtype grade IV glioma This is patient TCGA-06-5408from the TCGA-GBM collection
Finally we considered two examples of scans that were incorrectly predictedby our method see Figure 7 These two examples were chosen because ournetwork assigned high prediction scores to the wrong classes for these casesFigure 7a shows an example of a grade II IDH mutated 1p19q co-deletedglioma that was predicted as grade IV IDH wildtype by our method Ourmethodrsquos prediction was most likely caused by the hyperintensities in the post-contrast T1w scan being interpreted as contrast enhancement Since these hy-perintensities are also present in the pre-contrast T1w scan they are most likelycalcifications and the radiological appearance of this tumor is indicative of anoligodendroglioma Figure 7b shows an example of a grade IV IDH wildtypeglioma that was predicted as a grade III IDH mutated glioma by our method
11
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) TCGA-DU-6410 from the TCGA-LGG collection The ground truth histopatho-logical analysis indicated this glioma was grade II IDH mutated 1p19q co-deletedbut our method predicted it as a grade IV IDH wildtype
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) TCGA-76-7664 from the TCGA-HGG collection Histopathologically this gliomawas grade IV IDH wildtype but our method predicted it as grade III IDH mutated
Figure 7 Examples of scans that were incorrectly predicted by our method
12
3 Discussion
We have developed a method that can predict the IDH mutation status 1p19qco-deletion status and grade of glioma while simultaneously providing the tu-mor segmentation based on pre-operative MRI scans For the genetic andhistological feature predictions we achieved an AUC of 090 for the IDH mu-tation status prediction an AUC of 085 for the 1p19q co-deletion predictionand an AUC of 081 for the grade prediction in the test set
In an independent test set which contained data from 13 different instituteswe demonstrated that our method predicts these features with good overall per-formance we achieved an AUC of 090 for the IDH mutation status predictionan AUC of 085 for the 1p19q co-deletion prediction and an AUC of 081 forthe grade prediction and a mean whole tumor DICE score of 084 This per-formance on unseen data that was only used during the final evaluation of thealgorithm and that was purposefully not used to guide any decisions regardingthe method design shows the true generalizability of our method Using thelatest GPU capabilities we were able to train a large model which uses thefull 3D scan as input Furthermore by using the largest most diverse patientcohort to date we were able to make our method robust to the heterogeneitythat is naturally present in clinical imaging data such that it generalizes forbroad application in clinical practice
By using a multi-task network our method could learn the context betweendifferent features For example IDH wildtype and 1p19q co-deletion are mu-tually exclusive [21] If two separate methods had been used one to predict theIDH status and one to predict the 1p19q co-deletion status an IDH wildtypeglioma might be predicted to be 1p19q co-deleted which does not stroke withthe clinical reality Since our method learns both of these genetic features si-multaneously it correctly learned not to predict 1p19q co-deletion in tumorsthat were IDH wildtype there was only one patient in which our algorithm pre-dicted a tumor to be both IDH wildtype and 1p19q co-deleted Furthermoreby predicting the genetic and histological features individually instead of onlypredicting the WHO 2016 category it is possible to adopt updated guidelinessuch as cIMPACT-NOW future-proofing our method [22]
Some previous studies also used multi-task networks to predict the geneticand histological features of glioma [23 24 25] Tang et al [23] used a multi-tasknetwork that predicts multiple genetic features as well as the overall survivalof glioblastoma Since their method only works for glioblastoma patients thetumor grade must be known in advance complicating the use of their methodin the pre-operative setting when tumor grade is not yet known Furthermoretheir method requires a tumor segmentation prior to application of their methodwhich is a time-consuming expert task In a study by Xue et al [24] a multi-task network was used with a structure similar to the one proposed in thispaper to segment the tumor and predict the grade (LGG or HGG) and IDHmutation status However they do not predict the 1p19q co-deletion statusneeded for the WHO 2016 categorization Lastly Decuyper et al [25] useda multi-task network that predicts the IDH mutation and 1p19q co-deletion
13
status and the tumor grade (LGG or HGG) Their method requires a tumorsegmentation as input which they obtain from a U-Net that is applied earlierin their pipeline thus their method requires two networks instead of the singlenetwork we use in our method These differences aside the most importantlimitation of each of these studies is the lack of an independent test set forevaluating their results It is now considered essential that an independent testset is used to prevent an overly optimistic estimate of a methodrsquos performance[15 26 27 28] Thus our study improves on this previous work by providinga single network that combines the different tasks being trained on a moreextensive and diverse dataset not requiring a tumor segmentation as an in-put providing all information needed for the WHO 2016 categorization andcrucially by being evaluated in an independent test set
An important genetic feature that is not predicted by our method is theO6-methylguanine-methyltransferase (MGMT) methylation status Althoughthe MGMT methylation status is not part of the WHO 2016 categorizationit is part of clinical management guidelines and is an important prognosticmarker in glioblastoma [4] In the initial stages of this study we attemptedto predict the MGMT methylation status however the performance of thisprediction was poor Furthermore the methylation cutoff level which is usedto determine whether a tumor is MGMT methylated shows a wide varietybetween institutes leading to inconsistent results [29] We therefore opted not toinclude the MGMT prediction at all rather than to provide a poor prediction ofan unsharply defined parameter Although some methods attempted to predictthe MGMT status with varying degrees of success there is still an ongoingdiscussion on the validity of MR imaging features of the MGMT status [23 3031 32 33]
Our method shows good overall performance but there are noticeable per-formance differences between tumor categories For example when our methodpredicts a tumor as an IDH wildtype glioblastoma it is correct almost all of thetime On the other hand it has some difficulty differentiating IDH mutated1p19q co-deleted low-grade glioma from other low-grade glioma The sensitiv-ity for the prediction of grade III glioma was low which might be caused bythe lack of a central pathology review Because of this there were differences inmolecular testing and histological analysis and it is known that distinguishingbetween grade II and grade III has a poor observer reliability [34] Althoughour method can be relevant for certain subgroups our methodrsquos performancestill needs to be improved to ensure relevancy for the full patient population
In future work we aim to increase the performance of our method by includ-ing perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI)since there has been an increasing amount of evidence that these physiologi-cal imaging modalities contain additional information that correlates with thetumorrsquos genetic status and aggressiveness [35 36] They were not included inthis study since PWI and to a lesser extent DWI are not as ingrained in theclinical imaging routine as the structural scans used in this work [19 20] Thusincluding these modalities would limit our methodrsquos clinical applicability andsubstantially reduce the number of patients in the train and test set However
14
PWI and DWI are increasingly becoming more commonplace which will allowincluding these in future research and which might improve performance
In conclusion we have developed a non-invasive method that can predict theIDH mutation status 1p19q co-deletion status and grade of glioma while atthe same time segmenting the tumor based on pre-operative MRI scans withhigh overall performance Although the performance of our method might needto be improved before it will find widespread clinical acceptance we believe thatthis research is an important step forward in the field of radiomics Predict-ing multiple clinical features simultaneously steps away from the conventionalsingle-task methods and is more in line with the clinical practice where multipleclinical features are considered simultaneously and may even be related Fur-thermore by not limiting the patient population used to develop our method toa selection based on clinical or radiological characteristics we alleviate the needfor a priori (expert) knowledge which may not always be available Althoughsteps still have to be taken before radiomics will find its way into the clinicespecially in terms of performance our work provides a crucial step forward byresolving some of the hurdles of clinical implementation now and paving theway for a full transition in the future
4 Methods
41 Patient population
The train set was collected from four in-house datasets and five publicly avail-able datasets In-house datasets were collected from four different institutesErasmus MC (EMC) Haaglanden Medical Center (HMC) Amsterdam UMC(AUMC) [37] and University Medical Center Utrecht (UMCU) Four of the fivepublic datasets were collected from The Cancer Imaging Archive (TCIA) [38]the Repository of Molecular Brain Neoplasia Data (REMBRANDT) collection[39] the Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multi-forme (CPTAC-GBM) collection [40] the Ivy Glioblastoma Atlas Project (IvyGAP) collection [41 42] and the Brain-Tumor-Progression collection [43] Thefifth dataset was the 2019 Brain Tumor Segmentation challenge (BraTS) chal-lenge dataset [44 45 46] from which we excluded the patients that were alsoavailable in the TCGA-LGG and TCGA-GBM collections [47 48]
For the internal datasets from the EMC and the HMC manual segmentationswere available which were made by four different clinical experts For patientswhere segmentations from more than one observer were available we randomlypicked one of the segmentations to use in the train set The segmentationsfrom the AUMC data were made by a single observer of the study by Visseret al [37] From the public datasets only the BraTS dataset and the Brain-Tumor-Progression dataset provided manual segmentations Segmentations ofthe BraTS dataset as provided in the 2019 training and validation set wereused For the Brain-Tumor-Progression dataset the segmentations as providedin the TCIA data collection were used
15
Patients were included if pre-operative pre- and post-contrast T1w T2wand T2w-FLAIR scans were available no further inclusion criteria were setFor example patients were not excluded based on the radiological characteristicsof the scan such as low imaging quality or imaging artifacts or the gliomarsquosclinical characteristics such as the grade If multiple scans of the same contrasttype were available in a single scan session (eg multiple T2w scans) the scanupon which the segmentation was made was selected If no segmentation wasavailable or the segmentation was not made based on that scan contrast thescan with the highest axial resolution was used where a 3D acquisition waspreferred over a 2D acquisition
For the in-house data genetic and histological data were available for theEMC HMC and UMCU dataset which were obtained from analysis of tumortissue after biopsy or resection Genetic and histological data of the publicdatasets were also available for the REMBRANDT CPTAC-GBM and IvyGAP collections Data for the REMBRANDT and CPTAC-GBM collectionswas collected from the clinical data available at the TCIA [40 39] For theIvy GAP collection the genetic and histological data were obtained from theSwedish Institute at httpsivygapswedishorghome
As a test set we used the TCGA-LGG and TCGA-GBM collections from theTCIA [47 48] Genetic and histological labels were obtained from the clinicaldata available at the TCIA Segmentations were used as available from theTCIA based on the 2018 BraTS challenge [45 49 50] The inclusion criteriafor the patients included in the BraTS challenge were the same as our inclusioncriteria the presence of a pre-operative pre- and post-contrast T1w T2w andT2w-FLAIR scan Thus patients from the TCGA-LGG and TCGA-GBM wereincluded if a segmentation from the BraTS challenge was available Howeverfor three patients we found that although they did have manual segmentationsthey did not meet our inclusion requirements TCGA-08-0509 and TCGA-08-0510 from TCGA-GBM because they did not have a pre-contrast T1w scan andTCGA-FG-7634 from TCGA-LGG because there was no post-contrast T1wscan
42 Automatic segmentation in the train set
To present our method with a large diversity in scans we wanted to include asmany patients in the train set as possible from the different datasets Thereforewe performed automatic segmentation in patients that did not have manualsegmentations To this end we used an initial version of our network (presentedin Section 44) without the additional layers that were needed for the predictionof the genetic and histological features This network was initially trained usingall patients in the train set for whom a manual segmentation was availableand this trained network was then applied to all patients for which a manualsegmentation was not available The resulting automatic segmentations wereinspected and if their quality was acceptable they were added to the trainset The network was then trained again using this increased dataset and wasapplied to scans that did not yet have a segmentation of acceptable quality
16
This process was repeated until an acceptable segmentation was available forall patients which constituted our final complete train set
43 Pre-processing
For all datasets except for the BraTS dataset for which the scans were alreadyprovided in NIfTI format the scans were converted from DICOM format toNIfTI format using dcm2niix version v1020190410 [51] We then registeredall scans to the MNI152 T1w and T2w atlases version ICBM 2009a whichhad a resolution of 1x1x1 mm3 and a size of 197x233x189 voxels [52 53] Thescans were affinely registered using Elastix 50 [54 55] The pre- and post-contrast T1w scans were registered to the T1w atlas the T2w and T2w-FLAIRscans were registered to the T2w atlas When a manual segmentation wasavailable for patients from the in-house datasets the registration parametersthat resulted from registering the scan used during the segmentation were usedto transform the segmentation to the atlas In the case of the public datasetswe used the registration parameters of the T2w-FLAIR scans to transform thesegmentations
After the registration all scans were N4 bias field corrected using SimpleITKversion 124 [56] A brain mask was made for the atlas using HD-BET both forthe T1w atlas and the T2w atlas [57] This brain mask was used to skull stripall registered scans and crop them to a bounding box around the brain maskreducing the amount of background present in the scans resulting in a scan sizeof 152x182x145 voxels Subsequently the scans were normalized such that foreach scan the average image intensity was 0 and the standard deviation of theimage intensity was 1 within the brain mask Finally the background outsidethe brain mask was set to the minimum intensity value within the brain mask
Since the segmentation could sometimes be rugged at the edges after regis-tration especially when the segmentations were initially made on low-resolutionscans we smoothed the segmentation using a 3x3x3 median filter (this was onlydone in the train set) For segmentations that contained more than one la-bel eg when the tumor necrosis and enhancement were separately segmentedall labels were collapsed into a single label to obtain a single segmentation ofthe whole tumor The genetic and histological labels and the segmentations ofeach patient were one-hot encoded The four scans ground truth labels andsegmentation of each patient were then used as the input to the network
44 Model
We based the architecture of our model on the U-Net architecture with someadaptations made to allow for a full 3D input and the auxiliary tasks [58] Ournetwork architecture which we have named PrognosAIs Structure-Net or PS-Net for short can be seen in Figure 8
To use the full 3D scan as an input to the network we replaced the firstpooling layer that is usually present in the U-Net with a strided convolutionwith a kernel size of 9x9x9 and a stride of 3x3x3 In the upsampling branch of
17
32
32 64
128
256
512 256
7x8x7256 128
128 64
64 32
32 2
Segmentation
145x182x152
49x61x51
25x31x26
13x16x13
1472
512 2IDH
512 2
1p19q
512 3Grade
Batch normalization Concatenation Convolution amp ReLU3x3x3
Convolution amp Softmax1x1x1
(De)convolution amp ReLU9x9x9
stride 3x3x3
Dense amp ReLU Dense amp Softmax Dropout
Max pooling2x2x2
Up-convolution amp ReLU2x2x2
Global maxpooling
Figure 8 Overview of the PrognosAIs Structure-Net (PS-Net) architecture usedfor our model The numbers below the different layers indicate the number offilters dense units or features at that layer We have also indicated the featuremap size at the different depths of the network
the network the last up-convolution is replaced by a deconvolution with thesame kernel size and stride
At each depth of the network we have added global max-pooling layersdirectly after the dropout layer to obtain imaging features that can be used topredict the genetic and histological features We chose global pooling layers asthey do not introduce any additional parameters that need to be trained thuskeeping the memory required by our model manageable The features from thedifferent depths of the network were concatenated and fed into three differentdense layers one for each of the genetic and histological outputs
l2 kernel regularization was used in all convolutional layers except for thelast convolutional layer used for the output of the segmentation In total thismodel contained 27042473 trainable an 2944 non-trainable parameters
18
45 Model training
Training of the model was done on eight NVidia RTX2080Tirsquos with 11GB ofmemory using TensorFlow 220 [59] To be able to use the full 3D scan asinput to the network without running into memory issues we had to optimizethe memory efficiency of the model training Most importantly we used mixed-precision training which means that most of the variables of the model (such asthe weights) were stored in float16 which requires half the memory of float32which is typically used to store these variables [60] Only the last softmaxactivation layers of each output were stored as float32 We also stored ourpre-processed scans as float16 to further reduce memory usage
However even with these settings we could not use a batch size larger than1 It is known that a larger batch size is preferable as it increases the stabilityof the gradient updates and allows for a better estimation of the normalizationparameters in batch normalization layers [61] Therefore we distributed thetraining over the eight GPUs using the NCCL AllReduce algorithm whichcombines the gradients calculated on each GPU before calculating the updateto the model parameters [62] We also used synchronized batch normalizationlayers which synchronize the updates of their parameters over the distributedmodels In this way our model had a virtual batch size of eight for the gradientupdates and the batch normalization layers parameters
To provide more samples to the algorithm and prevent potential overtrain-ing we applied four types of data augmentation during training croppingrotation brightness shifts and contrast shifts Each augmentation was appliedwith a certain augmentation probability which determined the probability ofthat augmentation type being applied to a specific sample When an image wascropped a random number of voxels between 0 and 20 was cropped from eachdimension and filled with zeros For the random rotation an angle betweenminus30 and 30 degrees was selected from a uniform distribution for each dimen-sion The brightness shift was applied with a delta uniformly drawn between0 and 02 and the contrast shift factor was randomly drawn between 085 and115 We also introduced an augmentation factor which determines how ofteneach sample was parsed as an input sample during a single epoch where eachtime it could be augmented differently
For the IDH 1p19q and grade output we used a masked categorical cross-entropy loss and for the segmentation we used a DICE loss see Appendix E fordetails We used AdamW as an optimizer which has shown improved general-ization performance over Adam by introducing the weight decay parameter as aseparate parameter from the learning rate [63] The learning rate was automat-ically reduced by a factor of 025 if the loss did not improve during the last fiveepochs with a minimum learning rate of 1 middot 10minus11 The model could train for amaximum of 150 epochs and training was stopped early if the average loss overthe last five epochs did not improve Once the model was finished training theweights from the epoch with the lowest loss were restored
19
46 Hyperparameter tuning
Hyperparameters involved in the training of the model needed to be tuned toachieve the best performance We tuned a total of six hyper parameters the l2-norm the dropout rate the augmentation factor the augmentation probabilitythe optimizerrsquos initial learning rate and the optimizerrsquos weight decay A fulloverview of the trained parameters and the values tested for the different settingsis presented in Appendix F
To tune these hyperparameters we split the train set into a hyperparametertraining set (851282 patients of the full train data) and a hyperparametervalidation set (15226 patients of the full train data) Models were trained fordifferent hyperparameter settings via an exhaustive search using the hyperpa-rameter train set and then evaluated on the hyperparameter validation set Nodata augmentation was applied to the hyperparameter validation to ensure thatresults between trained models were comparable The hyperparameters that ledto the lowest overall loss in the hyperparameter validation set were chosen asthe optimal hyperparameters We trained the final model using these optimalhyperparameters and the full train set
47 Post-processing
The predictions of the network were post-processed to obtain the final predictedlabels and segmentations for the samples Since softmax activations were usedfor the genetic and histological outputs a prediction between 0 and 1 was out-putted for each class where the individual predictions summed to 1 The finalpredicted label was then considered as the class with the highest prediction scoreFor the prediction of LGG (grade IIIII) vs HGG (grade IV) the predictionscores of grade II and grade III were combined to obtain the prediction scorefor LGG the prediction score of grade IV was used as the prediction score forHGG If a segmentation contained multiple unconnected components we onlyretained the largest component to obtain a single whole tumor segmentation
48 Model evaluation
The performance of the final trained model was evaluated on the independenttest set comparing the predicted labels with the ground truth labels For thegenetic and histological features we evaluated the AUC the accuracy the sen-sitivity and the specificity using scikit-learn version 0231 for details see Ap-pendix G [64] We evaluated these metrics on the full test set and in subcate-gories relevant to the WHO 2016 guidelines We evaluated the IDH performanceseparately in the LGG (grade IIIII) and HGG (grade IV) subgroups the 1p19qperformance in LGG and we also evaluated the performance of distinguishingbetween LGG and HGG instead of predicting the individual grades
To evaluate the performance of the segmentation we calculated the DICEscores Hausdorff distances and volumetric similarity coefficient comparing theautomatic segmentation of our method and the manual ground truth segmen-
20
tations for all patients in the test set These metrics were calculated usingthe EvaluateSegmentation toolbox version 20170425 [65] for details see Ap-pendix G
To prevent an overly optimistic estimation of our modelrsquos predictive valuewe only evaluated our model on the test set once all hyperparameters werechosen and the final model was trained In this way the performance in thetest set did not influence decisions made during the development of the modelpreventing possible overfitting by fine-tuning to the test set
To gain insight into the model we made saliency maps that show whichparts of the scan contribute the most to the prediction of the CNN [66] Saliencymaps were made using tf-keras-vis 052 changing the activation function of alloutput layers from softmax to linear activations using SmoothGrad to reducethe noisiness of the saliency maps [66]
Another way to gain insight into the networkrsquos behavior is to visualize thefilter outputs of the convolutional layers as they can give some idea as to whatoperations the network applies to the scans We visualized the filter outputs ofthe last convolutional layers in the downsample and upsample path at the firstdepth (at an image size of 49x61x51) of our network These filter outputs werevisualized by passing a sample through the network and showing the convolu-tional layersrsquo outputs replacing the ReLU activation with linear activations
49 Data availability
An overview of the patients included from the public datasets used in the train-ing and testing of the algorithm and their ground truth label is available inAppendix H The data from the public datasets are available in TCIA un-der DOIs 107937K9TCIA2015588OZUZB 107937k9tcia20183rje41q1107937K9TCIA2016XLwaN6nL and 107937K9TCIA201815quzvnb Datafrom the BraTS are available at httpbraintumorsegmentationorg Datafrom the in-house datasets are not publicly available due to participant privacyand consent
410 Code availability
The code used in this paper is available on GitHub under an Apache 2 license athttpsgithubcomSvdvoortPrognosAIs_glioma This code includes thefull pipeline from registration of the patients to the final post-processing of thepredictions The trained model is also available on GitHub along with code toapply it to new patients
21
Appendices
A Confusion matrices
Tables 3 4 and 5 show the confusion matrices for the IDH 1p19q and gradepredictions and Table 6 shows the confusion matrix for the WHO 2016 subtypes
Table 4 shows that the algorithm mainly has difficulty recognizing 1p19qco-deleted tumors which are mostly predicted as 1p19q intact Table 5 showsthat most of the incorrectly predicted grade III tumors are predicted as gradeIV tumors
Table 6 shows that our algorithm often incorrectly predicts IDH-wildtypeastrocytoma as IDH-wildtype glioblastoma The latest cIMPACT-NOW guide-lines propose a new categorization in which IDH-wildtype astrocytoma thatshow either TERT promoter methylation or EFGR gene amplification or chro-mosome 7 gainchromsome 10 loss are classified as IDH-wildtype glioblastoma[22] This new categorization is proposed since the survival of patients withthose IDH-wildtype astrocytoma is similar to the survival of patients with IDH-wildtype glioblastoma [22] From the 13 IDH-wildtype astrocytoma that werewrongly predicted as IDH-wildtype glioblastoma 12 would actually be cate-gorized as IDH-wildtype glioblastoma under this new categorization Thusalthough our method wrongly predicted the WHO 2016 subtype it might ac-tually have picked up on imaging features related to the aggressiveness of thetumor which might lead to a better categorization
Table 3 Confusion matrix of the IDH predictions
Predicted
Wildtype Mutated
Actu
al
Wildtype 120 9
Mutated 25 63
Table 4 Confusion matrix of the 1p19q predictions
Predicted
Intact Co-deleted
Actu
al
Intact 197 10
Co-deleted 16 10
22
Table 5 Confusion matrix of the grade predictions
Predicted
Grade II Grade III Grade IV
Actu
al Grade II 35 6 6
Grade III 19 10 30
Grade IV 2 5 125
Table 6 Confusion matrix of the WHO 2016 predictions The rsquootherrsquo categoryindicates patients that were predicted as a non-existing WHO 2016 subtype forexample IDH wildtype 1p19q co-deleted tumors Only one patient (TCGA-HT-A5RC) was predicted as a non-existing category It was predicted as anIDH wildtype 1p19q co-deleted grade IV tumor
Predicted
Oligodendrogliom
a
IDH-m
utated
astrocytoma
IDH-w
ildtype
astrocytoma
IDH-m
utated
glioblastoma
IDH-w
ildtype
glioblastoma
Other
Actu
al
Oligodendroglioma 10 8 1 0 7 0
IDH-mutatedastrocytoma 6 34 4 3 10 0
IDH-wildtypeastrocytoma 1 2 3 2 13 1
IDH-mutatedglioblastoma 0 1 0 0 3 0
IDH-wildtypeglioblastoma 0 3 3 1 96 0
Oligodendroglioma are IDH-mutated 1p19q co-deleted grade IIIII giomaIDH-mutated astrocytoma are IDH-mutated 1p19q intact grade IIIII gliomaIDH-wildtype astrocytoma are IDH-wildtype 1p19q intact grade IIIII gliomaIDH-mutated glioblastoma are IDH-mutated grade IV gliomaIDH-wildtype glioblastoma are IDH-wildtype grade IV glioma
23
B Segmentation examples
To demonstrate the automatic segmentations made by our method we randomlyselected five patients from both the TCGA-LGG and the TCGA-GBM datasetThe scans and segmentations of the five patients from the TCGA-LGG datasetand the TCGA-GBM dataset are shown in Figures 9 and 10 respectively TheDICE score Hausdorff distance and volumetric similarity coefficient for thesepatients are given in Table 7 The method seems to mostly focus on the hyper-intensities of the T2w-FLAIR scan Despite the registrations issues that can beseen for the T2w scan in Figure 10d the tumor was still properly segmenteddemonstrating the robustness of our method
Patient DICE HD (mm) VSC
TCGA-LGG
TCGA-DU-7301 089 103 095TCGA-FG-5964 080 58 082TCGA-FG-A713 073 78 088TCGA-HT-7475 087 149 090TCGA-HT-8106 088 112 099
TCGA-GBM
TCGA-02-0037 082 226 099TCGA-08-0353 091 130 098TCGA-12-1094 090 73 093TCGA-14-3477 090 165 099TCGA-19-5951 073 197 073
Table 7 The DICE score Hausdorff distance (HD) and volumetric similaritycoefficient (VSC) for the randomly selected patients from the TCGA-LGG andTCGA-GBM data collections
24
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-DU-7301 from the TCGA-LGG data collection
(b) Patient TCGA-FG-5964 from the TCGA-LGG data collection
(c) Patient TCGA-FG-A713 from the TCGA-LGG data collection
(d) Patient TCGA-HT-7475 from the TCGA-LGG data collection
(e) Patient TCGA-HT-8106 from the TCGA-LGG data collection
Figure 9 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-LGG data collection
25
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-02-0037 from the TCGA-GBM data collection
(b) Patient TCGA-08-0353 from the TCGA-GBM data collection
(c) Patient TCGA-12-1094 from the TCGA-GBM data collection
(d) Patient TCGA-14-3477 from the TCGA-GBM data collection
(e) Patient TCGA-19-5951 from the TCGA-GBM data collection
Figure 10 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-GBM data collection
26
C Prediction results in the test set
27
D Filter output visualizations
Figures 11 and 12 show the output of the convolution filters for the same LGGpatient as shown in Figure 5a and Figures 13 and 14 show the output of theconvolution filters for the same HGG patient as shown in Figure 5b Figures 11and 13 show the outputs of the last convolution layer in the downsample pathat the feature size of 49x61x51 (the fourth convolutional layer in the network)Figures 12 and 14 show the outputs of the last convolution layer in the upsamplepath at the feature size of 49x61x51 (the nineteenth convolutional layer in thenetwork)
Comparing Figure 11 to Figure 12 and Figure 13 to Figure 14 we can seethat the convolutional layers in the upsample path do not keep a lot of detailfor the healthy part of the brain as this region seems blurred However withinthe tumor different regions can still be distinguished The different parts ofthe tumor from the scans can also be seen such as the contrast-enhancing partand the high signal intensity on the T2w-FLAIR For the grade IV glioma inFigure 14 some filters such as filter 26 also seem to focus on the necrotic partof the tumor
28
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 11 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
29
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 12 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
30
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 13 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-06-0238 This is an IDH wildtype grade IV glioma
31
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 14 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-06-0238 This is an IDH wildtype grade IV glioma
32
E Training losses
During the training of the network we used masked categorical cross-entropy lossfor the IDH 1p19q and grade outputs The normal categorical cross-entropyloss is defined as
LCEbatch = minus 1
Nbatch
sumj
sumiisinC
yij log (yij) (1)
where LCEbatch is the total cross-entropy loss over a batch yij is the ground truth
label of sample j for class i yij is the prediction score for sample j for class iC is the set of classes and Nbatch is the number of samples in the batch Here itis assumed that the ground truth labels are one-hot-encoded thus yij is either0 or 1 for each class In our case the ground truth is not known for all sampleswhich can be incorporated in Equation (1) by setting yij to 0 for all classesfor a sample for which the ground truth is not known That sample wouldthen not contribute to the overall loss and would not contribute to the gradientupdate However this can skew the total loss over a batch since the loss is stillaveraged over the total number of samples in a batch regardless of whether theground truth is known resulting in a lower loss for batches that contained moresamples with unknown ground truth Therefore we used a masked categoricalcross-entropy loss
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
yij log (yij) (2)
where
microbatchj =
Nbatchsumij yij
sumi
yij (3)
is the batch weight for sample j In this way the total batch loss is only averagedover the samples that actually have a ground truth
Since there was an imbalance between the number of ground truth samplesfor each class we used class weights to compensate for this imbalance Thusthe loss becomes
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
microclassi yij log (yij) (4)
where
microclassi =
N
Ni |C|(5)
is the class weight for class i N is the total number of samples with knownground truth Ni is the number of samples of class i and |C| is the number ofclasses By determining the class weight in this way we ensured that
microclassi Ni =
N
|C|= constant (6)
33
Thus each class would have the same contribution to the overall loss Theseclass weights were (individually) determined for the IDH output the 1p19qoutput and the grade output
For the segmentation output we used the DICE loss
LDICEbatch =
sumj
1minus 2 middotsumvoxels
k yjk middot yjksumvoxelsk yjk + yjk
(7)
where yjk is the ground truth label in voxel k of sample j and yjk is theprediction score outputted for voxel k of sample j
The total loss that was optimized for the model was a weighted sum of thefour individual losses
Ltotal =summ
micromLm (8)
with
microm =1
Xm (9)
where Lm is the loss for output m microm is the loss weight for loss m (either theIDH 1p19q grade or segmentation loss) and Xm is the number of sampleswith known ground truth for output m In this way we could counteract theeffect of certain outputs having more known labels than other outputs
34
F Parameter tuning
Table 8 Hyperparameters that were tuned and the values that were testedValues in bold show the selected values used in the final model
Tuning parameter Tested values
Dropout rate 015 02 025 030 035 040l2-norm 00001 000001 0000001Learning rate 001 0001 00001 000001 00000001Weight decay 0001 00001 000001Augmentation factor 1 2 3Augmentation probability 025 030 035 040 045
35
G Evaluation metrics
We calculated the AUC accuracy sensitivity and specificity metrics for thegenetic and histological features for the definitions of these metrics see [67]
For the IDH and 1p19q co-deletion outputs the IDH mutated and the1p19q co-deleted samples were regarded as the positive class respectively Sincethe grade was a multi-class problem no single positive class could be determinedFor the prediction of the individual grades that grade was seen as the positiveclass and all other grades as the negative class (eg in the case of the gradeIII prediction grade III was regarded as the positive class and grade II and IVwere regarded as the negative class) For the LGG vs HGG prediction LGGwas considered as the positive class and HGG as the negative class For theevaluation of these metrics for the genetic and histological features only thesubjects with known ground truth were taken into account
The overall AUC for the grade was a multi-class AUC determined in a one-vs-one approach comparing each class against the others in this way thismetric was insensitive to class imbalance [68] A multi-class accuracy was usedto determine the overall accuracy for the grade predictions [67]
To evaluate the performance of the automated segmentation we evaluatedthe DICE score the Hausdorff distance and the volumetric similarity coefficientThe DICE score is a measure of overlap between two segmentations where avalue of 1 indicates perfect overlap and the Hausdorff distance is a measureof the closeness of the borders of the segmentations The volumetric similaritycoefficient is a measure of the agreement between the volumes of two segmen-tations without taking account the actual location of the tumor where a valueof 1 indicates perfect agreement See [65] for the definitions of these metrics
36
H Ground truth labels of patients included frompublic datasets
Acknowledgments
Sebastian van der Voort and Fatih Incekara acknowledge funding by the DutchCancer Society (KWF project number EMCR 2015-7859)
Data used in this publication were generated by the National Cancer Insti-tute Clinical Proteomic Tumor Analysis Consortium (CPTAC)
The results published here are in whole or part based upon data generatedby the TCGA Research Network httpcancergenomenihgov
Author contributions
SRvdV FI WJN MS and SK contrived the study and designed theexperiments FI MMJW JWS RNT GJL PCDWH RSEAJPEV MJvdB and MS included patients in the different studiesSRvdV FI MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV MJvdB and MS collected the dataSRvdV carried out the experiments SRvdV FI MS and SK inter-preted the results SRvdV FI MJvdB MS and SK created the initialdraft of the paper MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV WJN and MJvdB revised the paper
References
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[2] Hendrikus J Dubbink Peggy N Atmodimedjo Johan M Kros Pim JFrench Marc Sanson Ahmed Idbaih Pieter Wesseling Roelien EntingWim Spliet Cees Tijssen Winand N M Dinjens Thierry Gorlia andMartin J van den Bent Molecular classification of anaplastic oligoden-droglioma using next-generation sequencing a report of the prospectiverandomized EORTC brain tumor group 26951 phase III trial Neuro-Oncology 18(3)388ndash400 March 2016 ISSN 1522-8517 URL https
doiorg101093neuoncnov182
[3] Jeanette E Eckel-Passow Daniel H Lachance Annette M Molinaro Kyle MWalsh Paul A Decker Hugues Sicotte Melike Pekmezci Terri Rice Matt LKosel Ivan V Smirnov Gobinda Sarkar Alissa A Caron Thomas M
37
Kollmeyer Corinne E Praska Anisha R Chada Chandralekha Halder He-len M Hansen Lucie S McCoy Paige M Bracci Roxanne Marshall ShichunZheng Gerald F Reis Alexander R Pico Brian P OrsquoNeill Jan C BucknerCaterina Giannini Jason T Huse Arie Perry Tarik Tihan Mitchell SBerger Susan M Chang Michael D Prados Joseph Wiemels John KWiencke Margaret R Wrensch and Robert B Jenkins Glioma groupsbased on 1p19q IDH and TERT promoter mutations in tumors NewEngland Journal of Medicine 372(26)2499ndash2508 June 2015 ISSN 0028-4793 URL httpsdoiorg101056NEJMoa1407279
[4] David N Louis Arie Perry Guido Reifenberger Andreas von Deimling Do-minique Figarella-Branger Webster K Cavenee Hiroko Ohgaki Otmar DWiestler Paul Kleihues and David W Ellison The 2016 world health orga-nization classification of tumors of the central nervous system a summaryActa Neuropathologica 131(6)803ndash820 June 2016 ISSN 0001-6322 URLhttpsdoiorg101007s00401-016-1545-1
[5] Ching-Chang Chen Peng-Wei Hsu Tai-Wei Erich Wu Shih-Tseng LeeChen-Nen Chang Kuo-chen Wei Chih-Cheng Chuang Chieh-Tsai WuTai-Ngar Lui Yung-Hsin Hsu Tzu-Kang Lin Sai-Cheung Lee and Yin-Cheng Huang Stereotactic brain biopsy Single center retrospective anal-ysis of complications Clinical Neurology and Neurosurgery 111(10)835ndash839 December 2009 ISSN 0303-8467 URL httpsdoiorg101016
jclineuro200908013
[6] Robert J Jackson Gregory N Fuller Dima Abi-Said Frederick F LangZiya L Gokaslan Wei Ming Shi David M Wildrick and Raymond SawayaLimitations of stereotactic biopsy in the initial management of gliomasNeuro-Oncology 3(3)193ndash200 July 2001 ISSN 1522-8517 URL https
doiorg101093neuonc33193
[7] M Zhou J Scott B Chaudhury L Hall D Goldgof KW Yeom M IvY Ou J Kalpathy-Cramer S Napel R Gillies O Gevaert and R GatenbyRadiomics in brain tumor Image assessment quantitative feature de-scriptors and machine-learning approaches American Journal of Neu-roradiology 39(2)208ndash216 February 2018 ISSN 0195-6108 URL https
doiorg103174ajnrA5391
[8] Wenya Linda Bi Ahmed Hosny Matthew B Schabath Maryellen LGiger Nicolai J Birkbak Alireza Mehrtash Tavis Allison Omar ArnaoutChristopher Abbosh Ian F Dunn Raymond H Mak Rulla M TamimiClare M Tempany Charles Swanton Udo Hoffmann Lawrence H SchwartzRobert J Gillies Raymond Y Huang and Hugo J W L Aerts Artificialintelligence in cancer imaging Clinical challenges and applications CAA Cancer Journal for Clinicians 69(2)caac21552 February 2019 ISSN0007-9235 URL httpsdoiorg103322caac21552
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[9] Ahmad Chaddad Michael Jonathan Kucharczyk Paul Daniel SihamSabri Bertrand J Jean-Claude Tamim Niazi and Bassam AbdulkarimRadiomics in glioblastoma Current status and challenges facing clinicalimplementation Frontiers in Oncology 9374ndash374 May 2019 ISSN 2234-943X URL httpsdoiorg103389fonc201900374
[10] Marion Smits Imaging of oligodendroglioma The British Journal of Ra-diology 89(1060)20150857 April 2016 ISSN 0007-1285 URL https
doiorg101259bjr20150857
[11] Rachel L Delfanti David E Piccioni Jason Handwerker Naeim BahramiAnithaPriya Krishnan Roshan Karunamuni Jona A Hattangadi-GluthTyler M Seibert Ashwin Srikant Karra A Jones Vivian S Snyder An-ders M Dale Nathan S White Carrie R McDonald and Nikdokht FaridImaging correlates for the 2016 update on WHO classification of gradeIIIII gliomas implications for IDH 1p19q and ATRX status Journal ofNeuro-Oncology 135(3)601ndash609 December 2017 ISSN 0167-594X URLhttpsdoiorg101007s11060-017-2613-7
[12] Sonal Gore Tanay Chougule Jayant Jagtap Jitender Saini and MadhuraIngalhalikar A review of radiomics and deep predictive modeling in gliomacharacterization Academic Radiology July 2020 ISSN 1076-6332 URLhttpsdoiorg101016jacra202006016
[13] Hugo J W L Aerts Emmanuel Rios Velazquez Ralph T H LeijenaarChintan Parmar Patrick Grossmann Sara Carvalho Johan BussinkRene Monshouwer Benjamin Haibe-Kains Derek Rietveld Frank HoebersMichelle M Rietbergen C Rene Leemans Andre Dekker John Quacken-bush Robert J Gillies and Philippe Lambin Decoding tumour pheno-type by noninvasive imaging using a quantitative radiomics approach Na-ture Communications 5(1)4006 September 2014 ISSN 2041-1723 URLhttpsdoiorg101038ncomms5006
[14] Sarah Chihati and Djamel Gaceb A review of recent progress in deeplearning-based methods for MRI brain tumor segmentation In 202011th International Conference on Information and Communication Sys-tems (ICICS) pages 149ndash154 Institute of Electrical and Electronics Engi-neers (IEEE) April 2020 ISBN 9781728162270 URL httpsdoiorg
101109icics494692020239550
[15] Robert J Gillies Paul E Kinahan and Hedvig Hricak Radiomics Imagesare more than pictures they are data Radiology 278(2)563ndash577 Febru-ary 2016 ISSN 0033-8419 URL httpsdoiorg101148radiol
2015151169
[16] James H Thrall Xiang Li Quanzheng Li Cinthia Cruz Synho Do KeithDreyer and James Brink Artificial intelligence and machine learning in ra-diology Opportunities challenges pitfalls and criteria for success Journal
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of the American College of Radiology 15(3 Part B)504ndash508 March 2018ISSN 1546-1440 URL httpsdoiorg101016jjacr201712026
[17] Ahmed Hosny Chintan Parmar John Quackenbush Lawrence H Schwartzand Hugo J W L Aerts Artificial intelligence in radiology Nature ReviewsCancer 18(8)500ndash510 August 2018 ISSN 1474-175X URL https
doiorg101038s41568-018-0016-5
[18] Okan Kopuklu Neslihan Kose Ahmet Gunduz and Gerhard Rigoll Re-source efficient 3D convolutional neural networks In 2019 IEEECVFInternational Conference on Computer Vision Workshop (ICCVW) Insti-tute of Electrical and Electronics Engineers (IEEE) October 2019 ISBN9781728150239 URL httpsdoiorg101109iccvw201900240
[19] S C Thust S Heiland A Falini H R Jager A D Waldman P C SundgrenC Godi V K Katsaros A Ramos N Bargallo M W Vernooij T YousryM Bendszus and M Smits Glioma imaging in europe A survey of 220centres and recommendations for best clinical practice European Radiology28(8)3306ndash3317 August 2018 ISSN 0938-7994 URL httpsdoiorg
101007s00330-018-5314-5
[20] Christian F Freyschlag Sandro M Krieg Johannes Kerschbaumer DanielPinggera Marie-Therese Forster Dominik Cordier Marco Rossi GabrieleMiceli Alexandre Roux Andres Reyes Silvio Sarubbo Anja Smits JoannaSierpowska Pierre A Robe Geert-Jan Rutten Thomas Santarius TomaszMatys Marc Zanello Fabien Almairac Lydiane Mondot Asgeir S JakolaMaria Zetterling Adria Rofes Gord von Campe Remy Guillevin DanieleBagatto Vincent Lubrano Marion Rapp John Goodden Philip C De WittHamer Johan Pallud Lorenzo Bello Claudius Thome Hugues Duffauand Emmanuel Mandonnet Imaging practice in low-grade gliomas amongeuropean specialized centers and proposal for a minimum core of imagingJournal of Neuro-Oncology 139(3)699ndash711 September 2018 ISSN 0167-594X URL httpsdoiorg101007s11060-018-2916-3
[21] M Labussiere A Idbaih X-W Wang Y Marie B Boisselier C FaletS Paris J Laffaire C Carpentier E Criniere F Ducray S El HallaniK Mokhtari K Hoang-Xuan J-Y Delattre and M Sanson All the 1p19qcodeleted gliomas are mutated on IDH1 or IDH2 Neurology 74(23)1886ndash1890 June 2010 ISSN 0028-3878 URL httpsdoiorg101212WNL
0b013e3181e1cf3a
[22] David N Louis Pieter Wesseling Kenneth Aldape Daniel J Brat DavidCapper Ian A Cree Charles Eberhart Dominique Figarella-BrangerMaryam Fouladi Gregory N Fuller Caterina Giannini Christine Haber-ler Cynthia Hawkins Takashi Komori Johan M Kros HK Ng Brent AOrr Sung-Hye Park Werner Paulus Arie Perry Torsten Pietsch GuidoReifenberger Marc Rosenblum Brian Rous Felix Sahm Chitra SarkarDavid A Solomon Uri Tabori Martin J Bent Andreas Deimling Michael
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Weller Valerie A White and David W Ellison cIMPACT-NOW up-date 6 new entity and diagnostic principle recommendations of thecIMPACT-Utrecht meeting on future CNS tumor classification and grad-ing Brain Pathology 30(4)844ndash856 July 2020 ISSN 1015-6305 URLhttpsdoiorg101111bpa12832
[23] Zhenyu Tang Yuyun Xu Zhicheng Jiao Junfeng Lu Lei Jin Abudumi-jiti Aibaidula Jinsong Wu Qian Wang Han Zhang and Dinggang ShenPre-operative overall survival time prediction for glioblastoma patients us-ing deep learning on both imaging phenotype and genotype In Ding-gang Shen Tianming Liu Terry M Peters Lawrence H Staib CarolineEssert Sean Zhou Pew-Thian Yap and Ali Khan editors Lecture Notesin Computer Science pages 415ndash422 Springer Science and Business Me-dia LLC 2019 ISBN 9783030322380 URL httpsdoiorg101007
978-3-030-32239-7_46
[24] Zhiyuan Xue Bowen Xin Dingqian Wang and Xiuying Wang Radiomics-enhanced multi-task neural network for non-invasive glioma subtypingand segmentation In Hassan Mohy-ud Din and Saima Rathore editorsRadiomics and Radiogenomics in Neuro-oncology pages 81ndash90 SpringerScience and Business Media LLC 2020 ISBN 9783030401238 URLhttpsdoiorg101007978-3-030-40124-5_9
[25] Milan Decuyper Stijn Bonte Karel Deblaere and Roel Van Holen Au-tomated MRI based pipeline for glioma segmentation and prediction ofgrade IDH mutation and 1p19q co-deletion 2020 Preprint at https
arxivorgabs200511965
[26] Stefania Rizzo Francesca Botta Sara Raimondi Daniela Origgi Cris-tiana Fanciullo Alessio Giuseppe Morganti and Massimo Bellomi Ra-diomics the facts and the challenges of image analysis European Ra-diology Experimental 2(1)36 December 2018 ISSN 2509-9280 URLhttpsdoiorg101186s41747-018-0068-z
[27] Philipp Lohmann Norbert Galldiks Martin Kocher Alexander HeinzelChristian P Filss Carina Stegmayr Felix M Mottaghy Gereon R FinkN Jon Shah and Karl-Josef Langen Radiomics in neuro-oncology Basicsworkflow and applications Methods June 2020 ISSN 1046-2023 URLhttpsdoiorg101016jymeth202006003
[28] Stephen S F Yip and Hugo J W L Aerts Applications and limitationsof radiomics Physics in Medicine and Biology 61(13)R150ndashR166 July2016 ISSN 0031-9155 URL httpsdoiorg1010880031-915561
13r150
[29] Annika Malmstrom Ma lgorzata Lysiak Bjarne Winther Kristensen Eliz-abeth Hovey Roger Henriksson and Peter Soderkvist Do we really knowwho has an MGMT methylated glioma Results of an international survey
41
regarding use of MGMT analyses for glioma Neuro-Oncology Practice 7(1)68ndash76 09 2019 ISSN 2054-2577 URL httpsdoiorg101093
nopnpz039
[30] Takahiro Sasaki Manabu Kinoshita Koji Fujita Junya Fukai NobuhideHayashi Yuji Uematsu Yoshiko Okita Masahiro Nonaka Shusuke Mo-riuchi Takehiro Uda Naohiro Tsuyuguchi Hideyuki Arita Kanji MoriKenichi Ishibashi Koji Takano Namiko Nishida Tomoko Shofuda EmaYoshioka Daisuke Kanematsu Yoshinori Kodama Masayuki ManoNaoyuki Nakao and Yonehiro Kanemura Radiomics and MGMT pro-moter methylation for prognostication of newly diagnosed glioblastomaScientific Reports 9(1)14435 December 2019 ISSN 2045-2322 URLhttpsdoiorg101038s41598-019-50849-y
[31] A Gupta A Prager RJ Young W Shi AMP Omuro and JJ GraberDiffusion-weighted MR imaging and MGMT methylation status in glioblas-toma A reappraisal of the role of preoperative quantitative ADC measure-ments American Journal of Neuroradiology 34(1)E10ndashE11 January 2013ISSN 0195-6108 URL httpsdoiorg103174ajnrA3467
[32] JA Carrillo A Lai PL Nghiemphu HJ Kim HS Phillips S KharbandaP Moftakhar S Lalaezari W Yong BM Ellingson TF Cloughesy andWB Pope Relationship between tumor enhancement edema IDH1 muta-tional status MGMT promoter methylation and survival in glioblastomaAmerican Journal of Neuroradiology 33(7)1349ndash1355 August 2012 ISSN0195-6108 URL httpsdoiorg103174ajnrA2950
[33] Vilde Elisabeth Mikkelsen Hong Yan Dai Anne Line Stensjoslashen Erik Mag-nus Berntsen Oslashyvind Salvesen Ole Solheim and Sverre Helge TorpMGMT promoter methylation status is not related to histological or radio-logical features in IDH wild-type glioblastomas Journal of Neuropathologyamp Experimental Neurology 79(8)855ndash862 August 2020 ISSN 0022-3069URL httpsdoiorg101093jnennlaa060
[34] Martin J van den Bent Interobserver variation of the histopathologicaldiagnosis in clinical trials on glioma a clinicianrsquos perspective Acta Neu-ropathologica 120(3)297ndash304 September 2010 ISSN 1432-0533 URLhttpsdoiorg101007s00401-010-0725-7
[35] Ji Eun Park Ho Sung Kim Youngheun Jo Roh-Eul Yoo Seung Hong ChoiSoo Jung Nam and Jeong Hoon Kim Radiomics prognostication modelin glioblastoma using diffusion- and perfusion-weighted MRI ScientificReports 10(1)4250 December 2020 ISSN 2045-2322 URL httpsdoi
org101038s41598-020-61178-w
[36] Minjae Kim So Yeong Jung Ji Eun Park Yeongheun Jo Seo YoungPark Soo Jung Nam Jeong Hoon Kim and Ho Sung Kim Diffusion-and perfusion-weighted MRI radiomics model may predict isocitrate dehy-drogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade
42
glioma European Radiology 30(4)2142ndash2151 April 2020 ISSN 0938-7994URL httpsdoiorg101007s00330-019-06548-3
[37] M Visser DMJ Muller RJM van Duijn M Smits N Verburg EJ HendriksRJA Nabuurs JCJ Bot RS Eijgelaar M Witte MB van Herk F BarkhofPC de WittHamer and JC de Munck Inter-rater agreement in glioma seg-mentations on longitudinal MRI NeuroImage Clinical 22101727 2019ISSN 2213-1582 URL httpsdoiorg101016jnicl2019101727
[38] Kenneth Clark Bruce Vendt Kirk Smith John Freymann Justin KirbyPaul Koppel Stephen Moore Stanley Phillips David Maffitt MichaelPringle Lawrence Tarbox and Fred Prior The cancer imaging archive(TCIA) Maintaining and operating a public information repository Jour-nal of Digital Imaging 26(6)1045ndash1057 December 2013 ISSN 0897-1889URL httpsdoiorg101007s10278-013-9622-7
[39] Lisa Scarpace Adam E Flanders Rajan Jain Tom Mikkelsen and David WAndrews Data from REMBRANDT The Cancer Imaging Archive 2015URL httpsdoiorg107937K9TCIA2015588OZUZB
[40] National Cancer Institute Clinical Proteomic Tumor Analysis Consortium(CPTAC) Radiology data from the clinical proteomic tumor analysisconsortium glioblastoma multiforme CPTAC-GBM collection The CancerImaging Archive 2018 URL httpsdoiorg107937k9tcia2018
3rje41q1
[41] Nameeta Shah Xu Feng Michael Lankerovich Ralph B Puchalski andBart Keogh Data from Ivy GAP The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016XLwaN6nL
[42] Ralph B Puchalski Nameeta Shah Jeremy Miller Rachel Dalley Steve RNomura Jae-Guen Yoon Kimberly A Smith Michael Lankerovich DarrenBertagnolli Kris Bickley Andrew F Boe Krissy Brouner Stephanie But-ler Shiella Caldejon Mike Chapin Suvro Datta Nick Dee Tsega DestaTim Dolbeare Nadezhda Dotson Amanda Ebbert David Feng Xu FengMichael Fisher Garrett Gee Jeff Goldy Lindsey Gourley Benjamin WGregor Guangyu Gu Nika Hejazinia John Hohmann Parvinder HothiRobert Howard Kevin Joines Ali Kriedberg Leonard Kuan Chris LauFelix Lee Hwahyung Lee Tracy Lemon Fuhui Long Naveed Mastan ErikaMott Chantal Murthy Kiet Ngo Eric Olson Melissa Reding Zack RileyDavid Rosen David Sandman Nadiya Shapovalova Clifford R Slaughter-beck Andrew Sodt Graham Stockdale Aaron Szafer Wayne WakemanPaul E Wohnoutka Steven J White Don Marsh Robert C RostomilyLydia Ng Chinh Dang Allan Jones Bart Keogh Haley R GittlemanJill S Barnholtz-Sloan Patrick J Cimino Megha S Uppin C Dirk KeeneFarrokh R Farrokhi Justin D Lathia Michael E Berens Antonio IavaroneAmy Bernard Ed Lein John W Phillips Steven W Rostad Charles Cobbs
43
Michael J Hawrylycz and Greg D Foltz An anatomic transcriptional at-las of human glioblastoma Science 360(6389)660ndash663 May 2018 ISSN0036-8075 URL httpsdoiorg101126scienceaaf2666
[43] Kathleen Schmainda and Melissa Prah Data from Brain-Tumor-Progression The Cancer Imaging Archive 2018 URL httpsdoiorg
107937K9TCIA201815quzvnb
[44] B H Menze A Jakab S Bauer J Kalpathy-Cramer K FarahaniJ Kirby Y Burren N Porz J Slotboom R Wiest L Lanczi E GerstnerM Weber T Arbel B B Avants N Ayache P Buendia D L CollinsN Cordier J J Corso A Criminisi T Das H Delingette C Demi-ralp C R Durst M Dojat S Doyle J Festa F Forbes E GeremiaB Glocker P Golland X Guo A Hamamci K M IftekharuddinR Jena N M John E Konukoglu D Lashkari J A Mariz R MeierS Pereira D Precup S J Price T R Raviv S M S Reza M RyanD Sarikaya L Schwartz H Shin J Shotton C A Silva N SousaN K Subbanna G Szekely T J Taylor O M Thomas N J Tusti-son G Unal F Vasseur M Wintermark D H Ye L Zhao B ZhaoD Zikic M Prastawa M Reyes and K Van Leemput The mul-timodal brain tumor image segmentation benchmark (BRATS) IEEETransactions on Medical Imaging 34(10)1993ndash2024 2015 URL https
doiorg101109TMI20142377694
[45] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin S Kirby John B Freymann Keyvan Farahani and ChristosDavatzikos Advancing the cancer genome atlas glioma MRI collectionswith expert segmentation labels and radiomic features Scientific Data 4(1)170117 December 2017 ISSN 2052-4463 URL httpsdoiorg10
1038sdata2017117
[46] Spyridon Bakas Mauricio Reyes Andras Jakab Stefan Bauer MarkusRempfler Alessandro Crimi Russell Takeshi Shinohara Christoph BergerSung Min Ha Martin Rozycki Marcel Prastawa Esther Alberts JanaLipkova John Freymann Justin Kirby Michel Bilello Hassan Fathallah-Shaykh Roland Wiest Jan Kirschke Benedikt Wiestler Rivka ColenAikaterini Kotrotsou Pamela Lamontagne Daniel Marcus MikhailMilchenko Arash Nazeri Marc-Andre Weber Abhishek Mahajan UjjwalBaid Elizabeth Gerstner Dongjin Kwon Gagan Acharya Manu Agar-wal Mahbubul Alam Alberto Albiol Antonio Albiol Francisco J AlbiolVarghese Alex Nigel Allinson Pedro H A Amorim Abhijit AmrutkarGanesh Anand Simon Andermatt Tal Arbel Pablo Arbelaez Aaron Av-ery Muneeza Azmat Pranjal B W Bai Subhashis Banerjee Bill BarthThomas Batchelder Kayhan Batmanghelich Enzo Battistella AndrewBeers Mikhail Belyaev Martin Bendszus Eze Benson Jose Bernal Ha-landur Nagaraja Bharath George Biros Sotirios Bisdas James BrownMariano Cabezas Shilei Cao Jorge M Cardoso Eric N Carver Adria
44
Casamitjana Laura Silvana Castillo Marcel Cata Philippe Cattin Al-bert Cerigues Vinicius S Chagas Siddhartha Chandra Yi-Ju ChangShiyu Chang Ken Chang Joseph Chazalon Shengcong Chen Wei ChenJefferson W Chen Zhaolin Chen Kun Cheng Ahana Roy ChoudhuryRoger Chylla Albert Clerigues Steven Colleman Ramiro German Ro-driguez Colmeiro Marc Combalia Anthony Costa Xiaomeng Cui Zhen-zhen Dai Lutao Dai Laura Alexandra Daza Eric Deutsch ChangxingDing Chao Dong Shidu Dong Wojciech Dudzik Zach Eaton-Rosen GaryEgan Guilherme Escudero Theo Estienne Richard Everson JonathanFabrizio Yong Fan Longwei Fang Xue Feng Enzo Ferrante Lucas Fi-don Martin Fischer Andrew P French Naomi Fridman Huan Fu DavidFuentes Yaozong Gao Evan Gates David Gering Amir Gholami WilliGierke Ben Glocker Mingming Gong Sandra Gonzalez-Villa T Gros-ges Yuanfang Guan Sheng Guo Sudeep Gupta Woo-Sup Han Il SongHan Konstantin Harmuth Huiguang He Aura Hernandez-Sabate EvelynHerrmann Naveen Himthani Winston Hsu Cheyu Hsu Xiaojun Hu Xi-aobin Hu Yan Hu Yifan Hu Rui Hua Teng-Yi Huang Weilin HuangSabine Van Huffel Quan Huo Vivek HV Khan M Iftekharuddin FabianIsensee Mobarakol Islam Aaron S Jackson Sachin R Jambawalikar An-drew Jesson Weijian Jian Peter Jin V Jeya Maria Jose Alain JungoB Kainz Konstantinos Kamnitsas Po-Yu Kao Ayush Karnawat ThomasKellermeier Adel Kermi Kurt Keutzer Mohamed Tarek Khadir Mahen-dra Khened Philipp Kickingereder Geena Kim Nik King Haley KnappUrspeter Knecht Lisa Kohli Deren Kong Xiangmao Kong Simon Kop-pers Avinash Kori Ganapathy Krishnamurthi Egor Krivov Piyush Ku-mar Kaisar Kushibar Dmitrii Lachinov Tryphon Lambrou Joon LeeChengen Lee Yuehchou Lee M Lee Szidonia Lefkovits Laszlo LefkovitsJames Levitt Tengfei Li Hongwei Li Wenqi Li Hongyang Li XiaochuanLi Yuexiang Li Heng Li Zhenye Li Xiaoyu Li Zeju Li XiaoGang LiWenqi Li Zheng-Shen Lin Fengming Lin Pietro Lio Chang Liu Bo-qiang Liu Xiang Liu Mingyuan Liu Ju Liu Luyan Liu Xavier LladoMarc Moreno Lopez Pablo Ribalta Lorenzo Zhentai Lu Lin Luo ZhigangLuo Jun Ma Kai Ma Thomas Mackie Anant Madabushi Issam Mah-moudi Klaus H Maier-Hein Pradipta Maji CP Mammen Andreas MangB S Manjunath Michal Marcinkiewicz S McDonagh Stephen McKennaRichard McKinley Miriam Mehl Sachin Mehta Raghav Mehta RaphaelMeier Christoph Meinel Dorit Merhof Craig Meyer Robert Miller Sush-mita Mitra Aliasgar Moiyadi David Molina-Garcia Miguel A B Mon-teiro Grzegorz Mrukwa Andriy Myronenko Jakub Nalepa Thuyen NgoDong Nie Holly Ning Chen Niu Nicholas K Nuechterlein Eric Oer-mann Arlindo Oliveira Diego D C Oliveira Arnau Oliver AlexanderF I Osman Yu-Nian Ou Sebastien Ourselin Nikos Paragios Moo SungPark Brad Paschke J Gregory Pauloski Kamlesh Pawar Nick PawlowskiLinmin Pei Suting Peng Silvio M Pereira Julian Perez-Beteta Vic-tor M Perez-Garcia Simon Pezold Bao Pham Ashish Phophalia GemmaPiella G N Pillai Marie Piraud Maxim Pisov Anmol Popli Michael P
45
Pound Reza Pourreza Prateek Prasanna Vesna Prkovska Tony P Prid-more Santi Puch Elodie Puybareau Buyue Qian Xu Qiao MartinRajchl Swapnil Rane Michael Rebsamen Hongliang Ren Xuhua RenKarthik Revanuru Mina Rezaei Oliver Rippel Luis Carlos Rivera Char-lotte Robert Bruce Rosen Daniel Rueckert Mohammed Safwan MostafaSalem Joaquim Salvi Irina Sanchez Irina Sanchez Heitor M Santos Em-mett Sartor Dawid Schellingerhout Klaudius Scheufele Matthew R ScottArtur A Scussel Sara Sedlar Juan Pablo Serrano-Rubio N Jon ShahNameetha Shah Mazhar Shaikh B Uma Shankar Zeina Shboul HaipengShen Dinggang Shen Linlin Shen Haocheng Shen Varun Shenoy FengShi Hyung Eun Shin Hai Shu Diana Sima M Sinclair Orjan SmedbyJames M Snyder Mohammadreza Soltaninejad Guidong Song MehulSoni Jean Stawiaski Shashank Subramanian Li Sun Roger Sun JiaweiSun Kay Sun Yu Sun Guoxia Sun Shuang Sun Yannick R Suter Las-zlo Szilagyi Sanjay Talbar Dacheng Tao Dacheng Tao Zhongzhao TengSiddhesh Thakur Meenakshi H Thakur Sameer Tharakan Pallavi Ti-wari Guillaume Tochon Tuan Tran Yuhsiang M Tsai Kuan-Lun TsengTran Anh Tuan Vadim Turlapov Nicholas Tustison Maria VakalopoulouSergi Valverde Rami Vanguri Evgeny Vasiliev Jonathan Ventura LuisVera Tom Vercauteren C A Verrastro Lasitha Vidyaratne Veronica Vi-laplana Ajeet Vivekanandan Guotai Wang Qian Wang Chiatse J WangWeichung Wang Duo Wang Ruixuan Wang Yuanyuan Wang ChunliangWang Guotai Wang Ning Wen Xin Wen Leon Weninger Wolfgang WickShaocheng Wu Qiang Wu Yihong Wu Yong Xia Yanwu Xu XiaowenXu Peiyuan Xu Tsai-Ling Yang Xiaoping Yang Hao-Yu Yang JunlinYang Haojin Yang Guang Yang Hongdou Yao Xujiong Ye ChangchangYin Brett Young-Moxon Jinhua Yu Xiangyu Yue Songtao Zhang AngelaZhang Kun Zhang Xuejie Zhang Lichi Zhang Xiaoyue Zhang YazhuoZhang Lei Zhang Jianguo Zhang Xiang Zhang Tianhao Zhang SichengZhao Yu Zhao Xiaomei Zhao Liang Zhao Yefeng Zheng Liming ZhongChenhong Zhou Xiaobing Zhou Fan Zhou Hongtu Zhu Jin Zhu YingZhuge Weiwei Zong Jayashree Kalpathy-Cramer Keyvan Farahani Chris-tos Davatzikos Koen van Leemput and Bjoern Menze Identifying the bestmachine learning algorithms for brain tumor segmentation progression as-sessment and overall survival prediction in the BRATS challenge 2018Preprint at httpsarxivorgabs181102629
[47] Nancy Pedano Adam E Flanders Lisa Scarpace Tom Mikkelsen Jen-nifer M Eschbacher Beth Hermes Victor Sisneros Jill Barnholtz-Sloanand Quinn Ostrom Radiology data from the cancer genome atlas lowgrade glioma [TCGA-LGG] collection The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016L4LTD3TK
[48] Lisa Scarpace Tom Mikkelsen Soonmee Cha Sujaya Rao SangeetaTekchandani David Gutman Joel H Saltz Bradley J Erickson NancyPedano Adam E Flanders Jill Barnholtz-Sloan Quinn Ostrom DanielBarboriak and Laura J Pierce Radiology data from the cancer genome
46
atlas glioblastoma multiforme [TCGA-GBM] collection The Cancer Imag-ing Archive 2016 URL httpsdoiorg107937K9TCIA2016
RNYFUYE9
[49] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin Kirby John Freymann Keyvan Farahani and ChristosDavatzikos Segmentation labels and radiomic features for the pre-operativescans of the TCGA-LGG collection [data set] The Cancer Imaging Archive2017 URL httpsdoiorg107937K9TCIA2017GJQ7R0EF
[50] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello Mar-tin Rozycki Justin Kirby John Freymann Keyvan Farahani and Chris-tos Davatzikos Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [data set] The CancerImaging Archive 2017 URL httpsdoiorg107937K9TCIA2017
KLXWJJ1Q
[51] Xiangrui Li Paul S Morgan John Ashburner Jolinda Smith and Christo-pher Rorden The first step for neuroimaging data analysis DICOM toNIfTI conversion Journal of Neuroscience Methods 26447ndash56 May 2016ISSN 0165-0270 URL httpsdoiorg101016jjneumeth201603
001
[52] Vladimir Fonov Alan C Evans Kelly Botteron C Robert Almli Robert CMcKinstry and D Louis Collins Unbiased average age-appropriate at-lases for pediatric studies NeuroImage 54(1)313ndash327 January 2011ISSN 1053-8119 URL httpsdoiorg101016jneuroimage2010
07033
[53] VS Fonov AC Evans RC McKinstry CR Almli and DL Collins Unbiasednonlinear average age-appropriate brain templates from birth to adulthoodNeuroImage 47S102 July 2009 ISSN 1053-8119 URL httpsdoi
org101016S1053-8119(09)70884-5 Organization for Human BrainMapping 2009 Annual Meeting
[54] S Klein M Staring K Murphy MA Viergever and J Pluim elastix Atoolbox for intensity-based medical image registration IEEE Transactionson Medical Imaging 29(1)196ndash205 January 2010 ISSN 0278-0062 URLhttpsdoiorg101109TMI20092035616
[55] Denis Shamonin Fast parallel image registration on CPU and GPU fordiagnostic classification of alzheimerrsquos disease Frontiers in Neuroinfor-matics 750 2013 ISSN 1662-5196 URL httpsdoiorg103389
fninf201300050
[56] Bradley C Lowekamp David T Chen Luis Ibanez and Daniel Blezek Thedesign of SimpleITK Frontiers in Neuroinformatics 745 2013 ISSN1662-5196 URL httpsdoiorg103389fninf201300045
47
[57] Fabian Isensee Marianne Schell Irada Pflueger Gianluca Brugnara DavidBonekamp Ulf Neuberger Antje Wick Heinz-Peter Schlemmer SabineHeiland Wolfgang Wick Martin Bendszus Klaus H Maier-Hein andPhilipp Kickingereder Automated brain extraction of multisequence MRIusing artificial neural networks Human Brain Mapping 40(17)4952ndash4964December 2019 ISSN 1065-9471 URL httpsdoiorg101002hbm
24750
[58] Olaf Ronneberger Invited talk U-net convolutional networks for biomed-ical image segmentation In geb Fritzsche K Maier-Hein geb Lehmann TDeserno Heinz Handels and Thomas Tolxdorff editors Bildverarbeitungfur die Medizin 2017 Informatik aktuell pages 3ndash3 Springer Scienceand Business Media LLC 2017 ISBN 9783662543443 URL https
doiorg101007978-3-662-54345-0_3
[59] Martın Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy DavisJeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving MichaelIsard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry MooreDerek G Murray Benoit Steiner Paul Tucker Vijay Vasudevan PeteWarden Martin Wicke Yuan Yu and Xiaoqiang Zheng Tensor-Flow A system for large-scale machine learning In 12th USENIXSymposium on Operating Systems Design and Implementation (OSDI16) pages 265ndash283 USENIX Association November 2016 ISBN978-1-931971-33-1 URL httpswwwusenixorgconferenceosdi16
technical-sessionspresentationabadi
[60] Dipankar Das Naveen Mellempudi Dheevatsa Mudigere Dhiraj KalamkarSasikanth Avancha Kunal Banerjee Srinivas Sridharan KarthikVaidyanathan Bharat Kaul Evangelos Georganas Alexander HeineckePradeep Dubey Jesus Corbal Nikita Shustrov Roma Dubtsov EvaristFomenko and Vadim Pirogov Mixed precision training of convolutionalneural networks using integer operations In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=H135uzZ0-
[61] Samuel L Smith Pieter-Jan Kindermans and Quoc V Le Donrsquot decaythe learning rate increase the batch size In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=B1Yy1BxCZ
[62] Cliff Woolley NCCL accelarated multi-GPU collective communicationsURL httpsimagesnvidiacomeventssc15pdfsNCCL-Woolley
pdf Accessed on 2020-09-30
[63] Ilya Loshchilov and Frank Hutter Decoupled weight decay regularization2017 Preprint at httpsarxivorgabs171105101
48
[64] F Pedregosa G Varoquaux A Gramfort V Michel B Thirion O GriselM Blondel P Prettenhofer R Weiss V Dubourg J Vanderplas A Pas-sos D Cournapeau M Brucher M Perrot and E Duchesnay Scikit-learn Machine learning in python Journal of Machine Learning Re-search 122825ndash2830 2011 URL httpswwwjmlrorgpapersv12
pedregosa11ahtml
[65] Abdel Aziz Taha and Allan Hanbury Metrics for evaluating 3d medicalimage segmentation analysis selection and tool BMC Medical Imaging15(1)29 August 2015 ISSN 1471-2342 URL httpsdoiorg101186
s12880-015-0068-x
[66] Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viegas and MartinWattenberg SmoothGrad removing noise by adding noise 2017 Preprintat httpsarxivorgabs170603825
[67] Alaa Tharwat Classification assessment methods Applied Computing andInformatics 2018 ISSN 2210-8327 URL httpsdoiorg101016j
aci201808003
[68] David J Hand and Robert J Till A simple generalisation of the areaunder the ROC curve for multiple class classification problems MachineLearning 45(2)171ndash186 November 2001 ISSN 1573-0565 URL https
doiorg101023A1010920819831
49
Data Collection | Patient | IDH_mutated | 1p19q_codeleted | Grade | |||||
BTumorP | PGBM-001 | -1 | -1 | -1 | |||||
BTumorP | PGBM-002 | -1 | -1 | -1 | |||||
BTumorP | PGBM-003 | -1 | -1 | -1 | |||||
BTumorP | PGBM-004 | -1 | -1 | -1 | |||||
BTumorP | PGBM-005 | -1 | -1 | -1 | |||||
BTumorP | PGBM-006 | -1 | -1 | -1 | |||||
BTumorP | PGBM-007 | -1 | -1 | -1 | |||||
BTumorP | PGBM-008 | -1 | -1 | -1 | |||||
BTumorP | PGBM-009 | -1 | -1 | -1 | |||||
BTumorP | PGBM-010 | -1 | -1 | -1 | |||||
BTumorP | PGBM-011 | -1 | -1 | -1 | |||||
BTumorP | PGBM-012 | -1 | -1 | -1 | |||||
BTumorP | PGBM-013 | -1 | -1 | -1 | |||||
BTumorP | PGBM-014 | -1 | -1 | -1 | |||||
BTumorP | PGBM-015 | -1 | -1 | -1 | |||||
BTumorP | PGBM-016 | -1 | -1 | -1 | |||||
BTumorP | PGBM-017 | -1 | -1 | -1 | |||||
BTumorP | PGBM-018 | -1 | -1 | -1 | |||||
BTumorP | PGBM-019 | -1 | -1 | -1 | |||||
BTumorP | PGBM-020 | -1 | -1 | -1 | |||||
BraTS | 2013_0 | -1 | -1 | -1 | |||||
BraTS | 2013_10 | -1 | -1 | -1 | |||||
BraTS | 2013_11 | -1 | -1 | -1 | |||||
BraTS | 2013_12 | -1 | -1 | -1 | |||||
BraTS | 2013_13 | -1 | -1 | -1 | |||||
BraTS | 2013_14 | -1 | -1 | -1 | |||||
BraTS | 2013_15 | -1 | -1 | -1 | |||||
BraTS | 2013_16 | -1 | -1 | -1 | |||||
BraTS | 2013_17 | -1 | -1 | -1 | |||||
BraTS | 2013_18 | -1 | -1 | -1 | |||||
BraTS | 2013_19 | -1 | -1 | -1 | |||||
BraTS | 2013_1 | -1 | -1 | -1 | |||||
BraTS | 2013_20 | -1 | -1 | -1 | |||||
BraTS | 2013_21 | -1 | -1 | -1 | |||||
BraTS | 2013_22 | -1 | -1 | -1 | |||||
BraTS | 2013_23 | -1 | -1 | -1 | |||||
BraTS | 2013_24 | -1 | -1 | -1 | |||||
BraTS | 2013_25 | -1 | -1 | -1 | |||||
BraTS | 2013_26 | -1 | -1 | -1 | |||||
BraTS | 2013_27 | -1 | -1 | -1 | |||||
BraTS | 2013_28 | -1 | -1 | -1 | |||||
BraTS | 2013_29 | -1 | -1 | -1 | |||||
BraTS | 2013_2 | -1 | -1 | -1 | |||||
BraTS | 2013_3 | -1 | -1 | -1 | |||||
BraTS | 2013_4 | -1 | -1 | -1 | |||||
BraTS | 2013_5 | -1 | -1 | -1 | |||||
BraTS | 2013_6 | -1 | -1 | -1 | |||||
BraTS | 2013_7 | -1 | -1 | -1 | |||||
BraTS | 2013_8 | -1 | -1 | -1 | |||||
BraTS | 2013_9 | -1 | -1 | -1 | |||||
BraTS | CBICA_AAB | -1 | -1 | -1 | |||||
BraTS | CBICA_AAG | -1 | -1 | -1 | |||||
BraTS | CBICA_AAL | -1 | -1 | -1 | |||||
BraTS | CBICA_AAP | -1 | -1 | -1 | |||||
BraTS | CBICA_ABB | -1 | -1 | -1 | |||||
BraTS | CBICA_ABE | -1 | -1 | -1 | |||||
BraTS | CBICA_ABM | -1 | -1 | -1 | |||||
BraTS | CBICA_ABN | -1 | -1 | -1 | |||||
BraTS | CBICA_ABO | -1 | -1 | -1 | |||||
BraTS | CBICA_ABY | -1 | -1 | -1 | |||||
BraTS | CBICA_ALN | -1 | -1 | -1 | |||||
BraTS | CBICA_ALU | -1 | -1 | -1 | |||||
BraTS | CBICA_ALX | -1 | -1 | -1 | |||||
BraTS | CBICA_AME | -1 | -1 | -1 | |||||
BraTS | CBICA_AMH | -1 | -1 | -1 | |||||
BraTS | CBICA_ANG | -1 | -1 | -1 | |||||
BraTS | CBICA_ANI | -1 | -1 | -1 | |||||
BraTS | CBICA_ANP | -1 | -1 | -1 | |||||
BraTS | CBICA_ANV | -1 | -1 | -1 | |||||
BraTS | CBICA_ANZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AOC | -1 | -1 | -1 | |||||
BraTS | CBICA_AOD | -1 | -1 | -1 | |||||
BraTS | CBICA_AOH | -1 | -1 | -1 | |||||
BraTS | CBICA_AOO | -1 | -1 | -1 | |||||
BraTS | CBICA_AOP | -1 | -1 | -1 | |||||
BraTS | CBICA_AOS | -1 | -1 | -1 | |||||
BraTS | CBICA_AOZ | -1 | -1 | -1 | |||||
BraTS | CBICA_APK | -1 | -1 | -1 | |||||
BraTS | CBICA_APR | -1 | -1 | -1 | |||||
BraTS | CBICA_APY | -1 | -1 | -1 | |||||
BraTS | CBICA_APZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQA | -1 | -1 | -1 | |||||
BraTS | CBICA_AQD | -1 | -1 | -1 | |||||
BraTS | CBICA_AQG | -1 | -1 | -1 | |||||
BraTS | CBICA_AQJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQN | -1 | -1 | -1 | |||||
BraTS | CBICA_AQO | -1 | -1 | -1 | |||||
BraTS | CBICA_AQP | -1 | -1 | -1 | |||||
BraTS | CBICA_AQQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQR | -1 | -1 | -1 | |||||
BraTS | CBICA_AQT | -1 | -1 | -1 | |||||
BraTS | CBICA_AQU | -1 | -1 | -1 | |||||
BraTS | CBICA_AQV | -1 | -1 | -1 | |||||
BraTS | CBICA_AQY | -1 | -1 | -1 | |||||
BraTS | CBICA_AQZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ARF | -1 | -1 | -1 | |||||
BraTS | CBICA_ARW | -1 | -1 | -1 | |||||
BraTS | CBICA_ARZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ASA | -1 | -1 | -1 | |||||
BraTS | CBICA_ASE | -1 | -1 | -1 | |||||
BraTS | CBICA_ASF | -1 | -1 | -1 | |||||
BraTS | CBICA_ASG | -1 | -1 | -1 | |||||
BraTS | CBICA_ASH | -1 | -1 | -1 | |||||
BraTS | CBICA_ASK | -1 | -1 | -1 | |||||
BraTS | CBICA_ASN | -1 | -1 | -1 | |||||
BraTS | CBICA_ASO | -1 | -1 | -1 | |||||
BraTS | CBICA_ASR | -1 | -1 | -1 | |||||
BraTS | CBICA_ASU | -1 | -1 | -1 | |||||
BraTS | CBICA_ASV | -1 | -1 | -1 | |||||
BraTS | CBICA_ASW | -1 | -1 | -1 | |||||
BraTS | CBICA_ASY | -1 | -1 | -1 | |||||
BraTS | CBICA_ATB | -1 | -1 | -1 | |||||
BraTS | CBICA_ATD | -1 | -1 | -1 | |||||
BraTS | CBICA_ATF | -1 | -1 | -1 | |||||
BraTS | CBICA_ATN | -1 | -1 | -1 | |||||
BraTS | CBICA_ATP | -1 | -1 | -1 | |||||
BraTS | CBICA_ATV | -1 | -1 | -1 | |||||
BraTS | CBICA_ATX | -1 | -1 | -1 | |||||
BraTS | CBICA_AUA | -1 | -1 | -1 | |||||
BraTS | CBICA_AUN | -1 | -1 | -1 | |||||
BraTS | CBICA_AUQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AUR | -1 | -1 | -1 | |||||
BraTS | CBICA_AUW | -1 | -1 | -1 | |||||
BraTS | CBICA_AUX | -1 | -1 | -1 | |||||
BraTS | CBICA_AVB | -1 | -1 | -1 | |||||
BraTS | CBICA_AVF | -1 | -1 | -1 | |||||
BraTS | CBICA_AVG | -1 | -1 | -1 | |||||
BraTS | CBICA_AVJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AVT | -1 | -1 | -1 | |||||
BraTS | CBICA_AVV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWG | -1 | -1 | -1 | |||||
BraTS | CBICA_AWH | -1 | -1 | -1 | |||||
BraTS | CBICA_AWI | -1 | -1 | -1 | |||||
BraTS | CBICA_AWV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWX | -1 | -1 | -1 | |||||
BraTS | CBICA_AXJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXL | -1 | -1 | -1 | |||||
BraTS | CBICA_AXM | -1 | -1 | -1 | |||||
BraTS | CBICA_AXN | -1 | -1 | -1 | |||||
BraTS | CBICA_AXO | -1 | -1 | -1 | |||||
BraTS | CBICA_AXQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXW | -1 | -1 | -1 | |||||
BraTS | CBICA_AYA | -1 | -1 | -1 | |||||
BraTS | CBICA_AYC | -1 | -1 | -1 | |||||
BraTS | CBICA_AYG | -1 | -1 | -1 | |||||
BraTS | CBICA_AYI | -1 | -1 | -1 | |||||
BraTS | CBICA_AYU | -1 | -1 | -1 | |||||
BraTS | CBICA_AYW | -1 | -1 | -1 | |||||
BraTS | CBICA_AZD | -1 | -1 | -1 | |||||
BraTS | CBICA_AZH | -1 | -1 | -1 | |||||
BraTS | CBICA_BAN | -1 | -1 | -1 | |||||
BraTS | CBICA_BAP | -1 | -1 | -1 | |||||
BraTS | CBICA_BAX | -1 | -1 | -1 | |||||
BraTS | CBICA_BBG | -1 | -1 | -1 | |||||
BraTS | CBICA_BCF | -1 | -1 | -1 | |||||
BraTS | CBICA_BCL | -1 | -1 | -1 | |||||
BraTS | CBICA_BDK | -1 | -1 | -1 | |||||
BraTS | CBICA_BEM | -1 | -1 | -1 | |||||
BraTS | CBICA_BFB | -1 | -1 | -1 | |||||
BraTS | CBICA_BFP | -1 | -1 | -1 | |||||
BraTS | CBICA_BGE | -1 | -1 | -1 | |||||
BraTS | CBICA_BGG | -1 | -1 | -1 | |||||
BraTS | CBICA_BGN | -1 | -1 | -1 | |||||
BraTS | CBICA_BGO | -1 | -1 | -1 | |||||
BraTS | CBICA_BGR | -1 | -1 | -1 | |||||
BraTS | CBICA_BGT | -1 | -1 | -1 | |||||
BraTS | CBICA_BGW | -1 | -1 | -1 | |||||
BraTS | CBICA_BGX | -1 | -1 | -1 | |||||
BraTS | CBICA_BHB | -1 | -1 | -1 | |||||
BraTS | CBICA_BHK | -1 | -1 | -1 | |||||
BraTS | CBICA_BHM | -1 | -1 | -1 | |||||
BraTS | CBICA_BHQ | -1 | -1 | -1 | |||||
BraTS | CBICA_BHV | -1 | -1 | -1 | |||||
BraTS | CBICA_BHZ | -1 | -1 | -1 | |||||
BraTS | CBICA_BIC | -1 | -1 | -1 | |||||
BraTS | CBICA_BJY | -1 | -1 | -1 | |||||
BraTS | CBICA_BKV | -1 | -1 | -1 | |||||
BraTS | CBICA_BLJ | -1 | -1 | -1 | |||||
BraTS | CBICA_BNR | -1 | -1 | -1 | |||||
BraTS | TMC_6290 | -1 | -1 | -1 | |||||
BraTS | TMC_6643 | -1 | -1 | -1 | |||||
BraTS | TMC_9043 | -1 | -1 | -1 | |||||
BraTS | TMC_11964 | -1 | -1 | -1 | |||||
BraTS | TMC_12866 | -1 | -1 | -1 | |||||
BraTS | TMC_15477 | -1 | -1 | -1 | |||||
BraTS | TMC_21360 | -1 | -1 | -1 | |||||
BraTS | TMC_27374 | -1 | -1 | -1 | |||||
BraTS | TMC_30014 | -1 | -1 | -1 | |||||
CPTAC-GBM | C3L-00016 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00019 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00265 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00278 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00349 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00424 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00429 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00506 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00528 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00591 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00631 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00636 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00671 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00674 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00677 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01045 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01046 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01142 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01156 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01327 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02041 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02465 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02504 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02704 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02706 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02707 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02708 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03260 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03266 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03727 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03728 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03747 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03748 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-04084 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00661 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00662 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00663 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00665 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01192 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01196 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01505 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01849 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01851 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01852 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02255 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02256 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02286 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03001 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03003 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03755 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-04686 | -1 | -1 | 4 | |||||
IvyGAP | W10 | 1 | 1 | 4 | |||||
IvyGAP | W11 | 0 | 0 | 4 | |||||
IvyGAP | W12 | 0 | 0 | 4 | |||||
IvyGAP | W13 | 0 | 0 | 4 | |||||
IvyGAP | W16 | 0 | 0 | 4 | |||||
IvyGAP | W18 | 0 | 0 | 4 | |||||
IvyGAP | W19 | 0 | 0 | 4 | |||||
IvyGAP | W1 | 0 | 0 | 4 | |||||
IvyGAP | W20 | 0 | 0 | 4 | |||||
IvyGAP | W21 | 0 | 0 | 4 | |||||
IvyGAP | W22 | 0 | 0 | -1 | |||||
IvyGAP | W26 | 0 | -1 | 4 | |||||
IvyGAP | W29 | 0 | 0 | 4 | |||||
IvyGAP | W2 | 0 | 1 | 4 | |||||
IvyGAP | W30 | 0 | 0 | 4 | |||||
IvyGAP | W31 | 1 | 1 | 4 | |||||
IvyGAP | W32 | 0 | 0 | 4 | |||||
IvyGAP | W33 | 0 | 0 | 4 | |||||
IvyGAP | W34 | 0 | 0 | 4 | |||||
IvyGAP | W35 | 1 | 0 | 3 | |||||
IvyGAP | W36 | 0 | 0 | 4 | |||||
IvyGAP | W38 | 0 | 0 | 4 | |||||
IvyGAP | W39 | 0 | 0 | 4 | |||||
IvyGAP | W3 | 1 | 0 | 4 | |||||
IvyGAP | W40 | 0 | 0 | 4 | |||||
IvyGAP | W42 | 0 | -1 | 4 | |||||
IvyGAP | W43 | 0 | -1 | 4 | |||||
IvyGAP | W45 | -1 | -1 | 4 | |||||
IvyGAP | W48 | 0 | -1 | 4 | |||||
IvyGAP | W4 | 1 | 0 | 4 | |||||
IvyGAP | W50 | 0 | -1 | 3 | |||||
IvyGAP | W53 | 1 | -1 | 4 | |||||
IvyGAP | W54 | 0 | -1 | 4 | |||||
IvyGAP | W55 | 0 | -1 | 4 | |||||
IvyGAP | W5 | 0 | 0 | 4 | |||||
IvyGAP | W6 | 0 | 0 | 4 | |||||
IvyGAP | W7 | 0 | 0 | 4 | |||||
IvyGAP | W8 | 0 | 0 | 4 | |||||
IvyGAP | W9 | 0 | 0 | 4 | |||||
REMBRANDT | 900-00-5299 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5303 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5308 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5316 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5317 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5332 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5339 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5341 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5342 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5346 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5380 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5381 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5382 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5385 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5396 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5404 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5412 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5414 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5458 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5459 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5462 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5468 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5476 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5477 | -1 | -1 | 2 | |||||
REMBRANDT | HF0763 | -1 | -1 | -1 | |||||
REMBRANDT | HF0828 | -1 | -1 | 3 | |||||
REMBRANDT | HF0835 | -1 | -1 | 2 | |||||
REMBRANDT | HF0855 | -1 | -1 | 2 | |||||
REMBRANDT | HF0868 | -1 | -1 | -1 | |||||
REMBRANDT | HF0883 | -1 | -1 | -1 | |||||
REMBRANDT | HF0899 | -1 | -1 | 2 | |||||
REMBRANDT | HF0920 | -1 | -1 | 2 | |||||
REMBRANDT | HF0931 | -1 | -1 | 2 | |||||
REMBRANDT | HF0953 | -1 | -1 | 2 | |||||
REMBRANDT | HF0960 | -1 | -1 | 2 | |||||
REMBRANDT | HF0966 | -1 | -1 | 3 | |||||
REMBRANDT | HF0986 | -1 | -1 | 4 | |||||
REMBRANDT | HF0990 | -1 | -1 | 4 | |||||
REMBRANDT | HF1000 | -1 | -1 | 2 | |||||
REMBRANDT | HF1058 | -1 | -1 | 4 | |||||
REMBRANDT | HF1059 | -1 | -1 | 3 | |||||
REMBRANDT | HF1071 | -1 | -1 | 4 | |||||
REMBRANDT | HF1077 | -1 | -1 | 4 | |||||
REMBRANDT | HF1078 | -1 | -1 | 4 | |||||
REMBRANDT | HF1097 | -1 | -1 | 4 | |||||
REMBRANDT | HF1113 | -1 | -1 | -1 | |||||
REMBRANDT | HF1122 | -1 | -1 | 4 | |||||
REMBRANDT | HF1136 | -1 | -1 | 3 | |||||
REMBRANDT | HF1139 | -1 | -1 | 4 | |||||
REMBRANDT | HF1150 | -1 | -1 | 3 | |||||
REMBRANDT | HF1156 | -1 | -1 | 2 | |||||
REMBRANDT | HF1167 | -1 | -1 | 2 | |||||
REMBRANDT | HF1185 | -1 | -1 | 3 | |||||
REMBRANDT | HF1191 | -1 | -1 | 4 | |||||
REMBRANDT | HF1199 | -1 | -1 | -1 | |||||
REMBRANDT | HF1219 | -1 | -1 | 3 | |||||
REMBRANDT | HF1227 | -1 | -1 | 2 | |||||
REMBRANDT | HF1232 | -1 | -1 | 3 | |||||
REMBRANDT | HF1235 | -1 | -1 | 2 | |||||
REMBRANDT | HF1242 | -1 | -1 | 3 | |||||
REMBRANDT | HF1246 | -1 | -1 | 2 | |||||
REMBRANDT | HF1264 | -1 | -1 | 2 | |||||
REMBRANDT | HF1269 | -1 | -1 | 4 | |||||
REMBRANDT | HF1280 | -1 | -1 | 3 | |||||
REMBRANDT | HF1292 | -1 | -1 | 4 | |||||
REMBRANDT | HF1293 | -1 | -1 | -1 | |||||
REMBRANDT | HF1297 | -1 | -1 | 4 | |||||
REMBRANDT | HF1300 | -1 | -1 | -1 | |||||
REMBRANDT | HF1307 | -1 | -1 | -1 | |||||
REMBRANDT | HF1316 | -1 | -1 | 2 | |||||
REMBRANDT | HF1318 | -1 | -1 | -1 | |||||
REMBRANDT | HF1325 | -1 | -1 | 2 | |||||
REMBRANDT | HF1331 | -1 | -1 | -1 | |||||
REMBRANDT | HF1334 | -1 | -1 | 2 | |||||
REMBRANDT | HF1344 | -1 | -1 | 2 | |||||
REMBRANDT | HF1345 | -1 | -1 | 2 | |||||
REMBRANDT | HF1357 | -1 | -1 | 3 | |||||
REMBRANDT | HF1381 | -1 | -1 | 2 | |||||
REMBRANDT | HF1397 | -1 | -1 | 4 | |||||
REMBRANDT | HF1398 | -1 | -1 | 3 | |||||
REMBRANDT | HF1407 | -1 | -1 | 2 | |||||
REMBRANDT | HF1409 | -1 | -1 | 3 | |||||
REMBRANDT | HF1420 | -1 | -1 | -1 | |||||
REMBRANDT | HF1429 | -1 | -1 | -1 | |||||
REMBRANDT | HF1433 | -1 | -1 | 2 | |||||
REMBRANDT | HF1437 | -1 | -1 | -1 | |||||
REMBRANDT | HF1442 | -1 | -1 | 2 | |||||
REMBRANDT | HF1458 | -1 | -1 | 3 | |||||
REMBRANDT | HF1463 | -1 | -1 | 2 | |||||
REMBRANDT | HF1489 | -1 | -1 | 2 | |||||
REMBRANDT | HF1490 | -1 | -1 | 3 | |||||
REMBRANDT | HF1493 | -1 | -1 | -1 | |||||
REMBRANDT | HF1510 | -1 | -1 | -1 | |||||
REMBRANDT | HF1511 | -1 | -1 | 2 | |||||
REMBRANDT | HF1517 | -1 | -1 | 4 | |||||
REMBRANDT | HF1538 | -1 | -1 | 4 | |||||
REMBRANDT | HF1551 | -1 | -1 | 2 | |||||
REMBRANDT | HF1553 | -1 | -1 | 2 | |||||
REMBRANDT | HF1560 | -1 | -1 | 4 | |||||
REMBRANDT | HF1568 | -1 | -1 | 2 | |||||
REMBRANDT | HF1587 | -1 | -1 | 3 | |||||
REMBRANDT | HF1588 | -1 | -1 | 2 | |||||
REMBRANDT | HF1606 | -1 | -1 | 2 | |||||
REMBRANDT | HF1613 | -1 | -1 | 3 | |||||
REMBRANDT | HF1628 | -1 | -1 | 4 | |||||
REMBRANDT | HF1652 | -1 | -1 | -1 | |||||
REMBRANDT | HF1677 | -1 | -1 | 2 | |||||
REMBRANDT | HF1702 | -1 | -1 | 3 | |||||
REMBRANDT | HF1708 | -1 | -1 | 2 | |||||
TCGA-GBM | TCGA-02-0003 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0006 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0009 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0011 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0027 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0033 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0034 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0037 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0046 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0047 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0048 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0054 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0059 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0060 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0064 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0068 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0069 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0070 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0075 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0085 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0086 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0087 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0102 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0106 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0116 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0119 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0122 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0128 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0130 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0132 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0133 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0137 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0138 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0139 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0142 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0145 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0149 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0154 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0158 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0162 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0164 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0166 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0168 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0175 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0176 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0177 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0179 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0182 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0184 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0185 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0187 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0188 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0189 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0190 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0192 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0213 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0238 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0240 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0241 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0644 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0646 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0648 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0649 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1084 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1802 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-2570 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5408 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5412 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5413 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5417 | 1 | -1 | 4 | |||||
TCGA-GBM | TCGA-06-6389 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0350 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0352 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0353 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0354 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0355 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0356 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0357 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0358 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0359 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0360 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0385 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0389 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0392 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0512 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0520 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0521 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0522 | -1 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0524 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0529 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0616 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0776 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1093 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1094 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1098 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1598 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1601 | 0 | -1 | -1 | |||||
TCGA-GBM | TCGA-12-1602 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-3650 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-0789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1456 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1794 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1825 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-3477 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-0963 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2624 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2631 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5951 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5954 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5958 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5960 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1834 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1838 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-2526 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4932 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-76-4934 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4935 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6191 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6193 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6280 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6282 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6285 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6656 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6657 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6661 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6662 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6663 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6664 | 0 | 0 | 4 | |||||
TCGA-LGG | TCGA-CS-4941 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4942 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4944 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5393 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-5395 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5396 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-CS-5397 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6186 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6188 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6290 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6665 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6666 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6667 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-6668 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-CS-6669 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5849 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-5851 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5852 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5854 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5871 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5874 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6397 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6399 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6400 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6404 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6405 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6407 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6410 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6542 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7008 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7010 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7014 | -1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7015 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7018 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7019 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7294 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-7298 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7300 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7302 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7309 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8162 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8164 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-8165 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8167 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8168 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-EZ-7265A | -1 | -1 | -1 | |||||
TCGA-LGG | TCGA-FG-5964 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-6688 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6691 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6692 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-7643 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-A713 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7473 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7616 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7680 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7684 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7686 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7692 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7693 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7694 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7856 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7860 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7874 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7879 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7882 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8105 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-8106 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8107 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8111 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8114 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8563 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A614 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0 | 2 |
Data_collection | Patient | IDH_mutated | Prediction_score_IDH_wildtype | Prediction_score_IDH_mutated | 1p19q_codeleted | Prediction_score_1p19q_codeleted | Prediction_score_1p19q_intact | Grade | Prediction_score_grade_2 | Prediction_score_grade_3 | Prediction_score_grade_4 | ||||||||||||
TCGA-GBM | TCGA-02-0003 | 0 | 099998915 | 10867886E-05 | 0 | 099996686 | 3308471E-05 | 4 | 7377526E-05 | 000074111245 | 099918514 | ||||||||||||
TCGA-GBM | TCGA-02-0006 | 0 | 042321962 | 05767803 | 0 | 068791837 | 031208166 | 4 | 060229343 | 026596427 | 013174225 | ||||||||||||
TCGA-GBM | TCGA-02-0009 | 0 | 099306935 | 0006930672 | 0 | 09906961 | 0009303949 | 4 | 0056565534 | 010282235 | 08406121 | ||||||||||||
TCGA-GBM | TCGA-02-0011 | 0 | 013531776 | 08646823 | 0 | 085318035 | 01468197 | 4 | 0015055533 | 092510724 | 005983725 | ||||||||||||
TCGA-GBM | TCGA-02-0027 | 0 | 09997279 | 000027212297 | 0 | 09986827 | 00013172914 | 4 | 00016104137 | 00038575265 | 0994532 | ||||||||||||
TCGA-GBM | TCGA-02-0033 | 0 | 099974436 | 000025564007 | 0 | 099940693 | 0000593021 | 4 | 00020670628 | 0003761288 | 09941717 | ||||||||||||
TCGA-GBM | TCGA-02-0034 | 0 | 091404164 | 008595832 | 0 | 089209336 | 01079066 | 4 | 00116944825 | 0061110377 | 092719513 | ||||||||||||
TCGA-GBM | TCGA-02-0037 | 0 | 09999577 | 42315594E-05 | 0 | 099992716 | 72827526E-05 | 4 | 82080274E-05 | 0009249337 | 09906686 | ||||||||||||
TCGA-GBM | TCGA-02-0046 | 0 | 0999129 | 00008710656 | 0 | 09989637 | 00010362669 | 4 | 0004290756 | 0022799779 | 097290945 | ||||||||||||
TCGA-GBM | TCGA-02-0047 | 0 | 099991703 | 83008505E-05 | 0 | 09999292 | 70863265E-05 | 4 | 000016252015 | 0040118434 | 095971906 | ||||||||||||
TCGA-GBM | TCGA-02-0048 | 0 | 09998785 | 000012148175 | 0 | 099959475 | 000040527192 | 4 | 00002215901 | 000039696065 | 09993814 | ||||||||||||
TCGA-GBM | TCGA-02-0054 | 0 | 09999831 | 1689829E-05 | 0 | 09999442 | 5583975E-05 | 4 | 00010063206 | 0060579527 | 093841416 | ||||||||||||
TCGA-GBM | TCGA-02-0059 | -1 | 09993749 | 000062511285 | 0 | 09996424 | 00003576683 | 4 | 00007046657 | 0010920537 | 09883748 | ||||||||||||
TCGA-GBM | TCGA-02-0060 | 0 | 07197039 | 028029615 | 0 | 09016612 | 009833879 | 4 | 017739706 | 03728545 | 04497484 | ||||||||||||
TCGA-GBM | TCGA-02-0064 | 0 | 09999083 | 9170197E-05 | 0 | 09995073 | 000049264234 | 4 | 000043781495 | 00028024286 | 099675983 | ||||||||||||
TCGA-GBM | TCGA-02-0068 | 0 | 099187535 | 0008124709 | 0 | 099528164 | 00047183693 | 4 | 00030539853 | 059695286 | 039999318 | ||||||||||||
TCGA-GBM | TCGA-02-0069 | 0 | 09890871 | 0010912909 | 0 | 099704784 | 0002952148 | 4 | 00057067247 | 0061368063 | 09329252 | ||||||||||||
TCGA-GBM | TCGA-02-0070 | 0 | 09940659 | 00059340666 | 0 | 0957794 | 0042206 | 4 | 0008216515 | 003556913 | 09562143 | ||||||||||||
TCGA-GBM | TCGA-02-0075 | 0 | 099933076 | 00006693099 | 0 | 099735296 | 00026470982 | 4 | 000044697264 | 00035736929 | 09959793 | ||||||||||||
TCGA-GBM | TCGA-02-0085 | 0 | 099114406 | 0008855922 | 0 | 09756698 | 002433019 | 4 | 00065203947 | 0035171553 | 095830804 | ||||||||||||
TCGA-GBM | TCGA-02-0086 | 0 | 099965334 | 000034666777 | 0 | 0998698 | 00013019645 | 4 | 000032699382 | 00018025768 | 099787045 | ||||||||||||
TCGA-GBM | TCGA-02-0087 | -1 | 09974885 | 00025114634 | 0 | 09990638 | 000093628286 | 4 | 0007505083 | 0008562708 | 098393226 | ||||||||||||
TCGA-GBM | TCGA-02-0102 | 0 | 09797647 | 0020235319 | 0 | 098292196 | 0017078074 | 4 | 003512482 | 03901857 | 05746895 | ||||||||||||
TCGA-GBM | TCGA-02-0106 | -1 | 099993694 | 6302759E-05 | 0 | 099980897 | 000019110431 | 4 | 60797247E-05 | 00008735659 | 09990657 | ||||||||||||
TCGA-GBM | TCGA-02-0116 | 0 | 09999778 | 22125667E-05 | 0 | 09996886 | 00003113695 | 4 | 000015498884 | 000051770627 | 09993273 | ||||||||||||
TCGA-GBM | TCGA-06-0119 | 0 | 09999362 | 63770494E-05 | 0 | 09999355 | 6452215E-05 | 4 | 000013225728 | 00028902534 | 099697745 | ||||||||||||
TCGA-GBM | TCGA-06-0122 | 0 | 09915298 | 0008470196 | 0 | 09859093 | 00140907345 | 4 | 00121390615 | 027333176 | 071452916 | ||||||||||||
TCGA-GBM | TCGA-06-0128 | 1 | 099988174 | 000011820537 | 0 | 099980634 | 000019373452 | 4 | 000016409029 | 0007865882 | 099197 | ||||||||||||
TCGA-GBM | TCGA-06-0130 | 0 | 099998784 | 12123987E-05 | 0 | 09999323 | 6775062E-05 | 4 | 80872844E-05 | 00026260202 | 099729306 | ||||||||||||
TCGA-GBM | TCGA-06-0132 | 0 | 09998566 | 000014341719 | 0 | 099988496 | 000011501736 | 4 | 000072843547 | 0005115947 | 099415565 | ||||||||||||
TCGA-GBM | TCGA-06-0133 | 0 | 097782 | 002218004 | 0 | 0993807 | 00061929906 | 4 | 0026753133 | 004659919 | 09266477 | ||||||||||||
TCGA-GBM | TCGA-06-0137 | 0 | 096448904 | 003551094 | 0 | 099403125 | 0005968731 | 4 | 000649511 | 038909483 | 060441005 | ||||||||||||
TCGA-GBM | TCGA-06-0138 | 0 | 09977743 | 00022256707 | 0 | 099736834 | 0002631674 | 4 | 00032954598 | 0011606657 | 09850979 | ||||||||||||
TCGA-GBM | TCGA-06-0139 | 0 | 09992649 | 00007350447 | 0 | 099898964 | 00010103129 | 4 | 00021781863 | 00069256434 | 099089617 | ||||||||||||
TCGA-GBM | TCGA-06-0142 | 0 | 099909425 | 00009057334 | 0 | 09985896 | 00014103584 | 4 | 0002598974 | 0046451908 | 09509491 | ||||||||||||
TCGA-GBM | TCGA-06-0145 | 0 | 099964654 | 000035350278 | 0 | 0999652 | 000034802416 | 4 | 00009068022 | 0021991275 | 0977102 | ||||||||||||
TCGA-GBM | TCGA-06-0149 | -1 | 09992161 | 00007839425 | 0 | 09981067 | 00018932257 | 4 | 00057726577 | 0013888515 | 09803388 | ||||||||||||
TCGA-GBM | TCGA-06-0154 | 0 | 099968064 | 000031937403 | 0 | 0999729 | 000027106237 | 4 | 000041507537 | 023430935 | 07652756 | ||||||||||||
TCGA-GBM | TCGA-06-0158 | 0 | 09999199 | 8014118E-05 | 0 | 099992514 | 74846226E-05 | 4 | 00026547876 | 020762624 | 0789719 | ||||||||||||
TCGA-GBM | TCGA-06-0162 | -1 | 099964297 | 00003569706 | 0 | 09997459 | 0000254147 | 4 | 000033955855 | 004318936 | 09564711 | ||||||||||||
TCGA-GBM | TCGA-06-0164 | -1 | 09983991 | 00016009645 | 0 | 09873262 | 0012673735 | 4 | 00016517473 | 00048346478 | 09935136 | ||||||||||||
TCGA-GBM | TCGA-06-0166 | 0 | 099991715 | 82846556E-05 | 0 | 0999554 | 00004459562 | 4 | 000013499439 | 0011635037 | 098823 | ||||||||||||
TCGA-GBM | TCGA-06-0168 | 0 | 09975561 | 00024438864 | 0 | 09964825 | 00035174883 | 4 | 0004766434 | 010448053 | 089075303 | ||||||||||||
TCGA-GBM | TCGA-06-0175 | -1 | 09996252 | 000037482675 | 0 | 09988098 | 00011902251 | 4 | 00026097735 | 004992068 | 094746953 | ||||||||||||
TCGA-GBM | TCGA-06-0176 | 0 | 099550986 | 00044901576 | 0 | 09998872 | 000011279297 | 4 | 0032868527 | 036690876 | 06002227 | ||||||||||||
TCGA-GBM | TCGA-06-0177 | -1 | 081774735 | 018225263 | 0 | 09946464 | 00053536464 | 4 | 0026683953 | 013013016 | 08431859 | ||||||||||||
TCGA-GBM | TCGA-06-0179 | -1 | 09997508 | 000024923254 | 0 | 09989778 | 00010222099 | 4 | 0002628482 | 0004127114 | 099324447 | ||||||||||||
TCGA-GBM | TCGA-06-0182 | -1 | 099999547 | 45838406E-06 | 0 | 099998736 | 12656287E-05 | 4 | 00002591103 | 000018499703 | 09995559 | ||||||||||||
TCGA-GBM | TCGA-06-0184 | 0 | 09935369 | 00064631375 | 0 | 099458355 | 00054164114 | 4 | 0023110552 | 0017436244 | 09594532 | ||||||||||||
TCGA-GBM | TCGA-06-0185 | 0 | 09999337 | 66310655E-05 | 0 | 099986255 | 000013738607 | 4 | 7657532E-05 | 0016089642 | 098383385 | ||||||||||||
TCGA-GBM | TCGA-06-0187 | 0 | 09991689 | 00008312097 | 0 | 099700147 | 00029984985 | 4 | 00020616595 | 0033111423 | 096482694 | ||||||||||||
TCGA-GBM | TCGA-06-0188 | 0 | 09883802 | 0011619771 | 0 | 09826743 | 0017325714 | 4 | 0013776424 | 0112841725 | 087338185 | ||||||||||||
TCGA-GBM | TCGA-06-0189 | 0 | 099906737 | 0000932636 | 0 | 09983865 | 00016135005 | 4 | 00022760795 | 00106745735 | 09870494 | ||||||||||||
TCGA-GBM | TCGA-06-0190 | 0 | 099954176 | 000045831292 | 0 | 09967013 | 00032986512 | 4 | 000040555766 | 0001246768 | 099834764 | ||||||||||||
TCGA-GBM | TCGA-06-0192 | 0 | 09997876 | 00002123566 | 0 | 09992735 | 00007264875 | 4 | 00004505576 | 00014473333 | 09981021 | ||||||||||||
TCGA-GBM | TCGA-06-0213 | 0 | 099986935 | 00001305845 | 0 | 099971646 | 000028351307 | 4 | 8755587E-05 | 00013480412 | 09985644 | ||||||||||||
TCGA-GBM | TCGA-06-0238 | 0 | 09999982 | 17603431E-06 | 0 | 09999894 | 10616134E-05 | 4 | 8076515E-05 | 56053756E-05 | 099986315 | ||||||||||||
TCGA-GBM | TCGA-06-0240 | 0 | 09989956 | 00010044163 | 0 | 099948466 | 00005152657 | 4 | 00016040986 | 021931975 | 077907616 | ||||||||||||
TCGA-GBM | TCGA-06-0241 | 0 | 099959785 | 000040211933 | 0 | 099910825 | 00008917038 | 4 | 00023411359 | 0007850656 | 098980826 | ||||||||||||
TCGA-GBM | TCGA-06-0644 | 0 | 09871044 | 0012895588 | 0 | 09859228 | 00140771745 | 4 | 0013671214 | 009819665 | 088813215 | ||||||||||||
TCGA-GBM | TCGA-06-0646 | 0 | 099959 | 00004100472 | 0 | 099936503 | 000063495064 | 4 | 00019223108 | 0040443853 | 095763385 | ||||||||||||
TCGA-GBM | TCGA-06-0648 | 0 | 09999709 | 29083441E-05 | 0 | 099982435 | 000017571273 | 4 | 000077678583 | 000038868992 | 099883455 | ||||||||||||
TCGA-GBM | TCGA-06-0649 | 0 | 09997805 | 000021952427 | 0 | 099951684 | 000048311835 | 4 | 0042641632 | 00058432207 | 095151514 | ||||||||||||
TCGA-GBM | TCGA-06-1084 | 0 | 099985826 | 000014174655 | 0 | 099968565 | 00003144242 | 4 | 00002676724 | 020492287 | 079480946 | ||||||||||||
TCGA-GBM | TCGA-06-1802 | -1 | 09991928 | 00008072305 | 0 | 09956176 | 0004382337 | 4 | 000043478087 | 00019495043 | 09976157 | ||||||||||||
TCGA-GBM | TCGA-06-2570 | 1 | 096841115 | 0031588882 | 0 | 09842457 | 0015754245 | 4 | 0015369608 | 0030956635 | 09536738 | ||||||||||||
TCGA-GBM | TCGA-06-5408 | 0 | 099857306 | 00014269598 | 0 | 09962638 | 00037362208 | 4 | 00027690146 | 0016195394 | 098103565 | ||||||||||||
TCGA-GBM | TCGA-06-5412 | 0 | 099366105 | 0006338921 | 0 | 099193794 | 0008061992 | 4 | 0011476759 | 006606435 | 09224589 | ||||||||||||
TCGA-GBM | TCGA-06-5413 | 0 | 09994105 | 000058955856 | 0 | 09983026 | 00016974095 | 4 | 00027100197 | 0021083053 | 097620696 | ||||||||||||
TCGA-GBM | TCGA-06-5417 | 1 | 01521267 | 08478733 | -1 | 03064492 | 06935508 | 4 | 013736826 | 037757674 | 048505494 | ||||||||||||
TCGA-GBM | TCGA-06-6389 | 1 | 099987435 | 000012558252 | 0 | 09997017 | 000029827762 | 4 | 00014020519 | 00020044278 | 099659353 | ||||||||||||
TCGA-GBM | TCGA-08-0350 | 0 | 019229275 | 08077072 | 0 | 0033211168 | 09667888 | 4 | 0051619414 | 022280572 | 072557485 | ||||||||||||
TCGA-GBM | TCGA-08-0352 | 0 | 099997497 | 25071595E-05 | 0 | 099992514 | 74846226E-05 | 4 | 000024192198 | 000048111935 | 099927694 | ||||||||||||
TCGA-GBM | TCGA-08-0353 | 0 | 09901496 | 0009850325 | 0 | 09967775 | 0003222484 | 4 | 00053748637 | 0004291497 | 09903336 | ||||||||||||
TCGA-GBM | TCGA-08-0354 | 0 | 076413894 | 023586108 | 0 | 07554566 | 024454337 | 4 | 008784444 | 02004897 | 071166587 | ||||||||||||
TCGA-GBM | TCGA-08-0355 | 0 | 09998349 | 000016506859 | 0 | 099984336 | 000015659066 | 4 | 000076689845 | 0023648744 | 09755844 | ||||||||||||
TCGA-GBM | TCGA-08-0356 | 0 | 097673583 | 0023264103 | 0 | 097773504 | 0022264915 | 4 | 001175834 | 0031075679 | 095716596 | ||||||||||||
TCGA-GBM | TCGA-08-0357 | 0 | 099509466 | 0004905406 | 0 | 099300176 | 00069982093 | 4 | 0005191745 | 0038681854 | 095612645 | ||||||||||||
TCGA-GBM | TCGA-08-0358 | 0 | 099999785 | 2199356E-06 | 0 | 099999034 | 9628425E-06 | 4 | 6113315E-06 | 00011283219 | 09988656 | ||||||||||||
TCGA-GBM | TCGA-08-0359 | 0 | 097885466 | 0021145396 | 0 | 09956006 | 00043994132 | 4 | 0009885523 | 0066605434 | 092350906 | ||||||||||||
TCGA-GBM | TCGA-08-0360 | 0 | 09922444 | 00077555366 | -1 | 09948704 | 00051296344 | 4 | 0013318472 | 003317344 | 095350814 | ||||||||||||
TCGA-GBM | TCGA-08-0385 | 0 | 099605453 | 00039454065 | -1 | 099686414 | 0003135836 | 4 | 00050293226 | 0029977333 | 096499336 | ||||||||||||
TCGA-GBM | TCGA-08-0389 | 0 | 099964714 | 000035281325 | 0 | 09991272 | 000087276706 | 4 | 00017554013 | 00024730961 | 099577147 | ||||||||||||
TCGA-GBM | TCGA-08-0390 | 0 | 099945146 | 000054847915 | 0 | 099936 | 00006399274 | 4 | 00036811908 | 00050958768 | 0991223 | ||||||||||||
TCGA-GBM | TCGA-08-0392 | 0 | 099962366 | 000037629317 | 0 | 09993575 | 00006424303 | 4 | 000036593352 | 0010291994 | 09893421 | ||||||||||||
TCGA-GBM | TCGA-08-0512 | -1 | 09982893 | 00017106998 | 0 | 099193794 | 0008061992 | 4 | 00016200381 | 00027773918 | 09956026 | ||||||||||||
TCGA-GBM | TCGA-08-0520 | -1 | 099603915 | 00039607873 | 0 | 09981933 | 00018066854 | 4 | 00007140295 | 0019064669 | 09802213 | ||||||||||||
TCGA-GBM | TCGA-08-0521 | -1 | 09975274 | 0002472623 | 0 | 099490017 | 00050998176 | 4 | 0001514669 | 0020103427 | 09783819 | ||||||||||||
TCGA-GBM | TCGA-08-0522 | -1 | 099960107 | 000039899128 | -1 | 09992053 | 00007947255 | 4 | 0000269389 | 0006173321 | 09935573 | ||||||||||||
TCGA-GBM | TCGA-08-0524 | -1 | 09964619 | 0003538086 | 0 | 099620515 | 0003794834 | 4 | 000019140428 | 0010096702 | 09897119 | ||||||||||||
TCGA-GBM | TCGA-08-0529 | -1 | 09996567 | 000034329997 | 0 | 099952066 | 00004793605 | 4 | 000032077235 | 0035970636 | 09637086 | ||||||||||||
TCGA-GBM | TCGA-12-0616 | 0 | 098521465 | 0014785408 | 0 | 098704207 | 001295789 | 4 | 001592791 | 012875569 | 08553164 | ||||||||||||
TCGA-GBM | TCGA-12-0776 | -1 | 099899167 | 00010083434 | 0 | 09987031 | 00012968953 | 4 | 0019219175 | 00637484 | 09170324 | ||||||||||||
TCGA-GBM | TCGA-12-0829 | 0 | 099913067 | 00008693674 | 0 | 099821776 | 00017821962 | 4 | 00021031094 | 0055067167 | 09428297 | ||||||||||||
TCGA-GBM | TCGA-12-1093 | 0 | 099992585 | 7411892E-05 | 0 | 09999448 | 5518923E-05 | 4 | 000046803855 | 0012115157 | 098741674 | ||||||||||||
TCGA-GBM | TCGA-12-1094 | -1 | 09980045 | 00019955388 | 0 | 09866105 | 0013389497 | 4 | 00053194338 | 001599471 | 097868586 | ||||||||||||
TCGA-GBM | TCGA-12-1098 | -1 | 09998406 | 000015936712 | 0 | 09977216 | 00022783307 | 4 | 000010218692 | 0035607774 | 09642901 | ||||||||||||
TCGA-GBM | TCGA-12-1598 | 0 | 096309197 | 0036908068 | 0 | 097933435 | 0020665688 | 4 | 0012952217 | 052912676 | 045792103 | ||||||||||||
TCGA-GBM | TCGA-12-1601 | 0 | 09875683 | 0012431651 | -1 | 0991891 | 0008108984 | -1 | 00118053425 | 0105477065 | 088271755 | ||||||||||||
TCGA-GBM | TCGA-12-1602 | 0 | 099830914 | 00016908031 | 0 | 099858415 | 00014158705 | 4 | 0008427611 | 0025996923 | 09655755 | ||||||||||||
TCGA-GBM | TCGA-12-3650 | 0 | 09761519 | 0023848088 | 0 | 097467697 | 0025323058 | 4 | 0010450666 | 043705726 | 05524921 | ||||||||||||
TCGA-GBM | TCGA-14-0789 | 0 | 099856466 | 00014353332 | 0 | 099666256 | 00033374047 | 4 | 0001406897 | 0008273975 | 099031913 | ||||||||||||
TCGA-GBM | TCGA-14-1456 | 1 | 006299064 | 093700933 | 0 | 08656222 | 013437784 | 4 | 016490369 | 047177824 | 036331803 | ||||||||||||
TCGA-GBM | TCGA-14-1794 | 0 | 08579393 | 014206071 | 0 | 09850429 | 0014957087 | 4 | 0023009384 | 009868736 | 08783033 | ||||||||||||
TCGA-GBM | TCGA-14-1825 | 0 | 099960107 | 000039899128 | 0 | 099968123 | 00003187511 | 4 | 0008552247 | 0010156045 | 09812918 | ||||||||||||
TCGA-GBM | TCGA-14-1829 | 0 | 090690076 | 009309922 | 0 | 09907856 | 0009214366 | 4 | 0008461936 | 0102735735 | 088880235 | ||||||||||||
TCGA-GBM | TCGA-14-3477 | 0 | 099796116 | 0002038787 | 0 | 09990728 | 00009271923 | 4 | 00032272525 | 0021644868 | 09751279 | ||||||||||||
TCGA-GBM | TCGA-19-0963 | -1 | 099876726 | 00012327607 | 0 | 09983612 | 00016388679 | 4 | 00031698826 | 013153598 | 086529416 | ||||||||||||
TCGA-GBM | TCGA-19-1390 | 0 | 099913234 | 00008676725 | 0 | 099703634 | 00029636684 | 4 | 00015592943 | 0026028048 | 097241265 | ||||||||||||
TCGA-GBM | TCGA-19-1789 | 0 | 09809491 | 00190509 | 0 | 09915216 | 0008478402 | 4 | 0038703684 | 014341596 | 08178804 | ||||||||||||
TCGA-GBM | TCGA-19-2624 | 0 | 07535573 | 024644265 | 0 | 09816127 | 0018387254 | 4 | 012311598 | 012769651 | 07491875 | ||||||||||||
TCGA-GBM | TCGA-19-2631 | 0 | 099860877 | 0001391234 | 0 | 09981178 | 00018821858 | 4 | 00009839778 | 001843531 | 09805807 | ||||||||||||
TCGA-GBM | TCGA-19-5951 | 0 | 09999031 | 9685608E-05 | 0 | 099977034 | 000022960825 | 4 | 00020246736 | 0004014765 | 09939606 | ||||||||||||
TCGA-GBM | TCGA-19-5954 | 0 | 099456257 | 00054374957 | 0 | 09968273 | 00031726828 | 4 | 00073725334 | 006310084 | 09295266 | ||||||||||||
TCGA-GBM | TCGA-19-5958 | 0 | 099999475 | 5234907E-06 | 0 | 0999941 | 58978338E-05 | 4 | 35422294E-05 | 86819025E-05 | 09998777 | ||||||||||||
TCGA-GBM | TCGA-19-5960 | 0 | 09683962 | 003160382 | 0 | 09013577 | 009864227 | 4 | 0011394806 | 018114014 | 08074651 | ||||||||||||
TCGA-GBM | TCGA-27-1834 | 0 | 099998164 | 18342893E-05 | 0 | 09999685 | 31446623E-05 | 4 | 8611921E-05 | 000031686216 | 0999597 | ||||||||||||
TCGA-GBM | TCGA-27-1838 | 0 | 09993625 | 000063743413 | 0 | 09940428 | 0005957154 | 4 | 00006736379 | 0007191195 | 099213517 | ||||||||||||
TCGA-GBM | TCGA-27-2526 | 0 | 099983776 | 000016219281 | 0 | 09996898 | 000031015594 | 4 | 000016658282 | 00006323714 | 09992011 | ||||||||||||
TCGA-GBM | TCGA-76-4932 | 0 | 09867389 | 0013261103 | -1 | 09949397 | 0005060332 | 4 | 00007321126 | 0003016794 | 099625117 | ||||||||||||
TCGA-GBM | TCGA-76-4934 | 0 | 099318933 | 0006810731 | 0 | 09995073 | 000049264234 | 4 | 00061555947 | 00070025027 | 098684186 | ||||||||||||
TCGA-GBM | TCGA-76-4935 | 0 | 074562997 | 025437003 | 0 | 098242307 | 001757688 | 4 | 076535034 | 006437644 | 017027317 | ||||||||||||
TCGA-GBM | TCGA-76-6191 | 0 | 09981067 | 00018932257 | 0 | 09970879 | 00029121784 | 4 | 00044340584 | 00096095055 | 098595643 | ||||||||||||
TCGA-GBM | TCGA-76-6193 | 0 | 09966168 | 0003383191 | 0 | 099850464 | 00014953383 | 4 | 00037061477 | 007873953 | 09175543 | ||||||||||||
TCGA-GBM | TCGA-76-6280 | 0 | 099948776 | 00005122569 | 0 | 099908185 | 000091819017 | 4 | 00001475792 | 000846075 | 099139166 | ||||||||||||
TCGA-GBM | TCGA-76-6282 | 0 | 0995906 | 0004093958 | 0 | 099861956 | 00013804223 | 4 | 00006694951 | 0009437619 | 09898929 | ||||||||||||
TCGA-GBM | TCGA-76-6285 | 0 | 099949074 | 00005092657 | 0 | 09971661 | 00028338495 | 4 | 00031175872 | 004005614 | 095682627 | ||||||||||||
TCGA-GBM | TCGA-76-6656 | 0 | 09996917 | 000030834455 | 0 | 09983897 | 00016103574 | 4 | 002648366 | 00017969633 | 09717193 | ||||||||||||
TCGA-GBM | TCGA-76-6657 | 0 | 099987245 | 000012755992 | 0 | 099951494 | 00004850083 | 4 | 000096620515 | 0005599633 | 09934342 | ||||||||||||
TCGA-GBM | TCGA-76-6661 | 0 | 093211424 | 006788577 | 0 | 09640178 | 0035982177 | 4 | 003490037 | 0026863772 | 09382358 | ||||||||||||
TCGA-GBM | TCGA-76-6662 | 0 | 096425414 | 0035745807 | 0 | 09963924 | 0003607617 | 4 | 002845819 | 002544755 | 09460942 | ||||||||||||
TCGA-GBM | TCGA-76-6663 | 0 | 088664144 | 0113358565 | 0 | 09984207 | 00015792594 | 4 | 0010206689 | 043740335 | 05523899 | ||||||||||||
TCGA-GBM | TCGA-76-6664 | 0 | 011047115 | 08895289 | 0 | 09559813 | 004401865 | 4 | 00049677677 | 08806894 | 011434281 | ||||||||||||
TCGA-LGG | TCGA-CS-4941 | 0 | 088931274 | 011068726 | 0 | 087037706 | 012962292 | 3 | 002865127 | 0048591908 | 092275685 | ||||||||||||
TCGA-LGG | TCGA-CS-4942 | 1 | 00031327847 | 099686724 | 0 | 096309197 | 0036908068 | 3 | 096261597 | 00148612335 | 0022522787 | ||||||||||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0005265965 | 099473405 | 0 | 09940544 | 00059455987 | 3 | 09439103 | 0023049146 | 003304057 | ||||||||||||
TCGA-LGG | TCGA-CS-4944 | 1 | 009363656 | 09063635 | 0 | 08755211 | 0124478824 | 2 | 034047556 | 033881712 | 03207073 | ||||||||||||
TCGA-LGG | TCGA-CS-5393 | 1 | 009623762 | 09037624 | 0 | 098178816 | 001821182 | 3 | 014111634 | 042021698 | 043866673 | ||||||||||||
TCGA-LGG | TCGA-CS-5395 | 0 | 08502822 | 014971776 | 0 | 09932025 | 00067975316 | 2 | 0052374925 | 018397054 | 076365453 | ||||||||||||
TCGA-LGG | TCGA-CS-5396 | 1 | 099839586 | 00016040892 | 1 | 099967945 | 000032062363 | 3 | 00016345463 | 029090768 | 07074577 | ||||||||||||
TCGA-LGG | TCGA-CS-5397 | 0 | 049304244 | 050695753 | 0 | 08829839 | 0117016025 | 3 | 038702008 | 021211159 | 040086827 | ||||||||||||
TCGA-LGG | TCGA-CS-6186 | 0 | 099913234 | 00008676725 | 0 | 099956185 | 000043818905 | 3 | 00008662089 | 016898473 | 083014905 | ||||||||||||
TCGA-LGG | TCGA-CS-6188 | 0 | 052768165 | 047231838 | 0 | 08584221 | 014157787 | 3 | 019437431 | 047675493 | 03288707 | ||||||||||||
TCGA-LGG | TCGA-CS-6290 | 1 | 09102666 | 008973339 | 0 | 09462997 | 0053700306 | 3 | 0104100704 | 025633416 | 06395651 | ||||||||||||
TCGA-LGG | TCGA-CS-6665 | 1 | 099600047 | 0003999501 | 0 | 099756086 | 00024391294 | 3 | 0011873978 | 001634113 | 097178483 | ||||||||||||
TCGA-LGG | TCGA-CS-6666 | 1 | 021655986 | 07834402 | 0 | 09327296 | 0067270435 | 3 | 017667453 | 036334327 | 045998225 | ||||||||||||
TCGA-LGG | TCGA-CS-6667 | 1 | 012061995 | 087938 | 0 | 095699733 | 0043002643 | 2 | 063733935 | 019323014 | 016943048 | ||||||||||||
TCGA-LGG | TCGA-CS-6668 | 1 | 0076787576 | 09232124 | 1 | 04240933 | 057590663 | 2 | 06810894 | 013706882 | 018184178 | ||||||||||||
TCGA-LGG | TCGA-CS-6669 | 0 | 08488156 | 01511844 | 0 | 094018847 | 005981148 | 2 | 0037862387 | 002352077 | 09386168 | ||||||||||||
TCGA-LGG | TCGA-DU-5849 | 1 | 005773187 | 094226813 | 1 | 08664153 | 013358466 | 2 | 072753835 | 015028271 | 012217898 | ||||||||||||
TCGA-LGG | TCGA-DU-5851 | 1 | 09963994 | 0003600603 | 0 | 099808073 | 00019192374 | 3 | 00060602655 | 012558761 | 08683521 | ||||||||||||
TCGA-LGG | TCGA-DU-5852 | 0 | 09998591 | 000014091856 | 0 | 099954873 | 000045121062 | 3 | 0002267452 | 00038046916 | 099392784 | ||||||||||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0010986943 | 09890131 | 0 | 09549844 | 0045015533 | 2 | 08603989 | 0077804394 | 0061796777 | ||||||||||||
TCGA-LGG | TCGA-DU-5854 | 0 | 09567354 | 0043264627 | 0 | 098768765 | 0012312326 | 3 | 01194655 | 027027336 | 06102612 | ||||||||||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0009312956 | 09906871 | 0 | 046602532 | 053397465 | 3 | 0008289882 | 097042197 | 0021288157 | ||||||||||||
TCGA-LGG | TCGA-DU-5871 | 1 | 005623634 | 09437636 | 0 | 09449439 | 005505607 | 2 | 042517176 | 020180763 | 037302068 | ||||||||||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0062359583 | 09376405 | 0 | 015278916 | 08472108 | 2 | 012133307 | 048199505 | 039667192 | ||||||||||||
TCGA-LGG | TCGA-DU-5874 | 1 | 022858672 | 077141327 | 1 | 06457066 | 03542934 | 2 | 058503634 | 020639434 | 02085693 | ||||||||||||
TCGA-LGG | TCGA-DU-6397 | 1 | 097691274 | 002308724 | 1 | 09908213 | 0009178773 | 3 | 00048094327 | 00412339 | 09539566 | ||||||||||||
TCGA-LGG | TCGA-DU-6399 | 1 | 00023920655 | 099760795 | 0 | 09970073 | 00029926652 | 2 | 098691386 | 0007037292 | 0006048777 | ||||||||||||
TCGA-LGG | TCGA-DU-6400 | 1 | 0030923586 | 09690764 | 1 | 037771282 | 06222872 | 2 | 09710506 | 0015339471 | 001360994 | ||||||||||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0014545513 | 098545444 | 0 | 045332992 | 054667014 | 2 | 0878585 | 006398724 | 005742785 | ||||||||||||
TCGA-LGG | TCGA-DU-6404 | 0 | 08563024 | 014369765 | 0 | 09857318 | 00142681915 | 3 | 0012578745 | 08931047 | 009431658 | ||||||||||||
TCGA-LGG | TCGA-DU-6405 | 0 | 094122344 | 0058776554 | 0 | 09657707 | 0034229323 | 3 | 0015099723 | 0858934 | 012596628 | ||||||||||||
TCGA-LGG | TCGA-DU-6407 | 1 | 00046772743 | 099532276 | 0 | 095787287 | 0042127114 | 2 | 095650303 | 0019410672 | 0024086302 | ||||||||||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0032852467 | 09671475 | 0 | 02978783 | 070212173 | 3 | 046377006 | 04552443 | 008098562 | ||||||||||||
TCGA-LGG | TCGA-DU-6410 | 1 | 084198 | 015801999 | 1 | 09610981 | 0038901985 | 3 | 0029748935 | 0547783 | 042246798 | ||||||||||||
TCGA-LGG | TCGA-DU-6542 | 1 | 099541724 | 0004582765 | 0 | 099690056 | 00030994152 | 3 | 00036504513 | 0033356518 | 0962993 | ||||||||||||
TCGA-LGG | TCGA-DU-7008 | 1 | 00027017966 | 09972982 | 0 | 09924154 | 0007584589 | 2 | 0945233 | 0033200152 | 0021566862 | ||||||||||||
TCGA-LGG | TCGA-DU-7010 | 1 | 09090629 | 0090937115 | 0 | 083999664 | 016000335 | 3 | 0011747591 | 011156695 | 08766855 | ||||||||||||
TCGA-LGG | TCGA-DU-7014 | -1 | 00067384504 | 09932615 | 0 | 09144437 | 008555635 | 2 | 090214694 | 005846623 | 003938676 | ||||||||||||
TCGA-LGG | TCGA-DU-7015 | 1 | 011059116 | 08894088 | 0 | 09457512 | 005424881 | 2 | 04990067 | 023008518 | 027090812 | ||||||||||||
TCGA-LGG | TCGA-DU-7018 | 1 | 06190684 | 038093168 | 1 | 09720721 | 0027927874 | 3 | 002608347 | 03462771 | 06276394 | ||||||||||||
TCGA-LGG | TCGA-DU-7019 | 1 | 006866228 | 09313377 | 0 | 068647516 | 031352484 | 3 | 06280373 | 02546188 | 011734395 | ||||||||||||
TCGA-LGG | TCGA-DU-7294 | 1 | 039513415 | 06048658 | 1 | 04910898 | 05089102 | 2 | 044678423 | 011827048 | 04349453 | ||||||||||||
TCGA-LGG | TCGA-DU-7298 | 1 | 002178117 | 097821885 | 0 | 058896303 | 041103697 | 3 | 04621931 | 040058115 | 013722575 | ||||||||||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0050494254 | 094950575 | 0 | 09805993 | 0019400762 | 3 | 088520575 | 003754964 | 0077244624 | ||||||||||||
TCGA-LGG | TCGA-DU-7300 | 1 | 020334144 | 07966585 | 1 | 021174264 | 078825736 | 3 | 06957292 | 014594184 | 015832895 | ||||||||||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0028517082 | 09714829 | 0 | 07594931 | 024050693 | 2 | 07559878 | 013617343 | 010783881 | ||||||||||||
TCGA-LGG | TCGA-DU-7302 | 1 | 007878401 | 092121595 | 1 | 097414124 | 0025858777 | 3 | 059945434 | 013100924 | 02695364 | ||||||||||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0049359404 | 09506406 | 0 | 09947084 | 0005291605 | 3 | 05746174 | 017312215 | 025226048 | ||||||||||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0774658 | 022534202 | 0 | 09720191 | 0027980946 | 2 | 007909051 | 04979186 | 042299092 | ||||||||||||
TCGA-LGG | TCGA-DU-7309 | 1 | 002068546 | 097931457 | 0 | 091696864 | 008303132 | 3 | 091011685 | 0041825026 | 0048058107 | ||||||||||||
TCGA-LGG | TCGA-DU-8162 | 0 | 019030987 | 08096902 | 0 | 084344435 | 015655571 | 3 | 06724078 | 015660264 | 017098951 | ||||||||||||
TCGA-LGG | TCGA-DU-8164 | 1 | 0026989132 | 09730109 | 1 | 06119184 | 038808158 | 2 | 078654927 | 011851947 | 00949313 | ||||||||||||
TCGA-LGG | TCGA-DU-8165 | 0 | 099918324 | 000081673806 | 0 | 09982692 | 00017308301 | 3 | 00077142627 | 001586733 | 097641844 | ||||||||||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0062617026 | 093738294 | 0 | 052265906 | 047734097 | 2 | 0571523 | 027175376 | 015672325 | ||||||||||||
TCGA-LGG | TCGA-DU-8167 | 1 | 008068282 | 09193171 | 0 | 08626991 | 013730097 | 2 | 07117111 | 014616342 | 014212546 | ||||||||||||
TCGA-LGG | TCGA-DU-8168 | 1 | 04501781 | 05498219 | 1 | 09405718 | 005942822 | 3 | 028535154 | 039651006 | 031813842 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 013576113 | 08642388 | 0 | 098667485 | 0013325148 | 3 | 06805368 | 011191124 | 020755199 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0038810804 | 09611892 | 0 | 094154674 | 005845324 | 2 | 07418394 | 01198958 | 013826479 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 036534345 | 06346565 | 0 | 097664696 | 0023353029 | 2 | 0076500095 | 07058904 | 021760948 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 057493186 | 042506814 | 0 | 08586593 | 014134066 | 3 | 024835269 | 018135522 | 05702921 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 017411166 | 082588834 | 0 | 08903419 | 0109658085 | 2 | 026840523 | 031951824 | 041207647 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 00015382263 | 099846184 | 0 | 09784259 | 0021574067 | 3 | 099424005 | 00014788082 | 0004281163 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 099497885 | 000502115 | 0 | 09904406 | 0009559399 | 3 | 00076062134 | 003340487 | 09589889 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 01338958 | 08661042 | 1 | 010181248 | 08981875 | 2 | 08703488 | 0033631936 | 0096019216 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 007097701 | 092902297 | 1 | 0049773447 | 09502266 | 2 | 08236395 | 0043779366 | 013258114 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 00054852334 | 09945148 | 1 | 00052813343 | 09947187 | 2 | 095740056 | 0030734295 | 0011865048 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 00015218158 | 099847823 | 0 | 09977216 | 00022783307 | 3 | 097611564 | 0011231668 | 0012652792 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 090418625 | 009581377 | 1 | 09320215 | 006797852 | 3 | 015433969 | 006605734 | 0779603 | ||||||||||||
TCGA-LGG | TCGA-EZ-7265A | -1 | 001654544 | 09834546 | -1 | 092290026 | 0077099696 | -1 | 091443384 | 0045349486 | 0040216673 | ||||||||||||
TCGA-LGG | TCGA-FG-5964 | 1 | 095945925 | 004054074 | 1 | 09480585 | 0051941562 | 2 | 0052469887 | 018844457 | 075908554 | ||||||||||||
TCGA-LGG | TCGA-FG-6688 | 0 | 041685596 | 0583144 | 0 | 0400786 | 0599214 | 3 | 032869554 | 028211078 | 03891937 | ||||||||||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0040960647 | 09590394 | 0 | 088871056 | 01112895 | 2 | 078484637 | 01247657 | 009038795 | ||||||||||||
TCGA-LGG | TCGA-FG-6691 | 1 | 00066411127 | 09933589 | 0 | 09705485 | 002945148 | 2 | 082394814 | 01353293 | 004072261 | ||||||||||||
TCGA-LGG | TCGA-FG-6692 | 0 | 099044985 | 0009550158 | 0 | 098370695 | 0016293105 | 3 | 002482948 | 023811981 | 07370507 | ||||||||||||
TCGA-LGG | TCGA-FG-7643 | 0 | 067991304 | 032008696 | 0 | 094600123 | 0053998843 | 2 | 032237333 | 020420441 | 047342223 | ||||||||||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 00037180893 | 09962819 | 0 | 098237246 | 0017627545 | 2 | 09685786 | 0019877713 | 0011543726 | ||||||||||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 0023916386 | 097608364 | 1 | 003330537 | 096669465 | 2 | 016134319 | 0751304 | 0087352775 | ||||||||||||
TCGA-LGG | TCGA-FG-A713 | 1 | 020932822 | 07906717 | 1 | 053740746 | 046259254 | 2 | 068370515 | 013241291 | 018388201 | ||||||||||||
TCGA-LGG | TCGA-HT-7473 | 1 | 026437023 | 073562974 | 0 | 09914391 | 0008560891 | 2 | 009070598 | 05834457 | 032584828 | ||||||||||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0014885316 | 09851147 | 0 | 093397486 | 006602513 | 3 | 09713343 | 0009202645 | 0019462984 | ||||||||||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0078306936 | 09216931 | 0 | 044295275 | 055704725 | 2 | 06683338 | 024550638 | 00861598 | ||||||||||||
TCGA-LGG | TCGA-HT-7616 | 1 | 0994089 | 0005911069 | 1 | 08912444 | 010875558 | 3 | 00015109215 | 00081261955 | 09903628 | ||||||||||||
TCGA-LGG | TCGA-HT-7680 | 0 | 01775255 | 08224745 | 0 | 079779327 | 020220678 | 2 | 06160002 | 021518312 | 016881672 | ||||||||||||
TCGA-LGG | TCGA-HT-7684 | 1 | 099250317 | 00074968883 | 0 | 09977216 | 00022783307 | 3 | 0001585032 | 0011880362 | 09865346 | ||||||||||||
TCGA-LGG | TCGA-HT-7686 | 1 | 043986762 | 05601324 | 0 | 09985134 | 00014866153 | 3 | 08800356 | 0017893802 | 010207067 | ||||||||||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0508178 | 049182203 | 0 | 09986749 | 00013250223 | 3 | 007351807 | 06479417 | 027854022 | ||||||||||||
TCGA-LGG | TCGA-HT-7692 | 1 | 0006764646 | 09932354 | 1 | 00017718028 | 099822825 | 2 | 084003216 | 009709251 | 00628754 | ||||||||||||
TCGA-LGG | TCGA-HT-7693 | 1 | 08835126 | 011648734 | 0 | 098880965 | 0011190402 | 2 | 00389769 | 060259813 | 035842496 | ||||||||||||
TCGA-LGG | TCGA-HT-7694 | 1 | 006299064 | 093700933 | 1 | 06663645 | 03336355 | 3 | 06368097 | 02515797 | 011161056 | ||||||||||||
TCGA-LGG | TCGA-HT-7855 | 1 | 013434944 | 086565053 | 0 | 06805072 | 031949285 | 3 | 03900612 | 035394293 | 02559958 | ||||||||||||
TCGA-LGG | TCGA-HT-7856 | 1 | 0037151825 | 09628482 | 1 | 045108467 | 05489153 | 3 | 0024809493 | 094240344 | 0032787096 | ||||||||||||
TCGA-LGG | TCGA-HT-7860 | 0 | 09996338 | 000036614697 | 0 | 099890125 | 00010987312 | 3 | 00023981468 | 004139189 | 095620996 | ||||||||||||
TCGA-LGG | TCGA-HT-7874 | 1 | 027373514 | 07262649 | 1 | 061277324 | 03872268 | 3 | 030690825 | 04321765 | 026091516 | ||||||||||||
TCGA-LGG | TCGA-HT-7879 | 1 | 006545533 | 09345446 | 0 | 07643643 | 023563562 | 3 | 07316188 | 01349482 | 0133433 | ||||||||||||
TCGA-LGG | TCGA-HT-7882 | 0 | 099826247 | 00017375927 | 0 | 099920684 | 0000793176 | 3 | 00026636408 | 0011237644 | 098609877 | ||||||||||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0045437213 | 09545628 | 0 | 09804874 | 0019512545 | 2 | 067944294 | 020575646 | 011480064 | ||||||||||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0090937115 | 09090629 | 0 | 08061669 | 019383314 | 2 | 069696444 | 017501967 | 012801588 | ||||||||||||
TCGA-LGG | TCGA-HT-8105 | 1 | 09291196 | 0070880495 | 1 | 09865976 | 0013402403 | 3 | 036353382 | 007970196 | 055676425 | ||||||||||||
TCGA-LGG | TCGA-HT-8106 | 1 | 09987081 | 00012918457 | 0 | 099922514 | 00007748164 | 3 | 0019909225 | 0057560045 | 09225307 | ||||||||||||
TCGA-LGG | TCGA-HT-8107 | 0 | 006150854 | 09384914 | 0 | 018944609 | 08105539 | 2 | 071847403 | 015055439 | 013097167 | ||||||||||||
TCGA-LGG | TCGA-HT-8111 | 1 | 062096643 | 037903354 | 0 | 09220272 | 007797278 | 3 | 00015017459 | 090417147 | 009432678 | ||||||||||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0003941571 | 099605846 | 0 | 00025608707 | 099743915 | 2 | 088384247 | 009021307 | 002594447 | ||||||||||||
TCGA-LGG | TCGA-HT-8114 | 1 | 09404078 | 005959219 | 0 | 09970708 | 00029292419 | 3 | 0015292862 | 028022403 | 07044831 | ||||||||||||
TCGA-LGG | TCGA-HT-8563 | 1 | 099999154 | 843094E-06 | 0 | 099999607 | 39515203E-06 | 3 | 32918017E-06 | 031742522 | 068257153 | ||||||||||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 06915494 | 030845058 | 0 | 045883363 | 054116637 | 3 | 012257236 | 02777765 | 059965116 | ||||||||||||
TCGA-LGG | TCGA-HT_A614 | 1 | 08180474 | 018195263 | 0 | 09584989 | 00415011 | 2 | 0067164555 | 0059489973 | 087334543 | ||||||||||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0035779487 | 09642206 | 0 | 07277821 | 0272218 | 2 | 07607039 | 014429174 | 009500429 |
22 Algorithm performance
We used 15 of the train set as a validation set and selected the model pa-rameters that achieved the best performance on this validation set where themodel achieved an area under receiver operating characteristic curve (AUC) of088 for the IDH mutation status prediction an AUC of 076 for the 1p19qco-deletion prediction an AUC of 075 for the grade prediction and a meansegmentation DICE score of 081 The selected model parameters are shown inAppendix F We then trained a model using these parameters and the full trainset and evaluated it on the independent test set
For the genetic and histological feature predictions we achieved an AUC of090 for the IDH mutation status prediction an AUC of 085 for the 1p19qco-deletion prediction and an AUC of 081 for the grade prediction in thetest set The full results are shown in Table 2 with the corresponding receiveroperating characteristic (ROC)-curves in Figure 3 Table 2 also shows the resultsin (clinically relevant) subgroups of patients This shows that we achieved anIDH-AUC of 081 in low grade glioma (LGG) (grade IIIII) an IDH-AUC of064 in high grade glioma (HGG) (grade IV) and a 1p19q-AUC of 073 in LGGWhen only predicting LGG vs HGG instead of predicting the individual gradeswe achieved an AUC of 091 In Appendix A we provide confusion matrices forthe IDH 1p19q and grade predictions as well as a confusion matrix for thefinal WHO 2016 subtype which shows that only one patient was predicted asa non-existing WHO 2016 subtype In Appendix C we provide the individualpredictions and ground truth labels for all patients in the test set to allow forthe calculation of additional metrics
For the automatic segmentation we achieved a mean DICE score of 084 amean Hausdorff distance of 189 mm and a mean volumetric similarity coeffi-cient of 090 Figure 4 shows boxplots of the DICE scores Hausdorff distancesand volumetric similarity coefficients for the different patients in the test set InAppendix B we show five patients that were randomly selected from both theTCGA-LGG and TCGA-GBM data collections to demonstrate the automaticsegmentations made by our method
23 Model interpretability
To provide insight into the behavior of our model we created saliency mapswhich show which parts of the scans contributed the most to the predictionThese saliency maps are shown in Figure 5 for two example patients from thetest set It can be seen that for the LGG the network focused on a bright rim inthe T2w-FLAIR scan whereas for the HGG it focused on the enhancement in thepost-contrast T1w scan To aid further interpretation we provide visualizationsof selected filter outputs in the network in Appendix D which also show thatthe network focuses on the tumor and these filters seem to recognize specificimaging features such as the contrast enhancement and T2w-FLAIR brightness
7
Table 2 Evaluation results of the final model on the test set
Patientgroup
Task AUC Accuracy Sensitivity Specificity
All IDH 090 084 072 0931p19q 085 089 039 095Grade (IIIIIIV) 081 071 NA NAGrade II 091 086 075 089Grade III 069 075 017 094Grade IV 091 082 095 066LGG vs HGG 091 084 072 093
LGG IDH 081 074 073 0771p19q 073 076 039 089
HGG IDH 064 094 040 096
Abbreviations AUC area under receiver operating characteristic curve IDHisocitrate dehydrogenase LGG low grade glioma HGG high grade glioma
Figure 3 Receiver operating characteristic (ROC)-curves of the genetic andhistological features evaluated on the test set The crosses indicate the locationof the decision threshold for the reported accuracy sensitivity and specificity
8
Figure 4 DICE scores Hausdorff distances and volumetric similarity coeffi-cients for all patients in the test set The DICE score is a measure of theoverlap between the ground truth and predicted segmentation (where 1 indi-cates perfect overlap) The Hausdorff distance is a measure of the agreementbetween the boundaries of the ground truth and predicted segmentation (loweris better) The volumetric similarity coefficient is a measure of the agreementin volume (where 1 indicates perfect agreement)
9
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Saliency maps of a low grade glioma patient (TCGA-DU-6400) This is an IDH mu-tated 1p19q co-deleted grade II tumor The network focuses on a rim of brightnessin the T2w-FLAIR scan
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) Saliency maps of a high grade glioma patient (TCGA-06-0238) This is an IDHwildtype grade IV tumor The network focuses on enhancing spots around the necrosison the post-contrast T1w scan
Figure 5 Saliency maps of two patients from the test set showing areas thatare relevant for the prediction
10
24 Model robustness
By not excluding scans from our train set based on radiological characteristicswe were able to make our model robust to low scan quality as can be seen in anexample from the test set in Figure 6 Even though this example scan containedimaging artifacts our method was able to properly segment the tumor (DICEscore of 087) and correctly predict the tumor as an IDH wildtype grade IVtumor
Figure 6 Example of a T2w-FLAIR scan containing imaging artifacts and theautomatic segmentation (red overlay) made by our method It was correctlypredicted as an IDH wildtype grade IV glioma This is patient TCGA-06-5408from the TCGA-GBM collection
Finally we considered two examples of scans that were incorrectly predictedby our method see Figure 7 These two examples were chosen because ournetwork assigned high prediction scores to the wrong classes for these casesFigure 7a shows an example of a grade II IDH mutated 1p19q co-deletedglioma that was predicted as grade IV IDH wildtype by our method Ourmethodrsquos prediction was most likely caused by the hyperintensities in the post-contrast T1w scan being interpreted as contrast enhancement Since these hy-perintensities are also present in the pre-contrast T1w scan they are most likelycalcifications and the radiological appearance of this tumor is indicative of anoligodendroglioma Figure 7b shows an example of a grade IV IDH wildtypeglioma that was predicted as a grade III IDH mutated glioma by our method
11
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) TCGA-DU-6410 from the TCGA-LGG collection The ground truth histopatho-logical analysis indicated this glioma was grade II IDH mutated 1p19q co-deletedbut our method predicted it as a grade IV IDH wildtype
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) TCGA-76-7664 from the TCGA-HGG collection Histopathologically this gliomawas grade IV IDH wildtype but our method predicted it as grade III IDH mutated
Figure 7 Examples of scans that were incorrectly predicted by our method
12
3 Discussion
We have developed a method that can predict the IDH mutation status 1p19qco-deletion status and grade of glioma while simultaneously providing the tu-mor segmentation based on pre-operative MRI scans For the genetic andhistological feature predictions we achieved an AUC of 090 for the IDH mu-tation status prediction an AUC of 085 for the 1p19q co-deletion predictionand an AUC of 081 for the grade prediction in the test set
In an independent test set which contained data from 13 different instituteswe demonstrated that our method predicts these features with good overall per-formance we achieved an AUC of 090 for the IDH mutation status predictionan AUC of 085 for the 1p19q co-deletion prediction and an AUC of 081 forthe grade prediction and a mean whole tumor DICE score of 084 This per-formance on unseen data that was only used during the final evaluation of thealgorithm and that was purposefully not used to guide any decisions regardingthe method design shows the true generalizability of our method Using thelatest GPU capabilities we were able to train a large model which uses thefull 3D scan as input Furthermore by using the largest most diverse patientcohort to date we were able to make our method robust to the heterogeneitythat is naturally present in clinical imaging data such that it generalizes forbroad application in clinical practice
By using a multi-task network our method could learn the context betweendifferent features For example IDH wildtype and 1p19q co-deletion are mu-tually exclusive [21] If two separate methods had been used one to predict theIDH status and one to predict the 1p19q co-deletion status an IDH wildtypeglioma might be predicted to be 1p19q co-deleted which does not stroke withthe clinical reality Since our method learns both of these genetic features si-multaneously it correctly learned not to predict 1p19q co-deletion in tumorsthat were IDH wildtype there was only one patient in which our algorithm pre-dicted a tumor to be both IDH wildtype and 1p19q co-deleted Furthermoreby predicting the genetic and histological features individually instead of onlypredicting the WHO 2016 category it is possible to adopt updated guidelinessuch as cIMPACT-NOW future-proofing our method [22]
Some previous studies also used multi-task networks to predict the geneticand histological features of glioma [23 24 25] Tang et al [23] used a multi-tasknetwork that predicts multiple genetic features as well as the overall survivalof glioblastoma Since their method only works for glioblastoma patients thetumor grade must be known in advance complicating the use of their methodin the pre-operative setting when tumor grade is not yet known Furthermoretheir method requires a tumor segmentation prior to application of their methodwhich is a time-consuming expert task In a study by Xue et al [24] a multi-task network was used with a structure similar to the one proposed in thispaper to segment the tumor and predict the grade (LGG or HGG) and IDHmutation status However they do not predict the 1p19q co-deletion statusneeded for the WHO 2016 categorization Lastly Decuyper et al [25] useda multi-task network that predicts the IDH mutation and 1p19q co-deletion
13
status and the tumor grade (LGG or HGG) Their method requires a tumorsegmentation as input which they obtain from a U-Net that is applied earlierin their pipeline thus their method requires two networks instead of the singlenetwork we use in our method These differences aside the most importantlimitation of each of these studies is the lack of an independent test set forevaluating their results It is now considered essential that an independent testset is used to prevent an overly optimistic estimate of a methodrsquos performance[15 26 27 28] Thus our study improves on this previous work by providinga single network that combines the different tasks being trained on a moreextensive and diverse dataset not requiring a tumor segmentation as an in-put providing all information needed for the WHO 2016 categorization andcrucially by being evaluated in an independent test set
An important genetic feature that is not predicted by our method is theO6-methylguanine-methyltransferase (MGMT) methylation status Althoughthe MGMT methylation status is not part of the WHO 2016 categorizationit is part of clinical management guidelines and is an important prognosticmarker in glioblastoma [4] In the initial stages of this study we attemptedto predict the MGMT methylation status however the performance of thisprediction was poor Furthermore the methylation cutoff level which is usedto determine whether a tumor is MGMT methylated shows a wide varietybetween institutes leading to inconsistent results [29] We therefore opted not toinclude the MGMT prediction at all rather than to provide a poor prediction ofan unsharply defined parameter Although some methods attempted to predictthe MGMT status with varying degrees of success there is still an ongoingdiscussion on the validity of MR imaging features of the MGMT status [23 3031 32 33]
Our method shows good overall performance but there are noticeable per-formance differences between tumor categories For example when our methodpredicts a tumor as an IDH wildtype glioblastoma it is correct almost all of thetime On the other hand it has some difficulty differentiating IDH mutated1p19q co-deleted low-grade glioma from other low-grade glioma The sensitiv-ity for the prediction of grade III glioma was low which might be caused bythe lack of a central pathology review Because of this there were differences inmolecular testing and histological analysis and it is known that distinguishingbetween grade II and grade III has a poor observer reliability [34] Althoughour method can be relevant for certain subgroups our methodrsquos performancestill needs to be improved to ensure relevancy for the full patient population
In future work we aim to increase the performance of our method by includ-ing perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI)since there has been an increasing amount of evidence that these physiologi-cal imaging modalities contain additional information that correlates with thetumorrsquos genetic status and aggressiveness [35 36] They were not included inthis study since PWI and to a lesser extent DWI are not as ingrained in theclinical imaging routine as the structural scans used in this work [19 20] Thusincluding these modalities would limit our methodrsquos clinical applicability andsubstantially reduce the number of patients in the train and test set However
14
PWI and DWI are increasingly becoming more commonplace which will allowincluding these in future research and which might improve performance
In conclusion we have developed a non-invasive method that can predict theIDH mutation status 1p19q co-deletion status and grade of glioma while atthe same time segmenting the tumor based on pre-operative MRI scans withhigh overall performance Although the performance of our method might needto be improved before it will find widespread clinical acceptance we believe thatthis research is an important step forward in the field of radiomics Predict-ing multiple clinical features simultaneously steps away from the conventionalsingle-task methods and is more in line with the clinical practice where multipleclinical features are considered simultaneously and may even be related Fur-thermore by not limiting the patient population used to develop our method toa selection based on clinical or radiological characteristics we alleviate the needfor a priori (expert) knowledge which may not always be available Althoughsteps still have to be taken before radiomics will find its way into the clinicespecially in terms of performance our work provides a crucial step forward byresolving some of the hurdles of clinical implementation now and paving theway for a full transition in the future
4 Methods
41 Patient population
The train set was collected from four in-house datasets and five publicly avail-able datasets In-house datasets were collected from four different institutesErasmus MC (EMC) Haaglanden Medical Center (HMC) Amsterdam UMC(AUMC) [37] and University Medical Center Utrecht (UMCU) Four of the fivepublic datasets were collected from The Cancer Imaging Archive (TCIA) [38]the Repository of Molecular Brain Neoplasia Data (REMBRANDT) collection[39] the Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multi-forme (CPTAC-GBM) collection [40] the Ivy Glioblastoma Atlas Project (IvyGAP) collection [41 42] and the Brain-Tumor-Progression collection [43] Thefifth dataset was the 2019 Brain Tumor Segmentation challenge (BraTS) chal-lenge dataset [44 45 46] from which we excluded the patients that were alsoavailable in the TCGA-LGG and TCGA-GBM collections [47 48]
For the internal datasets from the EMC and the HMC manual segmentationswere available which were made by four different clinical experts For patientswhere segmentations from more than one observer were available we randomlypicked one of the segmentations to use in the train set The segmentationsfrom the AUMC data were made by a single observer of the study by Visseret al [37] From the public datasets only the BraTS dataset and the Brain-Tumor-Progression dataset provided manual segmentations Segmentations ofthe BraTS dataset as provided in the 2019 training and validation set wereused For the Brain-Tumor-Progression dataset the segmentations as providedin the TCIA data collection were used
15
Patients were included if pre-operative pre- and post-contrast T1w T2wand T2w-FLAIR scans were available no further inclusion criteria were setFor example patients were not excluded based on the radiological characteristicsof the scan such as low imaging quality or imaging artifacts or the gliomarsquosclinical characteristics such as the grade If multiple scans of the same contrasttype were available in a single scan session (eg multiple T2w scans) the scanupon which the segmentation was made was selected If no segmentation wasavailable or the segmentation was not made based on that scan contrast thescan with the highest axial resolution was used where a 3D acquisition waspreferred over a 2D acquisition
For the in-house data genetic and histological data were available for theEMC HMC and UMCU dataset which were obtained from analysis of tumortissue after biopsy or resection Genetic and histological data of the publicdatasets were also available for the REMBRANDT CPTAC-GBM and IvyGAP collections Data for the REMBRANDT and CPTAC-GBM collectionswas collected from the clinical data available at the TCIA [40 39] For theIvy GAP collection the genetic and histological data were obtained from theSwedish Institute at httpsivygapswedishorghome
As a test set we used the TCGA-LGG and TCGA-GBM collections from theTCIA [47 48] Genetic and histological labels were obtained from the clinicaldata available at the TCIA Segmentations were used as available from theTCIA based on the 2018 BraTS challenge [45 49 50] The inclusion criteriafor the patients included in the BraTS challenge were the same as our inclusioncriteria the presence of a pre-operative pre- and post-contrast T1w T2w andT2w-FLAIR scan Thus patients from the TCGA-LGG and TCGA-GBM wereincluded if a segmentation from the BraTS challenge was available Howeverfor three patients we found that although they did have manual segmentationsthey did not meet our inclusion requirements TCGA-08-0509 and TCGA-08-0510 from TCGA-GBM because they did not have a pre-contrast T1w scan andTCGA-FG-7634 from TCGA-LGG because there was no post-contrast T1wscan
42 Automatic segmentation in the train set
To present our method with a large diversity in scans we wanted to include asmany patients in the train set as possible from the different datasets Thereforewe performed automatic segmentation in patients that did not have manualsegmentations To this end we used an initial version of our network (presentedin Section 44) without the additional layers that were needed for the predictionof the genetic and histological features This network was initially trained usingall patients in the train set for whom a manual segmentation was availableand this trained network was then applied to all patients for which a manualsegmentation was not available The resulting automatic segmentations wereinspected and if their quality was acceptable they were added to the trainset The network was then trained again using this increased dataset and wasapplied to scans that did not yet have a segmentation of acceptable quality
16
This process was repeated until an acceptable segmentation was available forall patients which constituted our final complete train set
43 Pre-processing
For all datasets except for the BraTS dataset for which the scans were alreadyprovided in NIfTI format the scans were converted from DICOM format toNIfTI format using dcm2niix version v1020190410 [51] We then registeredall scans to the MNI152 T1w and T2w atlases version ICBM 2009a whichhad a resolution of 1x1x1 mm3 and a size of 197x233x189 voxels [52 53] Thescans were affinely registered using Elastix 50 [54 55] The pre- and post-contrast T1w scans were registered to the T1w atlas the T2w and T2w-FLAIRscans were registered to the T2w atlas When a manual segmentation wasavailable for patients from the in-house datasets the registration parametersthat resulted from registering the scan used during the segmentation were usedto transform the segmentation to the atlas In the case of the public datasetswe used the registration parameters of the T2w-FLAIR scans to transform thesegmentations
After the registration all scans were N4 bias field corrected using SimpleITKversion 124 [56] A brain mask was made for the atlas using HD-BET both forthe T1w atlas and the T2w atlas [57] This brain mask was used to skull stripall registered scans and crop them to a bounding box around the brain maskreducing the amount of background present in the scans resulting in a scan sizeof 152x182x145 voxels Subsequently the scans were normalized such that foreach scan the average image intensity was 0 and the standard deviation of theimage intensity was 1 within the brain mask Finally the background outsidethe brain mask was set to the minimum intensity value within the brain mask
Since the segmentation could sometimes be rugged at the edges after regis-tration especially when the segmentations were initially made on low-resolutionscans we smoothed the segmentation using a 3x3x3 median filter (this was onlydone in the train set) For segmentations that contained more than one la-bel eg when the tumor necrosis and enhancement were separately segmentedall labels were collapsed into a single label to obtain a single segmentation ofthe whole tumor The genetic and histological labels and the segmentations ofeach patient were one-hot encoded The four scans ground truth labels andsegmentation of each patient were then used as the input to the network
44 Model
We based the architecture of our model on the U-Net architecture with someadaptations made to allow for a full 3D input and the auxiliary tasks [58] Ournetwork architecture which we have named PrognosAIs Structure-Net or PS-Net for short can be seen in Figure 8
To use the full 3D scan as an input to the network we replaced the firstpooling layer that is usually present in the U-Net with a strided convolutionwith a kernel size of 9x9x9 and a stride of 3x3x3 In the upsampling branch of
17
32
32 64
128
256
512 256
7x8x7256 128
128 64
64 32
32 2
Segmentation
145x182x152
49x61x51
25x31x26
13x16x13
1472
512 2IDH
512 2
1p19q
512 3Grade
Batch normalization Concatenation Convolution amp ReLU3x3x3
Convolution amp Softmax1x1x1
(De)convolution amp ReLU9x9x9
stride 3x3x3
Dense amp ReLU Dense amp Softmax Dropout
Max pooling2x2x2
Up-convolution amp ReLU2x2x2
Global maxpooling
Figure 8 Overview of the PrognosAIs Structure-Net (PS-Net) architecture usedfor our model The numbers below the different layers indicate the number offilters dense units or features at that layer We have also indicated the featuremap size at the different depths of the network
the network the last up-convolution is replaced by a deconvolution with thesame kernel size and stride
At each depth of the network we have added global max-pooling layersdirectly after the dropout layer to obtain imaging features that can be used topredict the genetic and histological features We chose global pooling layers asthey do not introduce any additional parameters that need to be trained thuskeeping the memory required by our model manageable The features from thedifferent depths of the network were concatenated and fed into three differentdense layers one for each of the genetic and histological outputs
l2 kernel regularization was used in all convolutional layers except for thelast convolutional layer used for the output of the segmentation In total thismodel contained 27042473 trainable an 2944 non-trainable parameters
18
45 Model training
Training of the model was done on eight NVidia RTX2080Tirsquos with 11GB ofmemory using TensorFlow 220 [59] To be able to use the full 3D scan asinput to the network without running into memory issues we had to optimizethe memory efficiency of the model training Most importantly we used mixed-precision training which means that most of the variables of the model (such asthe weights) were stored in float16 which requires half the memory of float32which is typically used to store these variables [60] Only the last softmaxactivation layers of each output were stored as float32 We also stored ourpre-processed scans as float16 to further reduce memory usage
However even with these settings we could not use a batch size larger than1 It is known that a larger batch size is preferable as it increases the stabilityof the gradient updates and allows for a better estimation of the normalizationparameters in batch normalization layers [61] Therefore we distributed thetraining over the eight GPUs using the NCCL AllReduce algorithm whichcombines the gradients calculated on each GPU before calculating the updateto the model parameters [62] We also used synchronized batch normalizationlayers which synchronize the updates of their parameters over the distributedmodels In this way our model had a virtual batch size of eight for the gradientupdates and the batch normalization layers parameters
To provide more samples to the algorithm and prevent potential overtrain-ing we applied four types of data augmentation during training croppingrotation brightness shifts and contrast shifts Each augmentation was appliedwith a certain augmentation probability which determined the probability ofthat augmentation type being applied to a specific sample When an image wascropped a random number of voxels between 0 and 20 was cropped from eachdimension and filled with zeros For the random rotation an angle betweenminus30 and 30 degrees was selected from a uniform distribution for each dimen-sion The brightness shift was applied with a delta uniformly drawn between0 and 02 and the contrast shift factor was randomly drawn between 085 and115 We also introduced an augmentation factor which determines how ofteneach sample was parsed as an input sample during a single epoch where eachtime it could be augmented differently
For the IDH 1p19q and grade output we used a masked categorical cross-entropy loss and for the segmentation we used a DICE loss see Appendix E fordetails We used AdamW as an optimizer which has shown improved general-ization performance over Adam by introducing the weight decay parameter as aseparate parameter from the learning rate [63] The learning rate was automat-ically reduced by a factor of 025 if the loss did not improve during the last fiveepochs with a minimum learning rate of 1 middot 10minus11 The model could train for amaximum of 150 epochs and training was stopped early if the average loss overthe last five epochs did not improve Once the model was finished training theweights from the epoch with the lowest loss were restored
19
46 Hyperparameter tuning
Hyperparameters involved in the training of the model needed to be tuned toachieve the best performance We tuned a total of six hyper parameters the l2-norm the dropout rate the augmentation factor the augmentation probabilitythe optimizerrsquos initial learning rate and the optimizerrsquos weight decay A fulloverview of the trained parameters and the values tested for the different settingsis presented in Appendix F
To tune these hyperparameters we split the train set into a hyperparametertraining set (851282 patients of the full train data) and a hyperparametervalidation set (15226 patients of the full train data) Models were trained fordifferent hyperparameter settings via an exhaustive search using the hyperpa-rameter train set and then evaluated on the hyperparameter validation set Nodata augmentation was applied to the hyperparameter validation to ensure thatresults between trained models were comparable The hyperparameters that ledto the lowest overall loss in the hyperparameter validation set were chosen asthe optimal hyperparameters We trained the final model using these optimalhyperparameters and the full train set
47 Post-processing
The predictions of the network were post-processed to obtain the final predictedlabels and segmentations for the samples Since softmax activations were usedfor the genetic and histological outputs a prediction between 0 and 1 was out-putted for each class where the individual predictions summed to 1 The finalpredicted label was then considered as the class with the highest prediction scoreFor the prediction of LGG (grade IIIII) vs HGG (grade IV) the predictionscores of grade II and grade III were combined to obtain the prediction scorefor LGG the prediction score of grade IV was used as the prediction score forHGG If a segmentation contained multiple unconnected components we onlyretained the largest component to obtain a single whole tumor segmentation
48 Model evaluation
The performance of the final trained model was evaluated on the independenttest set comparing the predicted labels with the ground truth labels For thegenetic and histological features we evaluated the AUC the accuracy the sen-sitivity and the specificity using scikit-learn version 0231 for details see Ap-pendix G [64] We evaluated these metrics on the full test set and in subcate-gories relevant to the WHO 2016 guidelines We evaluated the IDH performanceseparately in the LGG (grade IIIII) and HGG (grade IV) subgroups the 1p19qperformance in LGG and we also evaluated the performance of distinguishingbetween LGG and HGG instead of predicting the individual grades
To evaluate the performance of the segmentation we calculated the DICEscores Hausdorff distances and volumetric similarity coefficient comparing theautomatic segmentation of our method and the manual ground truth segmen-
20
tations for all patients in the test set These metrics were calculated usingthe EvaluateSegmentation toolbox version 20170425 [65] for details see Ap-pendix G
To prevent an overly optimistic estimation of our modelrsquos predictive valuewe only evaluated our model on the test set once all hyperparameters werechosen and the final model was trained In this way the performance in thetest set did not influence decisions made during the development of the modelpreventing possible overfitting by fine-tuning to the test set
To gain insight into the model we made saliency maps that show whichparts of the scan contribute the most to the prediction of the CNN [66] Saliencymaps were made using tf-keras-vis 052 changing the activation function of alloutput layers from softmax to linear activations using SmoothGrad to reducethe noisiness of the saliency maps [66]
Another way to gain insight into the networkrsquos behavior is to visualize thefilter outputs of the convolutional layers as they can give some idea as to whatoperations the network applies to the scans We visualized the filter outputs ofthe last convolutional layers in the downsample and upsample path at the firstdepth (at an image size of 49x61x51) of our network These filter outputs werevisualized by passing a sample through the network and showing the convolu-tional layersrsquo outputs replacing the ReLU activation with linear activations
49 Data availability
An overview of the patients included from the public datasets used in the train-ing and testing of the algorithm and their ground truth label is available inAppendix H The data from the public datasets are available in TCIA un-der DOIs 107937K9TCIA2015588OZUZB 107937k9tcia20183rje41q1107937K9TCIA2016XLwaN6nL and 107937K9TCIA201815quzvnb Datafrom the BraTS are available at httpbraintumorsegmentationorg Datafrom the in-house datasets are not publicly available due to participant privacyand consent
410 Code availability
The code used in this paper is available on GitHub under an Apache 2 license athttpsgithubcomSvdvoortPrognosAIs_glioma This code includes thefull pipeline from registration of the patients to the final post-processing of thepredictions The trained model is also available on GitHub along with code toapply it to new patients
21
Appendices
A Confusion matrices
Tables 3 4 and 5 show the confusion matrices for the IDH 1p19q and gradepredictions and Table 6 shows the confusion matrix for the WHO 2016 subtypes
Table 4 shows that the algorithm mainly has difficulty recognizing 1p19qco-deleted tumors which are mostly predicted as 1p19q intact Table 5 showsthat most of the incorrectly predicted grade III tumors are predicted as gradeIV tumors
Table 6 shows that our algorithm often incorrectly predicts IDH-wildtypeastrocytoma as IDH-wildtype glioblastoma The latest cIMPACT-NOW guide-lines propose a new categorization in which IDH-wildtype astrocytoma thatshow either TERT promoter methylation or EFGR gene amplification or chro-mosome 7 gainchromsome 10 loss are classified as IDH-wildtype glioblastoma[22] This new categorization is proposed since the survival of patients withthose IDH-wildtype astrocytoma is similar to the survival of patients with IDH-wildtype glioblastoma [22] From the 13 IDH-wildtype astrocytoma that werewrongly predicted as IDH-wildtype glioblastoma 12 would actually be cate-gorized as IDH-wildtype glioblastoma under this new categorization Thusalthough our method wrongly predicted the WHO 2016 subtype it might ac-tually have picked up on imaging features related to the aggressiveness of thetumor which might lead to a better categorization
Table 3 Confusion matrix of the IDH predictions
Predicted
Wildtype Mutated
Actu
al
Wildtype 120 9
Mutated 25 63
Table 4 Confusion matrix of the 1p19q predictions
Predicted
Intact Co-deleted
Actu
al
Intact 197 10
Co-deleted 16 10
22
Table 5 Confusion matrix of the grade predictions
Predicted
Grade II Grade III Grade IV
Actu
al Grade II 35 6 6
Grade III 19 10 30
Grade IV 2 5 125
Table 6 Confusion matrix of the WHO 2016 predictions The rsquootherrsquo categoryindicates patients that were predicted as a non-existing WHO 2016 subtype forexample IDH wildtype 1p19q co-deleted tumors Only one patient (TCGA-HT-A5RC) was predicted as a non-existing category It was predicted as anIDH wildtype 1p19q co-deleted grade IV tumor
Predicted
Oligodendrogliom
a
IDH-m
utated
astrocytoma
IDH-w
ildtype
astrocytoma
IDH-m
utated
glioblastoma
IDH-w
ildtype
glioblastoma
Other
Actu
al
Oligodendroglioma 10 8 1 0 7 0
IDH-mutatedastrocytoma 6 34 4 3 10 0
IDH-wildtypeastrocytoma 1 2 3 2 13 1
IDH-mutatedglioblastoma 0 1 0 0 3 0
IDH-wildtypeglioblastoma 0 3 3 1 96 0
Oligodendroglioma are IDH-mutated 1p19q co-deleted grade IIIII giomaIDH-mutated astrocytoma are IDH-mutated 1p19q intact grade IIIII gliomaIDH-wildtype astrocytoma are IDH-wildtype 1p19q intact grade IIIII gliomaIDH-mutated glioblastoma are IDH-mutated grade IV gliomaIDH-wildtype glioblastoma are IDH-wildtype grade IV glioma
23
B Segmentation examples
To demonstrate the automatic segmentations made by our method we randomlyselected five patients from both the TCGA-LGG and the TCGA-GBM datasetThe scans and segmentations of the five patients from the TCGA-LGG datasetand the TCGA-GBM dataset are shown in Figures 9 and 10 respectively TheDICE score Hausdorff distance and volumetric similarity coefficient for thesepatients are given in Table 7 The method seems to mostly focus on the hyper-intensities of the T2w-FLAIR scan Despite the registrations issues that can beseen for the T2w scan in Figure 10d the tumor was still properly segmenteddemonstrating the robustness of our method
Patient DICE HD (mm) VSC
TCGA-LGG
TCGA-DU-7301 089 103 095TCGA-FG-5964 080 58 082TCGA-FG-A713 073 78 088TCGA-HT-7475 087 149 090TCGA-HT-8106 088 112 099
TCGA-GBM
TCGA-02-0037 082 226 099TCGA-08-0353 091 130 098TCGA-12-1094 090 73 093TCGA-14-3477 090 165 099TCGA-19-5951 073 197 073
Table 7 The DICE score Hausdorff distance (HD) and volumetric similaritycoefficient (VSC) for the randomly selected patients from the TCGA-LGG andTCGA-GBM data collections
24
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-DU-7301 from the TCGA-LGG data collection
(b) Patient TCGA-FG-5964 from the TCGA-LGG data collection
(c) Patient TCGA-FG-A713 from the TCGA-LGG data collection
(d) Patient TCGA-HT-7475 from the TCGA-LGG data collection
(e) Patient TCGA-HT-8106 from the TCGA-LGG data collection
Figure 9 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-LGG data collection
25
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-02-0037 from the TCGA-GBM data collection
(b) Patient TCGA-08-0353 from the TCGA-GBM data collection
(c) Patient TCGA-12-1094 from the TCGA-GBM data collection
(d) Patient TCGA-14-3477 from the TCGA-GBM data collection
(e) Patient TCGA-19-5951 from the TCGA-GBM data collection
Figure 10 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-GBM data collection
26
C Prediction results in the test set
27
D Filter output visualizations
Figures 11 and 12 show the output of the convolution filters for the same LGGpatient as shown in Figure 5a and Figures 13 and 14 show the output of theconvolution filters for the same HGG patient as shown in Figure 5b Figures 11and 13 show the outputs of the last convolution layer in the downsample pathat the feature size of 49x61x51 (the fourth convolutional layer in the network)Figures 12 and 14 show the outputs of the last convolution layer in the upsamplepath at the feature size of 49x61x51 (the nineteenth convolutional layer in thenetwork)
Comparing Figure 11 to Figure 12 and Figure 13 to Figure 14 we can seethat the convolutional layers in the upsample path do not keep a lot of detailfor the healthy part of the brain as this region seems blurred However withinthe tumor different regions can still be distinguished The different parts ofthe tumor from the scans can also be seen such as the contrast-enhancing partand the high signal intensity on the T2w-FLAIR For the grade IV glioma inFigure 14 some filters such as filter 26 also seem to focus on the necrotic partof the tumor
28
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 11 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
29
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 12 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
30
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 13 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-06-0238 This is an IDH wildtype grade IV glioma
31
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 14 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-06-0238 This is an IDH wildtype grade IV glioma
32
E Training losses
During the training of the network we used masked categorical cross-entropy lossfor the IDH 1p19q and grade outputs The normal categorical cross-entropyloss is defined as
LCEbatch = minus 1
Nbatch
sumj
sumiisinC
yij log (yij) (1)
where LCEbatch is the total cross-entropy loss over a batch yij is the ground truth
label of sample j for class i yij is the prediction score for sample j for class iC is the set of classes and Nbatch is the number of samples in the batch Here itis assumed that the ground truth labels are one-hot-encoded thus yij is either0 or 1 for each class In our case the ground truth is not known for all sampleswhich can be incorporated in Equation (1) by setting yij to 0 for all classesfor a sample for which the ground truth is not known That sample wouldthen not contribute to the overall loss and would not contribute to the gradientupdate However this can skew the total loss over a batch since the loss is stillaveraged over the total number of samples in a batch regardless of whether theground truth is known resulting in a lower loss for batches that contained moresamples with unknown ground truth Therefore we used a masked categoricalcross-entropy loss
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
yij log (yij) (2)
where
microbatchj =
Nbatchsumij yij
sumi
yij (3)
is the batch weight for sample j In this way the total batch loss is only averagedover the samples that actually have a ground truth
Since there was an imbalance between the number of ground truth samplesfor each class we used class weights to compensate for this imbalance Thusthe loss becomes
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
microclassi yij log (yij) (4)
where
microclassi =
N
Ni |C|(5)
is the class weight for class i N is the total number of samples with knownground truth Ni is the number of samples of class i and |C| is the number ofclasses By determining the class weight in this way we ensured that
microclassi Ni =
N
|C|= constant (6)
33
Thus each class would have the same contribution to the overall loss Theseclass weights were (individually) determined for the IDH output the 1p19qoutput and the grade output
For the segmentation output we used the DICE loss
LDICEbatch =
sumj
1minus 2 middotsumvoxels
k yjk middot yjksumvoxelsk yjk + yjk
(7)
where yjk is the ground truth label in voxel k of sample j and yjk is theprediction score outputted for voxel k of sample j
The total loss that was optimized for the model was a weighted sum of thefour individual losses
Ltotal =summ
micromLm (8)
with
microm =1
Xm (9)
where Lm is the loss for output m microm is the loss weight for loss m (either theIDH 1p19q grade or segmentation loss) and Xm is the number of sampleswith known ground truth for output m In this way we could counteract theeffect of certain outputs having more known labels than other outputs
34
F Parameter tuning
Table 8 Hyperparameters that were tuned and the values that were testedValues in bold show the selected values used in the final model
Tuning parameter Tested values
Dropout rate 015 02 025 030 035 040l2-norm 00001 000001 0000001Learning rate 001 0001 00001 000001 00000001Weight decay 0001 00001 000001Augmentation factor 1 2 3Augmentation probability 025 030 035 040 045
35
G Evaluation metrics
We calculated the AUC accuracy sensitivity and specificity metrics for thegenetic and histological features for the definitions of these metrics see [67]
For the IDH and 1p19q co-deletion outputs the IDH mutated and the1p19q co-deleted samples were regarded as the positive class respectively Sincethe grade was a multi-class problem no single positive class could be determinedFor the prediction of the individual grades that grade was seen as the positiveclass and all other grades as the negative class (eg in the case of the gradeIII prediction grade III was regarded as the positive class and grade II and IVwere regarded as the negative class) For the LGG vs HGG prediction LGGwas considered as the positive class and HGG as the negative class For theevaluation of these metrics for the genetic and histological features only thesubjects with known ground truth were taken into account
The overall AUC for the grade was a multi-class AUC determined in a one-vs-one approach comparing each class against the others in this way thismetric was insensitive to class imbalance [68] A multi-class accuracy was usedto determine the overall accuracy for the grade predictions [67]
To evaluate the performance of the automated segmentation we evaluatedthe DICE score the Hausdorff distance and the volumetric similarity coefficientThe DICE score is a measure of overlap between two segmentations where avalue of 1 indicates perfect overlap and the Hausdorff distance is a measureof the closeness of the borders of the segmentations The volumetric similaritycoefficient is a measure of the agreement between the volumes of two segmen-tations without taking account the actual location of the tumor where a valueof 1 indicates perfect agreement See [65] for the definitions of these metrics
36
H Ground truth labels of patients included frompublic datasets
Acknowledgments
Sebastian van der Voort and Fatih Incekara acknowledge funding by the DutchCancer Society (KWF project number EMCR 2015-7859)
Data used in this publication were generated by the National Cancer Insti-tute Clinical Proteomic Tumor Analysis Consortium (CPTAC)
The results published here are in whole or part based upon data generatedby the TCGA Research Network httpcancergenomenihgov
Author contributions
SRvdV FI WJN MS and SK contrived the study and designed theexperiments FI MMJW JWS RNT GJL PCDWH RSEAJPEV MJvdB and MS included patients in the different studiesSRvdV FI MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV MJvdB and MS collected the dataSRvdV carried out the experiments SRvdV FI MS and SK inter-preted the results SRvdV FI MJvdB MS and SK created the initialdraft of the paper MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV WJN and MJvdB revised the paper
References
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[2] Hendrikus J Dubbink Peggy N Atmodimedjo Johan M Kros Pim JFrench Marc Sanson Ahmed Idbaih Pieter Wesseling Roelien EntingWim Spliet Cees Tijssen Winand N M Dinjens Thierry Gorlia andMartin J van den Bent Molecular classification of anaplastic oligoden-droglioma using next-generation sequencing a report of the prospectiverandomized EORTC brain tumor group 26951 phase III trial Neuro-Oncology 18(3)388ndash400 March 2016 ISSN 1522-8517 URL https
doiorg101093neuoncnov182
[3] Jeanette E Eckel-Passow Daniel H Lachance Annette M Molinaro Kyle MWalsh Paul A Decker Hugues Sicotte Melike Pekmezci Terri Rice Matt LKosel Ivan V Smirnov Gobinda Sarkar Alissa A Caron Thomas M
37
Kollmeyer Corinne E Praska Anisha R Chada Chandralekha Halder He-len M Hansen Lucie S McCoy Paige M Bracci Roxanne Marshall ShichunZheng Gerald F Reis Alexander R Pico Brian P OrsquoNeill Jan C BucknerCaterina Giannini Jason T Huse Arie Perry Tarik Tihan Mitchell SBerger Susan M Chang Michael D Prados Joseph Wiemels John KWiencke Margaret R Wrensch and Robert B Jenkins Glioma groupsbased on 1p19q IDH and TERT promoter mutations in tumors NewEngland Journal of Medicine 372(26)2499ndash2508 June 2015 ISSN 0028-4793 URL httpsdoiorg101056NEJMoa1407279
[4] David N Louis Arie Perry Guido Reifenberger Andreas von Deimling Do-minique Figarella-Branger Webster K Cavenee Hiroko Ohgaki Otmar DWiestler Paul Kleihues and David W Ellison The 2016 world health orga-nization classification of tumors of the central nervous system a summaryActa Neuropathologica 131(6)803ndash820 June 2016 ISSN 0001-6322 URLhttpsdoiorg101007s00401-016-1545-1
[5] Ching-Chang Chen Peng-Wei Hsu Tai-Wei Erich Wu Shih-Tseng LeeChen-Nen Chang Kuo-chen Wei Chih-Cheng Chuang Chieh-Tsai WuTai-Ngar Lui Yung-Hsin Hsu Tzu-Kang Lin Sai-Cheung Lee and Yin-Cheng Huang Stereotactic brain biopsy Single center retrospective anal-ysis of complications Clinical Neurology and Neurosurgery 111(10)835ndash839 December 2009 ISSN 0303-8467 URL httpsdoiorg101016
jclineuro200908013
[6] Robert J Jackson Gregory N Fuller Dima Abi-Said Frederick F LangZiya L Gokaslan Wei Ming Shi David M Wildrick and Raymond SawayaLimitations of stereotactic biopsy in the initial management of gliomasNeuro-Oncology 3(3)193ndash200 July 2001 ISSN 1522-8517 URL https
doiorg101093neuonc33193
[7] M Zhou J Scott B Chaudhury L Hall D Goldgof KW Yeom M IvY Ou J Kalpathy-Cramer S Napel R Gillies O Gevaert and R GatenbyRadiomics in brain tumor Image assessment quantitative feature de-scriptors and machine-learning approaches American Journal of Neu-roradiology 39(2)208ndash216 February 2018 ISSN 0195-6108 URL https
doiorg103174ajnrA5391
[8] Wenya Linda Bi Ahmed Hosny Matthew B Schabath Maryellen LGiger Nicolai J Birkbak Alireza Mehrtash Tavis Allison Omar ArnaoutChristopher Abbosh Ian F Dunn Raymond H Mak Rulla M TamimiClare M Tempany Charles Swanton Udo Hoffmann Lawrence H SchwartzRobert J Gillies Raymond Y Huang and Hugo J W L Aerts Artificialintelligence in cancer imaging Clinical challenges and applications CAA Cancer Journal for Clinicians 69(2)caac21552 February 2019 ISSN0007-9235 URL httpsdoiorg103322caac21552
38
[9] Ahmad Chaddad Michael Jonathan Kucharczyk Paul Daniel SihamSabri Bertrand J Jean-Claude Tamim Niazi and Bassam AbdulkarimRadiomics in glioblastoma Current status and challenges facing clinicalimplementation Frontiers in Oncology 9374ndash374 May 2019 ISSN 2234-943X URL httpsdoiorg103389fonc201900374
[10] Marion Smits Imaging of oligodendroglioma The British Journal of Ra-diology 89(1060)20150857 April 2016 ISSN 0007-1285 URL https
doiorg101259bjr20150857
[11] Rachel L Delfanti David E Piccioni Jason Handwerker Naeim BahramiAnithaPriya Krishnan Roshan Karunamuni Jona A Hattangadi-GluthTyler M Seibert Ashwin Srikant Karra A Jones Vivian S Snyder An-ders M Dale Nathan S White Carrie R McDonald and Nikdokht FaridImaging correlates for the 2016 update on WHO classification of gradeIIIII gliomas implications for IDH 1p19q and ATRX status Journal ofNeuro-Oncology 135(3)601ndash609 December 2017 ISSN 0167-594X URLhttpsdoiorg101007s11060-017-2613-7
[12] Sonal Gore Tanay Chougule Jayant Jagtap Jitender Saini and MadhuraIngalhalikar A review of radiomics and deep predictive modeling in gliomacharacterization Academic Radiology July 2020 ISSN 1076-6332 URLhttpsdoiorg101016jacra202006016
[13] Hugo J W L Aerts Emmanuel Rios Velazquez Ralph T H LeijenaarChintan Parmar Patrick Grossmann Sara Carvalho Johan BussinkRene Monshouwer Benjamin Haibe-Kains Derek Rietveld Frank HoebersMichelle M Rietbergen C Rene Leemans Andre Dekker John Quacken-bush Robert J Gillies and Philippe Lambin Decoding tumour pheno-type by noninvasive imaging using a quantitative radiomics approach Na-ture Communications 5(1)4006 September 2014 ISSN 2041-1723 URLhttpsdoiorg101038ncomms5006
[14] Sarah Chihati and Djamel Gaceb A review of recent progress in deeplearning-based methods for MRI brain tumor segmentation In 202011th International Conference on Information and Communication Sys-tems (ICICS) pages 149ndash154 Institute of Electrical and Electronics Engi-neers (IEEE) April 2020 ISBN 9781728162270 URL httpsdoiorg
101109icics494692020239550
[15] Robert J Gillies Paul E Kinahan and Hedvig Hricak Radiomics Imagesare more than pictures they are data Radiology 278(2)563ndash577 Febru-ary 2016 ISSN 0033-8419 URL httpsdoiorg101148radiol
2015151169
[16] James H Thrall Xiang Li Quanzheng Li Cinthia Cruz Synho Do KeithDreyer and James Brink Artificial intelligence and machine learning in ra-diology Opportunities challenges pitfalls and criteria for success Journal
39
of the American College of Radiology 15(3 Part B)504ndash508 March 2018ISSN 1546-1440 URL httpsdoiorg101016jjacr201712026
[17] Ahmed Hosny Chintan Parmar John Quackenbush Lawrence H Schwartzand Hugo J W L Aerts Artificial intelligence in radiology Nature ReviewsCancer 18(8)500ndash510 August 2018 ISSN 1474-175X URL https
doiorg101038s41568-018-0016-5
[18] Okan Kopuklu Neslihan Kose Ahmet Gunduz and Gerhard Rigoll Re-source efficient 3D convolutional neural networks In 2019 IEEECVFInternational Conference on Computer Vision Workshop (ICCVW) Insti-tute of Electrical and Electronics Engineers (IEEE) October 2019 ISBN9781728150239 URL httpsdoiorg101109iccvw201900240
[19] S C Thust S Heiland A Falini H R Jager A D Waldman P C SundgrenC Godi V K Katsaros A Ramos N Bargallo M W Vernooij T YousryM Bendszus and M Smits Glioma imaging in europe A survey of 220centres and recommendations for best clinical practice European Radiology28(8)3306ndash3317 August 2018 ISSN 0938-7994 URL httpsdoiorg
101007s00330-018-5314-5
[20] Christian F Freyschlag Sandro M Krieg Johannes Kerschbaumer DanielPinggera Marie-Therese Forster Dominik Cordier Marco Rossi GabrieleMiceli Alexandre Roux Andres Reyes Silvio Sarubbo Anja Smits JoannaSierpowska Pierre A Robe Geert-Jan Rutten Thomas Santarius TomaszMatys Marc Zanello Fabien Almairac Lydiane Mondot Asgeir S JakolaMaria Zetterling Adria Rofes Gord von Campe Remy Guillevin DanieleBagatto Vincent Lubrano Marion Rapp John Goodden Philip C De WittHamer Johan Pallud Lorenzo Bello Claudius Thome Hugues Duffauand Emmanuel Mandonnet Imaging practice in low-grade gliomas amongeuropean specialized centers and proposal for a minimum core of imagingJournal of Neuro-Oncology 139(3)699ndash711 September 2018 ISSN 0167-594X URL httpsdoiorg101007s11060-018-2916-3
[21] M Labussiere A Idbaih X-W Wang Y Marie B Boisselier C FaletS Paris J Laffaire C Carpentier E Criniere F Ducray S El HallaniK Mokhtari K Hoang-Xuan J-Y Delattre and M Sanson All the 1p19qcodeleted gliomas are mutated on IDH1 or IDH2 Neurology 74(23)1886ndash1890 June 2010 ISSN 0028-3878 URL httpsdoiorg101212WNL
0b013e3181e1cf3a
[22] David N Louis Pieter Wesseling Kenneth Aldape Daniel J Brat DavidCapper Ian A Cree Charles Eberhart Dominique Figarella-BrangerMaryam Fouladi Gregory N Fuller Caterina Giannini Christine Haber-ler Cynthia Hawkins Takashi Komori Johan M Kros HK Ng Brent AOrr Sung-Hye Park Werner Paulus Arie Perry Torsten Pietsch GuidoReifenberger Marc Rosenblum Brian Rous Felix Sahm Chitra SarkarDavid A Solomon Uri Tabori Martin J Bent Andreas Deimling Michael
40
Weller Valerie A White and David W Ellison cIMPACT-NOW up-date 6 new entity and diagnostic principle recommendations of thecIMPACT-Utrecht meeting on future CNS tumor classification and grad-ing Brain Pathology 30(4)844ndash856 July 2020 ISSN 1015-6305 URLhttpsdoiorg101111bpa12832
[23] Zhenyu Tang Yuyun Xu Zhicheng Jiao Junfeng Lu Lei Jin Abudumi-jiti Aibaidula Jinsong Wu Qian Wang Han Zhang and Dinggang ShenPre-operative overall survival time prediction for glioblastoma patients us-ing deep learning on both imaging phenotype and genotype In Ding-gang Shen Tianming Liu Terry M Peters Lawrence H Staib CarolineEssert Sean Zhou Pew-Thian Yap and Ali Khan editors Lecture Notesin Computer Science pages 415ndash422 Springer Science and Business Me-dia LLC 2019 ISBN 9783030322380 URL httpsdoiorg101007
978-3-030-32239-7_46
[24] Zhiyuan Xue Bowen Xin Dingqian Wang and Xiuying Wang Radiomics-enhanced multi-task neural network for non-invasive glioma subtypingand segmentation In Hassan Mohy-ud Din and Saima Rathore editorsRadiomics and Radiogenomics in Neuro-oncology pages 81ndash90 SpringerScience and Business Media LLC 2020 ISBN 9783030401238 URLhttpsdoiorg101007978-3-030-40124-5_9
[25] Milan Decuyper Stijn Bonte Karel Deblaere and Roel Van Holen Au-tomated MRI based pipeline for glioma segmentation and prediction ofgrade IDH mutation and 1p19q co-deletion 2020 Preprint at https
arxivorgabs200511965
[26] Stefania Rizzo Francesca Botta Sara Raimondi Daniela Origgi Cris-tiana Fanciullo Alessio Giuseppe Morganti and Massimo Bellomi Ra-diomics the facts and the challenges of image analysis European Ra-diology Experimental 2(1)36 December 2018 ISSN 2509-9280 URLhttpsdoiorg101186s41747-018-0068-z
[27] Philipp Lohmann Norbert Galldiks Martin Kocher Alexander HeinzelChristian P Filss Carina Stegmayr Felix M Mottaghy Gereon R FinkN Jon Shah and Karl-Josef Langen Radiomics in neuro-oncology Basicsworkflow and applications Methods June 2020 ISSN 1046-2023 URLhttpsdoiorg101016jymeth202006003
[28] Stephen S F Yip and Hugo J W L Aerts Applications and limitationsof radiomics Physics in Medicine and Biology 61(13)R150ndashR166 July2016 ISSN 0031-9155 URL httpsdoiorg1010880031-915561
13r150
[29] Annika Malmstrom Ma lgorzata Lysiak Bjarne Winther Kristensen Eliz-abeth Hovey Roger Henriksson and Peter Soderkvist Do we really knowwho has an MGMT methylated glioma Results of an international survey
41
regarding use of MGMT analyses for glioma Neuro-Oncology Practice 7(1)68ndash76 09 2019 ISSN 2054-2577 URL httpsdoiorg101093
nopnpz039
[30] Takahiro Sasaki Manabu Kinoshita Koji Fujita Junya Fukai NobuhideHayashi Yuji Uematsu Yoshiko Okita Masahiro Nonaka Shusuke Mo-riuchi Takehiro Uda Naohiro Tsuyuguchi Hideyuki Arita Kanji MoriKenichi Ishibashi Koji Takano Namiko Nishida Tomoko Shofuda EmaYoshioka Daisuke Kanematsu Yoshinori Kodama Masayuki ManoNaoyuki Nakao and Yonehiro Kanemura Radiomics and MGMT pro-moter methylation for prognostication of newly diagnosed glioblastomaScientific Reports 9(1)14435 December 2019 ISSN 2045-2322 URLhttpsdoiorg101038s41598-019-50849-y
[31] A Gupta A Prager RJ Young W Shi AMP Omuro and JJ GraberDiffusion-weighted MR imaging and MGMT methylation status in glioblas-toma A reappraisal of the role of preoperative quantitative ADC measure-ments American Journal of Neuroradiology 34(1)E10ndashE11 January 2013ISSN 0195-6108 URL httpsdoiorg103174ajnrA3467
[32] JA Carrillo A Lai PL Nghiemphu HJ Kim HS Phillips S KharbandaP Moftakhar S Lalaezari W Yong BM Ellingson TF Cloughesy andWB Pope Relationship between tumor enhancement edema IDH1 muta-tional status MGMT promoter methylation and survival in glioblastomaAmerican Journal of Neuroradiology 33(7)1349ndash1355 August 2012 ISSN0195-6108 URL httpsdoiorg103174ajnrA2950
[33] Vilde Elisabeth Mikkelsen Hong Yan Dai Anne Line Stensjoslashen Erik Mag-nus Berntsen Oslashyvind Salvesen Ole Solheim and Sverre Helge TorpMGMT promoter methylation status is not related to histological or radio-logical features in IDH wild-type glioblastomas Journal of Neuropathologyamp Experimental Neurology 79(8)855ndash862 August 2020 ISSN 0022-3069URL httpsdoiorg101093jnennlaa060
[34] Martin J van den Bent Interobserver variation of the histopathologicaldiagnosis in clinical trials on glioma a clinicianrsquos perspective Acta Neu-ropathologica 120(3)297ndash304 September 2010 ISSN 1432-0533 URLhttpsdoiorg101007s00401-010-0725-7
[35] Ji Eun Park Ho Sung Kim Youngheun Jo Roh-Eul Yoo Seung Hong ChoiSoo Jung Nam and Jeong Hoon Kim Radiomics prognostication modelin glioblastoma using diffusion- and perfusion-weighted MRI ScientificReports 10(1)4250 December 2020 ISSN 2045-2322 URL httpsdoi
org101038s41598-020-61178-w
[36] Minjae Kim So Yeong Jung Ji Eun Park Yeongheun Jo Seo YoungPark Soo Jung Nam Jeong Hoon Kim and Ho Sung Kim Diffusion-and perfusion-weighted MRI radiomics model may predict isocitrate dehy-drogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade
42
glioma European Radiology 30(4)2142ndash2151 April 2020 ISSN 0938-7994URL httpsdoiorg101007s00330-019-06548-3
[37] M Visser DMJ Muller RJM van Duijn M Smits N Verburg EJ HendriksRJA Nabuurs JCJ Bot RS Eijgelaar M Witte MB van Herk F BarkhofPC de WittHamer and JC de Munck Inter-rater agreement in glioma seg-mentations on longitudinal MRI NeuroImage Clinical 22101727 2019ISSN 2213-1582 URL httpsdoiorg101016jnicl2019101727
[38] Kenneth Clark Bruce Vendt Kirk Smith John Freymann Justin KirbyPaul Koppel Stephen Moore Stanley Phillips David Maffitt MichaelPringle Lawrence Tarbox and Fred Prior The cancer imaging archive(TCIA) Maintaining and operating a public information repository Jour-nal of Digital Imaging 26(6)1045ndash1057 December 2013 ISSN 0897-1889URL httpsdoiorg101007s10278-013-9622-7
[39] Lisa Scarpace Adam E Flanders Rajan Jain Tom Mikkelsen and David WAndrews Data from REMBRANDT The Cancer Imaging Archive 2015URL httpsdoiorg107937K9TCIA2015588OZUZB
[40] National Cancer Institute Clinical Proteomic Tumor Analysis Consortium(CPTAC) Radiology data from the clinical proteomic tumor analysisconsortium glioblastoma multiforme CPTAC-GBM collection The CancerImaging Archive 2018 URL httpsdoiorg107937k9tcia2018
3rje41q1
[41] Nameeta Shah Xu Feng Michael Lankerovich Ralph B Puchalski andBart Keogh Data from Ivy GAP The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016XLwaN6nL
[42] Ralph B Puchalski Nameeta Shah Jeremy Miller Rachel Dalley Steve RNomura Jae-Guen Yoon Kimberly A Smith Michael Lankerovich DarrenBertagnolli Kris Bickley Andrew F Boe Krissy Brouner Stephanie But-ler Shiella Caldejon Mike Chapin Suvro Datta Nick Dee Tsega DestaTim Dolbeare Nadezhda Dotson Amanda Ebbert David Feng Xu FengMichael Fisher Garrett Gee Jeff Goldy Lindsey Gourley Benjamin WGregor Guangyu Gu Nika Hejazinia John Hohmann Parvinder HothiRobert Howard Kevin Joines Ali Kriedberg Leonard Kuan Chris LauFelix Lee Hwahyung Lee Tracy Lemon Fuhui Long Naveed Mastan ErikaMott Chantal Murthy Kiet Ngo Eric Olson Melissa Reding Zack RileyDavid Rosen David Sandman Nadiya Shapovalova Clifford R Slaughter-beck Andrew Sodt Graham Stockdale Aaron Szafer Wayne WakemanPaul E Wohnoutka Steven J White Don Marsh Robert C RostomilyLydia Ng Chinh Dang Allan Jones Bart Keogh Haley R GittlemanJill S Barnholtz-Sloan Patrick J Cimino Megha S Uppin C Dirk KeeneFarrokh R Farrokhi Justin D Lathia Michael E Berens Antonio IavaroneAmy Bernard Ed Lein John W Phillips Steven W Rostad Charles Cobbs
43
Michael J Hawrylycz and Greg D Foltz An anatomic transcriptional at-las of human glioblastoma Science 360(6389)660ndash663 May 2018 ISSN0036-8075 URL httpsdoiorg101126scienceaaf2666
[43] Kathleen Schmainda and Melissa Prah Data from Brain-Tumor-Progression The Cancer Imaging Archive 2018 URL httpsdoiorg
107937K9TCIA201815quzvnb
[44] B H Menze A Jakab S Bauer J Kalpathy-Cramer K FarahaniJ Kirby Y Burren N Porz J Slotboom R Wiest L Lanczi E GerstnerM Weber T Arbel B B Avants N Ayache P Buendia D L CollinsN Cordier J J Corso A Criminisi T Das H Delingette C Demi-ralp C R Durst M Dojat S Doyle J Festa F Forbes E GeremiaB Glocker P Golland X Guo A Hamamci K M IftekharuddinR Jena N M John E Konukoglu D Lashkari J A Mariz R MeierS Pereira D Precup S J Price T R Raviv S M S Reza M RyanD Sarikaya L Schwartz H Shin J Shotton C A Silva N SousaN K Subbanna G Szekely T J Taylor O M Thomas N J Tusti-son G Unal F Vasseur M Wintermark D H Ye L Zhao B ZhaoD Zikic M Prastawa M Reyes and K Van Leemput The mul-timodal brain tumor image segmentation benchmark (BRATS) IEEETransactions on Medical Imaging 34(10)1993ndash2024 2015 URL https
doiorg101109TMI20142377694
[45] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin S Kirby John B Freymann Keyvan Farahani and ChristosDavatzikos Advancing the cancer genome atlas glioma MRI collectionswith expert segmentation labels and radiomic features Scientific Data 4(1)170117 December 2017 ISSN 2052-4463 URL httpsdoiorg10
1038sdata2017117
[46] Spyridon Bakas Mauricio Reyes Andras Jakab Stefan Bauer MarkusRempfler Alessandro Crimi Russell Takeshi Shinohara Christoph BergerSung Min Ha Martin Rozycki Marcel Prastawa Esther Alberts JanaLipkova John Freymann Justin Kirby Michel Bilello Hassan Fathallah-Shaykh Roland Wiest Jan Kirschke Benedikt Wiestler Rivka ColenAikaterini Kotrotsou Pamela Lamontagne Daniel Marcus MikhailMilchenko Arash Nazeri Marc-Andre Weber Abhishek Mahajan UjjwalBaid Elizabeth Gerstner Dongjin Kwon Gagan Acharya Manu Agar-wal Mahbubul Alam Alberto Albiol Antonio Albiol Francisco J AlbiolVarghese Alex Nigel Allinson Pedro H A Amorim Abhijit AmrutkarGanesh Anand Simon Andermatt Tal Arbel Pablo Arbelaez Aaron Av-ery Muneeza Azmat Pranjal B W Bai Subhashis Banerjee Bill BarthThomas Batchelder Kayhan Batmanghelich Enzo Battistella AndrewBeers Mikhail Belyaev Martin Bendszus Eze Benson Jose Bernal Ha-landur Nagaraja Bharath George Biros Sotirios Bisdas James BrownMariano Cabezas Shilei Cao Jorge M Cardoso Eric N Carver Adria
44
Casamitjana Laura Silvana Castillo Marcel Cata Philippe Cattin Al-bert Cerigues Vinicius S Chagas Siddhartha Chandra Yi-Ju ChangShiyu Chang Ken Chang Joseph Chazalon Shengcong Chen Wei ChenJefferson W Chen Zhaolin Chen Kun Cheng Ahana Roy ChoudhuryRoger Chylla Albert Clerigues Steven Colleman Ramiro German Ro-driguez Colmeiro Marc Combalia Anthony Costa Xiaomeng Cui Zhen-zhen Dai Lutao Dai Laura Alexandra Daza Eric Deutsch ChangxingDing Chao Dong Shidu Dong Wojciech Dudzik Zach Eaton-Rosen GaryEgan Guilherme Escudero Theo Estienne Richard Everson JonathanFabrizio Yong Fan Longwei Fang Xue Feng Enzo Ferrante Lucas Fi-don Martin Fischer Andrew P French Naomi Fridman Huan Fu DavidFuentes Yaozong Gao Evan Gates David Gering Amir Gholami WilliGierke Ben Glocker Mingming Gong Sandra Gonzalez-Villa T Gros-ges Yuanfang Guan Sheng Guo Sudeep Gupta Woo-Sup Han Il SongHan Konstantin Harmuth Huiguang He Aura Hernandez-Sabate EvelynHerrmann Naveen Himthani Winston Hsu Cheyu Hsu Xiaojun Hu Xi-aobin Hu Yan Hu Yifan Hu Rui Hua Teng-Yi Huang Weilin HuangSabine Van Huffel Quan Huo Vivek HV Khan M Iftekharuddin FabianIsensee Mobarakol Islam Aaron S Jackson Sachin R Jambawalikar An-drew Jesson Weijian Jian Peter Jin V Jeya Maria Jose Alain JungoB Kainz Konstantinos Kamnitsas Po-Yu Kao Ayush Karnawat ThomasKellermeier Adel Kermi Kurt Keutzer Mohamed Tarek Khadir Mahen-dra Khened Philipp Kickingereder Geena Kim Nik King Haley KnappUrspeter Knecht Lisa Kohli Deren Kong Xiangmao Kong Simon Kop-pers Avinash Kori Ganapathy Krishnamurthi Egor Krivov Piyush Ku-mar Kaisar Kushibar Dmitrii Lachinov Tryphon Lambrou Joon LeeChengen Lee Yuehchou Lee M Lee Szidonia Lefkovits Laszlo LefkovitsJames Levitt Tengfei Li Hongwei Li Wenqi Li Hongyang Li XiaochuanLi Yuexiang Li Heng Li Zhenye Li Xiaoyu Li Zeju Li XiaoGang LiWenqi Li Zheng-Shen Lin Fengming Lin Pietro Lio Chang Liu Bo-qiang Liu Xiang Liu Mingyuan Liu Ju Liu Luyan Liu Xavier LladoMarc Moreno Lopez Pablo Ribalta Lorenzo Zhentai Lu Lin Luo ZhigangLuo Jun Ma Kai Ma Thomas Mackie Anant Madabushi Issam Mah-moudi Klaus H Maier-Hein Pradipta Maji CP Mammen Andreas MangB S Manjunath Michal Marcinkiewicz S McDonagh Stephen McKennaRichard McKinley Miriam Mehl Sachin Mehta Raghav Mehta RaphaelMeier Christoph Meinel Dorit Merhof Craig Meyer Robert Miller Sush-mita Mitra Aliasgar Moiyadi David Molina-Garcia Miguel A B Mon-teiro Grzegorz Mrukwa Andriy Myronenko Jakub Nalepa Thuyen NgoDong Nie Holly Ning Chen Niu Nicholas K Nuechterlein Eric Oer-mann Arlindo Oliveira Diego D C Oliveira Arnau Oliver AlexanderF I Osman Yu-Nian Ou Sebastien Ourselin Nikos Paragios Moo SungPark Brad Paschke J Gregory Pauloski Kamlesh Pawar Nick PawlowskiLinmin Pei Suting Peng Silvio M Pereira Julian Perez-Beteta Vic-tor M Perez-Garcia Simon Pezold Bao Pham Ashish Phophalia GemmaPiella G N Pillai Marie Piraud Maxim Pisov Anmol Popli Michael P
45
Pound Reza Pourreza Prateek Prasanna Vesna Prkovska Tony P Prid-more Santi Puch Elodie Puybareau Buyue Qian Xu Qiao MartinRajchl Swapnil Rane Michael Rebsamen Hongliang Ren Xuhua RenKarthik Revanuru Mina Rezaei Oliver Rippel Luis Carlos Rivera Char-lotte Robert Bruce Rosen Daniel Rueckert Mohammed Safwan MostafaSalem Joaquim Salvi Irina Sanchez Irina Sanchez Heitor M Santos Em-mett Sartor Dawid Schellingerhout Klaudius Scheufele Matthew R ScottArtur A Scussel Sara Sedlar Juan Pablo Serrano-Rubio N Jon ShahNameetha Shah Mazhar Shaikh B Uma Shankar Zeina Shboul HaipengShen Dinggang Shen Linlin Shen Haocheng Shen Varun Shenoy FengShi Hyung Eun Shin Hai Shu Diana Sima M Sinclair Orjan SmedbyJames M Snyder Mohammadreza Soltaninejad Guidong Song MehulSoni Jean Stawiaski Shashank Subramanian Li Sun Roger Sun JiaweiSun Kay Sun Yu Sun Guoxia Sun Shuang Sun Yannick R Suter Las-zlo Szilagyi Sanjay Talbar Dacheng Tao Dacheng Tao Zhongzhao TengSiddhesh Thakur Meenakshi H Thakur Sameer Tharakan Pallavi Ti-wari Guillaume Tochon Tuan Tran Yuhsiang M Tsai Kuan-Lun TsengTran Anh Tuan Vadim Turlapov Nicholas Tustison Maria VakalopoulouSergi Valverde Rami Vanguri Evgeny Vasiliev Jonathan Ventura LuisVera Tom Vercauteren C A Verrastro Lasitha Vidyaratne Veronica Vi-laplana Ajeet Vivekanandan Guotai Wang Qian Wang Chiatse J WangWeichung Wang Duo Wang Ruixuan Wang Yuanyuan Wang ChunliangWang Guotai Wang Ning Wen Xin Wen Leon Weninger Wolfgang WickShaocheng Wu Qiang Wu Yihong Wu Yong Xia Yanwu Xu XiaowenXu Peiyuan Xu Tsai-Ling Yang Xiaoping Yang Hao-Yu Yang JunlinYang Haojin Yang Guang Yang Hongdou Yao Xujiong Ye ChangchangYin Brett Young-Moxon Jinhua Yu Xiangyu Yue Songtao Zhang AngelaZhang Kun Zhang Xuejie Zhang Lichi Zhang Xiaoyue Zhang YazhuoZhang Lei Zhang Jianguo Zhang Xiang Zhang Tianhao Zhang SichengZhao Yu Zhao Xiaomei Zhao Liang Zhao Yefeng Zheng Liming ZhongChenhong Zhou Xiaobing Zhou Fan Zhou Hongtu Zhu Jin Zhu YingZhuge Weiwei Zong Jayashree Kalpathy-Cramer Keyvan Farahani Chris-tos Davatzikos Koen van Leemput and Bjoern Menze Identifying the bestmachine learning algorithms for brain tumor segmentation progression as-sessment and overall survival prediction in the BRATS challenge 2018Preprint at httpsarxivorgabs181102629
[47] Nancy Pedano Adam E Flanders Lisa Scarpace Tom Mikkelsen Jen-nifer M Eschbacher Beth Hermes Victor Sisneros Jill Barnholtz-Sloanand Quinn Ostrom Radiology data from the cancer genome atlas lowgrade glioma [TCGA-LGG] collection The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016L4LTD3TK
[48] Lisa Scarpace Tom Mikkelsen Soonmee Cha Sujaya Rao SangeetaTekchandani David Gutman Joel H Saltz Bradley J Erickson NancyPedano Adam E Flanders Jill Barnholtz-Sloan Quinn Ostrom DanielBarboriak and Laura J Pierce Radiology data from the cancer genome
46
atlas glioblastoma multiforme [TCGA-GBM] collection The Cancer Imag-ing Archive 2016 URL httpsdoiorg107937K9TCIA2016
RNYFUYE9
[49] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin Kirby John Freymann Keyvan Farahani and ChristosDavatzikos Segmentation labels and radiomic features for the pre-operativescans of the TCGA-LGG collection [data set] The Cancer Imaging Archive2017 URL httpsdoiorg107937K9TCIA2017GJQ7R0EF
[50] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello Mar-tin Rozycki Justin Kirby John Freymann Keyvan Farahani and Chris-tos Davatzikos Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [data set] The CancerImaging Archive 2017 URL httpsdoiorg107937K9TCIA2017
KLXWJJ1Q
[51] Xiangrui Li Paul S Morgan John Ashburner Jolinda Smith and Christo-pher Rorden The first step for neuroimaging data analysis DICOM toNIfTI conversion Journal of Neuroscience Methods 26447ndash56 May 2016ISSN 0165-0270 URL httpsdoiorg101016jjneumeth201603
001
[52] Vladimir Fonov Alan C Evans Kelly Botteron C Robert Almli Robert CMcKinstry and D Louis Collins Unbiased average age-appropriate at-lases for pediatric studies NeuroImage 54(1)313ndash327 January 2011ISSN 1053-8119 URL httpsdoiorg101016jneuroimage2010
07033
[53] VS Fonov AC Evans RC McKinstry CR Almli and DL Collins Unbiasednonlinear average age-appropriate brain templates from birth to adulthoodNeuroImage 47S102 July 2009 ISSN 1053-8119 URL httpsdoi
org101016S1053-8119(09)70884-5 Organization for Human BrainMapping 2009 Annual Meeting
[54] S Klein M Staring K Murphy MA Viergever and J Pluim elastix Atoolbox for intensity-based medical image registration IEEE Transactionson Medical Imaging 29(1)196ndash205 January 2010 ISSN 0278-0062 URLhttpsdoiorg101109TMI20092035616
[55] Denis Shamonin Fast parallel image registration on CPU and GPU fordiagnostic classification of alzheimerrsquos disease Frontiers in Neuroinfor-matics 750 2013 ISSN 1662-5196 URL httpsdoiorg103389
fninf201300050
[56] Bradley C Lowekamp David T Chen Luis Ibanez and Daniel Blezek Thedesign of SimpleITK Frontiers in Neuroinformatics 745 2013 ISSN1662-5196 URL httpsdoiorg103389fninf201300045
47
[57] Fabian Isensee Marianne Schell Irada Pflueger Gianluca Brugnara DavidBonekamp Ulf Neuberger Antje Wick Heinz-Peter Schlemmer SabineHeiland Wolfgang Wick Martin Bendszus Klaus H Maier-Hein andPhilipp Kickingereder Automated brain extraction of multisequence MRIusing artificial neural networks Human Brain Mapping 40(17)4952ndash4964December 2019 ISSN 1065-9471 URL httpsdoiorg101002hbm
24750
[58] Olaf Ronneberger Invited talk U-net convolutional networks for biomed-ical image segmentation In geb Fritzsche K Maier-Hein geb Lehmann TDeserno Heinz Handels and Thomas Tolxdorff editors Bildverarbeitungfur die Medizin 2017 Informatik aktuell pages 3ndash3 Springer Scienceand Business Media LLC 2017 ISBN 9783662543443 URL https
doiorg101007978-3-662-54345-0_3
[59] Martın Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy DavisJeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving MichaelIsard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry MooreDerek G Murray Benoit Steiner Paul Tucker Vijay Vasudevan PeteWarden Martin Wicke Yuan Yu and Xiaoqiang Zheng Tensor-Flow A system for large-scale machine learning In 12th USENIXSymposium on Operating Systems Design and Implementation (OSDI16) pages 265ndash283 USENIX Association November 2016 ISBN978-1-931971-33-1 URL httpswwwusenixorgconferenceosdi16
technical-sessionspresentationabadi
[60] Dipankar Das Naveen Mellempudi Dheevatsa Mudigere Dhiraj KalamkarSasikanth Avancha Kunal Banerjee Srinivas Sridharan KarthikVaidyanathan Bharat Kaul Evangelos Georganas Alexander HeineckePradeep Dubey Jesus Corbal Nikita Shustrov Roma Dubtsov EvaristFomenko and Vadim Pirogov Mixed precision training of convolutionalneural networks using integer operations In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=H135uzZ0-
[61] Samuel L Smith Pieter-Jan Kindermans and Quoc V Le Donrsquot decaythe learning rate increase the batch size In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=B1Yy1BxCZ
[62] Cliff Woolley NCCL accelarated multi-GPU collective communicationsURL httpsimagesnvidiacomeventssc15pdfsNCCL-Woolley
pdf Accessed on 2020-09-30
[63] Ilya Loshchilov and Frank Hutter Decoupled weight decay regularization2017 Preprint at httpsarxivorgabs171105101
48
[64] F Pedregosa G Varoquaux A Gramfort V Michel B Thirion O GriselM Blondel P Prettenhofer R Weiss V Dubourg J Vanderplas A Pas-sos D Cournapeau M Brucher M Perrot and E Duchesnay Scikit-learn Machine learning in python Journal of Machine Learning Re-search 122825ndash2830 2011 URL httpswwwjmlrorgpapersv12
pedregosa11ahtml
[65] Abdel Aziz Taha and Allan Hanbury Metrics for evaluating 3d medicalimage segmentation analysis selection and tool BMC Medical Imaging15(1)29 August 2015 ISSN 1471-2342 URL httpsdoiorg101186
s12880-015-0068-x
[66] Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viegas and MartinWattenberg SmoothGrad removing noise by adding noise 2017 Preprintat httpsarxivorgabs170603825
[67] Alaa Tharwat Classification assessment methods Applied Computing andInformatics 2018 ISSN 2210-8327 URL httpsdoiorg101016j
aci201808003
[68] David J Hand and Robert J Till A simple generalisation of the areaunder the ROC curve for multiple class classification problems MachineLearning 45(2)171ndash186 November 2001 ISSN 1573-0565 URL https
doiorg101023A1010920819831
49
Data Collection | Patient | IDH_mutated | 1p19q_codeleted | Grade | |||||
BTumorP | PGBM-001 | -1 | -1 | -1 | |||||
BTumorP | PGBM-002 | -1 | -1 | -1 | |||||
BTumorP | PGBM-003 | -1 | -1 | -1 | |||||
BTumorP | PGBM-004 | -1 | -1 | -1 | |||||
BTumorP | PGBM-005 | -1 | -1 | -1 | |||||
BTumorP | PGBM-006 | -1 | -1 | -1 | |||||
BTumorP | PGBM-007 | -1 | -1 | -1 | |||||
BTumorP | PGBM-008 | -1 | -1 | -1 | |||||
BTumorP | PGBM-009 | -1 | -1 | -1 | |||||
BTumorP | PGBM-010 | -1 | -1 | -1 | |||||
BTumorP | PGBM-011 | -1 | -1 | -1 | |||||
BTumorP | PGBM-012 | -1 | -1 | -1 | |||||
BTumorP | PGBM-013 | -1 | -1 | -1 | |||||
BTumorP | PGBM-014 | -1 | -1 | -1 | |||||
BTumorP | PGBM-015 | -1 | -1 | -1 | |||||
BTumorP | PGBM-016 | -1 | -1 | -1 | |||||
BTumorP | PGBM-017 | -1 | -1 | -1 | |||||
BTumorP | PGBM-018 | -1 | -1 | -1 | |||||
BTumorP | PGBM-019 | -1 | -1 | -1 | |||||
BTumorP | PGBM-020 | -1 | -1 | -1 | |||||
BraTS | 2013_0 | -1 | -1 | -1 | |||||
BraTS | 2013_10 | -1 | -1 | -1 | |||||
BraTS | 2013_11 | -1 | -1 | -1 | |||||
BraTS | 2013_12 | -1 | -1 | -1 | |||||
BraTS | 2013_13 | -1 | -1 | -1 | |||||
BraTS | 2013_14 | -1 | -1 | -1 | |||||
BraTS | 2013_15 | -1 | -1 | -1 | |||||
BraTS | 2013_16 | -1 | -1 | -1 | |||||
BraTS | 2013_17 | -1 | -1 | -1 | |||||
BraTS | 2013_18 | -1 | -1 | -1 | |||||
BraTS | 2013_19 | -1 | -1 | -1 | |||||
BraTS | 2013_1 | -1 | -1 | -1 | |||||
BraTS | 2013_20 | -1 | -1 | -1 | |||||
BraTS | 2013_21 | -1 | -1 | -1 | |||||
BraTS | 2013_22 | -1 | -1 | -1 | |||||
BraTS | 2013_23 | -1 | -1 | -1 | |||||
BraTS | 2013_24 | -1 | -1 | -1 | |||||
BraTS | 2013_25 | -1 | -1 | -1 | |||||
BraTS | 2013_26 | -1 | -1 | -1 | |||||
BraTS | 2013_27 | -1 | -1 | -1 | |||||
BraTS | 2013_28 | -1 | -1 | -1 | |||||
BraTS | 2013_29 | -1 | -1 | -1 | |||||
BraTS | 2013_2 | -1 | -1 | -1 | |||||
BraTS | 2013_3 | -1 | -1 | -1 | |||||
BraTS | 2013_4 | -1 | -1 | -1 | |||||
BraTS | 2013_5 | -1 | -1 | -1 | |||||
BraTS | 2013_6 | -1 | -1 | -1 | |||||
BraTS | 2013_7 | -1 | -1 | -1 | |||||
BraTS | 2013_8 | -1 | -1 | -1 | |||||
BraTS | 2013_9 | -1 | -1 | -1 | |||||
BraTS | CBICA_AAB | -1 | -1 | -1 | |||||
BraTS | CBICA_AAG | -1 | -1 | -1 | |||||
BraTS | CBICA_AAL | -1 | -1 | -1 | |||||
BraTS | CBICA_AAP | -1 | -1 | -1 | |||||
BraTS | CBICA_ABB | -1 | -1 | -1 | |||||
BraTS | CBICA_ABE | -1 | -1 | -1 | |||||
BraTS | CBICA_ABM | -1 | -1 | -1 | |||||
BraTS | CBICA_ABN | -1 | -1 | -1 | |||||
BraTS | CBICA_ABO | -1 | -1 | -1 | |||||
BraTS | CBICA_ABY | -1 | -1 | -1 | |||||
BraTS | CBICA_ALN | -1 | -1 | -1 | |||||
BraTS | CBICA_ALU | -1 | -1 | -1 | |||||
BraTS | CBICA_ALX | -1 | -1 | -1 | |||||
BraTS | CBICA_AME | -1 | -1 | -1 | |||||
BraTS | CBICA_AMH | -1 | -1 | -1 | |||||
BraTS | CBICA_ANG | -1 | -1 | -1 | |||||
BraTS | CBICA_ANI | -1 | -1 | -1 | |||||
BraTS | CBICA_ANP | -1 | -1 | -1 | |||||
BraTS | CBICA_ANV | -1 | -1 | -1 | |||||
BraTS | CBICA_ANZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AOC | -1 | -1 | -1 | |||||
BraTS | CBICA_AOD | -1 | -1 | -1 | |||||
BraTS | CBICA_AOH | -1 | -1 | -1 | |||||
BraTS | CBICA_AOO | -1 | -1 | -1 | |||||
BraTS | CBICA_AOP | -1 | -1 | -1 | |||||
BraTS | CBICA_AOS | -1 | -1 | -1 | |||||
BraTS | CBICA_AOZ | -1 | -1 | -1 | |||||
BraTS | CBICA_APK | -1 | -1 | -1 | |||||
BraTS | CBICA_APR | -1 | -1 | -1 | |||||
BraTS | CBICA_APY | -1 | -1 | -1 | |||||
BraTS | CBICA_APZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQA | -1 | -1 | -1 | |||||
BraTS | CBICA_AQD | -1 | -1 | -1 | |||||
BraTS | CBICA_AQG | -1 | -1 | -1 | |||||
BraTS | CBICA_AQJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQN | -1 | -1 | -1 | |||||
BraTS | CBICA_AQO | -1 | -1 | -1 | |||||
BraTS | CBICA_AQP | -1 | -1 | -1 | |||||
BraTS | CBICA_AQQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQR | -1 | -1 | -1 | |||||
BraTS | CBICA_AQT | -1 | -1 | -1 | |||||
BraTS | CBICA_AQU | -1 | -1 | -1 | |||||
BraTS | CBICA_AQV | -1 | -1 | -1 | |||||
BraTS | CBICA_AQY | -1 | -1 | -1 | |||||
BraTS | CBICA_AQZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ARF | -1 | -1 | -1 | |||||
BraTS | CBICA_ARW | -1 | -1 | -1 | |||||
BraTS | CBICA_ARZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ASA | -1 | -1 | -1 | |||||
BraTS | CBICA_ASE | -1 | -1 | -1 | |||||
BraTS | CBICA_ASF | -1 | -1 | -1 | |||||
BraTS | CBICA_ASG | -1 | -1 | -1 | |||||
BraTS | CBICA_ASH | -1 | -1 | -1 | |||||
BraTS | CBICA_ASK | -1 | -1 | -1 | |||||
BraTS | CBICA_ASN | -1 | -1 | -1 | |||||
BraTS | CBICA_ASO | -1 | -1 | -1 | |||||
BraTS | CBICA_ASR | -1 | -1 | -1 | |||||
BraTS | CBICA_ASU | -1 | -1 | -1 | |||||
BraTS | CBICA_ASV | -1 | -1 | -1 | |||||
BraTS | CBICA_ASW | -1 | -1 | -1 | |||||
BraTS | CBICA_ASY | -1 | -1 | -1 | |||||
BraTS | CBICA_ATB | -1 | -1 | -1 | |||||
BraTS | CBICA_ATD | -1 | -1 | -1 | |||||
BraTS | CBICA_ATF | -1 | -1 | -1 | |||||
BraTS | CBICA_ATN | -1 | -1 | -1 | |||||
BraTS | CBICA_ATP | -1 | -1 | -1 | |||||
BraTS | CBICA_ATV | -1 | -1 | -1 | |||||
BraTS | CBICA_ATX | -1 | -1 | -1 | |||||
BraTS | CBICA_AUA | -1 | -1 | -1 | |||||
BraTS | CBICA_AUN | -1 | -1 | -1 | |||||
BraTS | CBICA_AUQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AUR | -1 | -1 | -1 | |||||
BraTS | CBICA_AUW | -1 | -1 | -1 | |||||
BraTS | CBICA_AUX | -1 | -1 | -1 | |||||
BraTS | CBICA_AVB | -1 | -1 | -1 | |||||
BraTS | CBICA_AVF | -1 | -1 | -1 | |||||
BraTS | CBICA_AVG | -1 | -1 | -1 | |||||
BraTS | CBICA_AVJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AVT | -1 | -1 | -1 | |||||
BraTS | CBICA_AVV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWG | -1 | -1 | -1 | |||||
BraTS | CBICA_AWH | -1 | -1 | -1 | |||||
BraTS | CBICA_AWI | -1 | -1 | -1 | |||||
BraTS | CBICA_AWV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWX | -1 | -1 | -1 | |||||
BraTS | CBICA_AXJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXL | -1 | -1 | -1 | |||||
BraTS | CBICA_AXM | -1 | -1 | -1 | |||||
BraTS | CBICA_AXN | -1 | -1 | -1 | |||||
BraTS | CBICA_AXO | -1 | -1 | -1 | |||||
BraTS | CBICA_AXQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXW | -1 | -1 | -1 | |||||
BraTS | CBICA_AYA | -1 | -1 | -1 | |||||
BraTS | CBICA_AYC | -1 | -1 | -1 | |||||
BraTS | CBICA_AYG | -1 | -1 | -1 | |||||
BraTS | CBICA_AYI | -1 | -1 | -1 | |||||
BraTS | CBICA_AYU | -1 | -1 | -1 | |||||
BraTS | CBICA_AYW | -1 | -1 | -1 | |||||
BraTS | CBICA_AZD | -1 | -1 | -1 | |||||
BraTS | CBICA_AZH | -1 | -1 | -1 | |||||
BraTS | CBICA_BAN | -1 | -1 | -1 | |||||
BraTS | CBICA_BAP | -1 | -1 | -1 | |||||
BraTS | CBICA_BAX | -1 | -1 | -1 | |||||
BraTS | CBICA_BBG | -1 | -1 | -1 | |||||
BraTS | CBICA_BCF | -1 | -1 | -1 | |||||
BraTS | CBICA_BCL | -1 | -1 | -1 | |||||
BraTS | CBICA_BDK | -1 | -1 | -1 | |||||
BraTS | CBICA_BEM | -1 | -1 | -1 | |||||
BraTS | CBICA_BFB | -1 | -1 | -1 | |||||
BraTS | CBICA_BFP | -1 | -1 | -1 | |||||
BraTS | CBICA_BGE | -1 | -1 | -1 | |||||
BraTS | CBICA_BGG | -1 | -1 | -1 | |||||
BraTS | CBICA_BGN | -1 | -1 | -1 | |||||
BraTS | CBICA_BGO | -1 | -1 | -1 | |||||
BraTS | CBICA_BGR | -1 | -1 | -1 | |||||
BraTS | CBICA_BGT | -1 | -1 | -1 | |||||
BraTS | CBICA_BGW | -1 | -1 | -1 | |||||
BraTS | CBICA_BGX | -1 | -1 | -1 | |||||
BraTS | CBICA_BHB | -1 | -1 | -1 | |||||
BraTS | CBICA_BHK | -1 | -1 | -1 | |||||
BraTS | CBICA_BHM | -1 | -1 | -1 | |||||
BraTS | CBICA_BHQ | -1 | -1 | -1 | |||||
BraTS | CBICA_BHV | -1 | -1 | -1 | |||||
BraTS | CBICA_BHZ | -1 | -1 | -1 | |||||
BraTS | CBICA_BIC | -1 | -1 | -1 | |||||
BraTS | CBICA_BJY | -1 | -1 | -1 | |||||
BraTS | CBICA_BKV | -1 | -1 | -1 | |||||
BraTS | CBICA_BLJ | -1 | -1 | -1 | |||||
BraTS | CBICA_BNR | -1 | -1 | -1 | |||||
BraTS | TMC_6290 | -1 | -1 | -1 | |||||
BraTS | TMC_6643 | -1 | -1 | -1 | |||||
BraTS | TMC_9043 | -1 | -1 | -1 | |||||
BraTS | TMC_11964 | -1 | -1 | -1 | |||||
BraTS | TMC_12866 | -1 | -1 | -1 | |||||
BraTS | TMC_15477 | -1 | -1 | -1 | |||||
BraTS | TMC_21360 | -1 | -1 | -1 | |||||
BraTS | TMC_27374 | -1 | -1 | -1 | |||||
BraTS | TMC_30014 | -1 | -1 | -1 | |||||
CPTAC-GBM | C3L-00016 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00019 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00265 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00278 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00349 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00424 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00429 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00506 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00528 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00591 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00631 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00636 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00671 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00674 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00677 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01045 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01046 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01142 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01156 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01327 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02041 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02465 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02504 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02704 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02706 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02707 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02708 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03260 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03266 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03727 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03728 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03747 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03748 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-04084 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00661 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00662 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00663 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00665 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01192 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01196 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01505 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01849 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01851 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01852 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02255 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02256 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02286 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03001 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03003 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03755 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-04686 | -1 | -1 | 4 | |||||
IvyGAP | W10 | 1 | 1 | 4 | |||||
IvyGAP | W11 | 0 | 0 | 4 | |||||
IvyGAP | W12 | 0 | 0 | 4 | |||||
IvyGAP | W13 | 0 | 0 | 4 | |||||
IvyGAP | W16 | 0 | 0 | 4 | |||||
IvyGAP | W18 | 0 | 0 | 4 | |||||
IvyGAP | W19 | 0 | 0 | 4 | |||||
IvyGAP | W1 | 0 | 0 | 4 | |||||
IvyGAP | W20 | 0 | 0 | 4 | |||||
IvyGAP | W21 | 0 | 0 | 4 | |||||
IvyGAP | W22 | 0 | 0 | -1 | |||||
IvyGAP | W26 | 0 | -1 | 4 | |||||
IvyGAP | W29 | 0 | 0 | 4 | |||||
IvyGAP | W2 | 0 | 1 | 4 | |||||
IvyGAP | W30 | 0 | 0 | 4 | |||||
IvyGAP | W31 | 1 | 1 | 4 | |||||
IvyGAP | W32 | 0 | 0 | 4 | |||||
IvyGAP | W33 | 0 | 0 | 4 | |||||
IvyGAP | W34 | 0 | 0 | 4 | |||||
IvyGAP | W35 | 1 | 0 | 3 | |||||
IvyGAP | W36 | 0 | 0 | 4 | |||||
IvyGAP | W38 | 0 | 0 | 4 | |||||
IvyGAP | W39 | 0 | 0 | 4 | |||||
IvyGAP | W3 | 1 | 0 | 4 | |||||
IvyGAP | W40 | 0 | 0 | 4 | |||||
IvyGAP | W42 | 0 | -1 | 4 | |||||
IvyGAP | W43 | 0 | -1 | 4 | |||||
IvyGAP | W45 | -1 | -1 | 4 | |||||
IvyGAP | W48 | 0 | -1 | 4 | |||||
IvyGAP | W4 | 1 | 0 | 4 | |||||
IvyGAP | W50 | 0 | -1 | 3 | |||||
IvyGAP | W53 | 1 | -1 | 4 | |||||
IvyGAP | W54 | 0 | -1 | 4 | |||||
IvyGAP | W55 | 0 | -1 | 4 | |||||
IvyGAP | W5 | 0 | 0 | 4 | |||||
IvyGAP | W6 | 0 | 0 | 4 | |||||
IvyGAP | W7 | 0 | 0 | 4 | |||||
IvyGAP | W8 | 0 | 0 | 4 | |||||
IvyGAP | W9 | 0 | 0 | 4 | |||||
REMBRANDT | 900-00-5299 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5303 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5308 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5316 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5317 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5332 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5339 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5341 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5342 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5346 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5380 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5381 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5382 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5385 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5396 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5404 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5412 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5414 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5458 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5459 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5462 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5468 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5476 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5477 | -1 | -1 | 2 | |||||
REMBRANDT | HF0763 | -1 | -1 | -1 | |||||
REMBRANDT | HF0828 | -1 | -1 | 3 | |||||
REMBRANDT | HF0835 | -1 | -1 | 2 | |||||
REMBRANDT | HF0855 | -1 | -1 | 2 | |||||
REMBRANDT | HF0868 | -1 | -1 | -1 | |||||
REMBRANDT | HF0883 | -1 | -1 | -1 | |||||
REMBRANDT | HF0899 | -1 | -1 | 2 | |||||
REMBRANDT | HF0920 | -1 | -1 | 2 | |||||
REMBRANDT | HF0931 | -1 | -1 | 2 | |||||
REMBRANDT | HF0953 | -1 | -1 | 2 | |||||
REMBRANDT | HF0960 | -1 | -1 | 2 | |||||
REMBRANDT | HF0966 | -1 | -1 | 3 | |||||
REMBRANDT | HF0986 | -1 | -1 | 4 | |||||
REMBRANDT | HF0990 | -1 | -1 | 4 | |||||
REMBRANDT | HF1000 | -1 | -1 | 2 | |||||
REMBRANDT | HF1058 | -1 | -1 | 4 | |||||
REMBRANDT | HF1059 | -1 | -1 | 3 | |||||
REMBRANDT | HF1071 | -1 | -1 | 4 | |||||
REMBRANDT | HF1077 | -1 | -1 | 4 | |||||
REMBRANDT | HF1078 | -1 | -1 | 4 | |||||
REMBRANDT | HF1097 | -1 | -1 | 4 | |||||
REMBRANDT | HF1113 | -1 | -1 | -1 | |||||
REMBRANDT | HF1122 | -1 | -1 | 4 | |||||
REMBRANDT | HF1136 | -1 | -1 | 3 | |||||
REMBRANDT | HF1139 | -1 | -1 | 4 | |||||
REMBRANDT | HF1150 | -1 | -1 | 3 | |||||
REMBRANDT | HF1156 | -1 | -1 | 2 | |||||
REMBRANDT | HF1167 | -1 | -1 | 2 | |||||
REMBRANDT | HF1185 | -1 | -1 | 3 | |||||
REMBRANDT | HF1191 | -1 | -1 | 4 | |||||
REMBRANDT | HF1199 | -1 | -1 | -1 | |||||
REMBRANDT | HF1219 | -1 | -1 | 3 | |||||
REMBRANDT | HF1227 | -1 | -1 | 2 | |||||
REMBRANDT | HF1232 | -1 | -1 | 3 | |||||
REMBRANDT | HF1235 | -1 | -1 | 2 | |||||
REMBRANDT | HF1242 | -1 | -1 | 3 | |||||
REMBRANDT | HF1246 | -1 | -1 | 2 | |||||
REMBRANDT | HF1264 | -1 | -1 | 2 | |||||
REMBRANDT | HF1269 | -1 | -1 | 4 | |||||
REMBRANDT | HF1280 | -1 | -1 | 3 | |||||
REMBRANDT | HF1292 | -1 | -1 | 4 | |||||
REMBRANDT | HF1293 | -1 | -1 | -1 | |||||
REMBRANDT | HF1297 | -1 | -1 | 4 | |||||
REMBRANDT | HF1300 | -1 | -1 | -1 | |||||
REMBRANDT | HF1307 | -1 | -1 | -1 | |||||
REMBRANDT | HF1316 | -1 | -1 | 2 | |||||
REMBRANDT | HF1318 | -1 | -1 | -1 | |||||
REMBRANDT | HF1325 | -1 | -1 | 2 | |||||
REMBRANDT | HF1331 | -1 | -1 | -1 | |||||
REMBRANDT | HF1334 | -1 | -1 | 2 | |||||
REMBRANDT | HF1344 | -1 | -1 | 2 | |||||
REMBRANDT | HF1345 | -1 | -1 | 2 | |||||
REMBRANDT | HF1357 | -1 | -1 | 3 | |||||
REMBRANDT | HF1381 | -1 | -1 | 2 | |||||
REMBRANDT | HF1397 | -1 | -1 | 4 | |||||
REMBRANDT | HF1398 | -1 | -1 | 3 | |||||
REMBRANDT | HF1407 | -1 | -1 | 2 | |||||
REMBRANDT | HF1409 | -1 | -1 | 3 | |||||
REMBRANDT | HF1420 | -1 | -1 | -1 | |||||
REMBRANDT | HF1429 | -1 | -1 | -1 | |||||
REMBRANDT | HF1433 | -1 | -1 | 2 | |||||
REMBRANDT | HF1437 | -1 | -1 | -1 | |||||
REMBRANDT | HF1442 | -1 | -1 | 2 | |||||
REMBRANDT | HF1458 | -1 | -1 | 3 | |||||
REMBRANDT | HF1463 | -1 | -1 | 2 | |||||
REMBRANDT | HF1489 | -1 | -1 | 2 | |||||
REMBRANDT | HF1490 | -1 | -1 | 3 | |||||
REMBRANDT | HF1493 | -1 | -1 | -1 | |||||
REMBRANDT | HF1510 | -1 | -1 | -1 | |||||
REMBRANDT | HF1511 | -1 | -1 | 2 | |||||
REMBRANDT | HF1517 | -1 | -1 | 4 | |||||
REMBRANDT | HF1538 | -1 | -1 | 4 | |||||
REMBRANDT | HF1551 | -1 | -1 | 2 | |||||
REMBRANDT | HF1553 | -1 | -1 | 2 | |||||
REMBRANDT | HF1560 | -1 | -1 | 4 | |||||
REMBRANDT | HF1568 | -1 | -1 | 2 | |||||
REMBRANDT | HF1587 | -1 | -1 | 3 | |||||
REMBRANDT | HF1588 | -1 | -1 | 2 | |||||
REMBRANDT | HF1606 | -1 | -1 | 2 | |||||
REMBRANDT | HF1613 | -1 | -1 | 3 | |||||
REMBRANDT | HF1628 | -1 | -1 | 4 | |||||
REMBRANDT | HF1652 | -1 | -1 | -1 | |||||
REMBRANDT | HF1677 | -1 | -1 | 2 | |||||
REMBRANDT | HF1702 | -1 | -1 | 3 | |||||
REMBRANDT | HF1708 | -1 | -1 | 2 | |||||
TCGA-GBM | TCGA-02-0003 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0006 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0009 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0011 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0027 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0033 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0034 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0037 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0046 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0047 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0048 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0054 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0059 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0060 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0064 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0068 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0069 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0070 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0075 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0085 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0086 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0087 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0102 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0106 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0116 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0119 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0122 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0128 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0130 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0132 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0133 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0137 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0138 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0139 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0142 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0145 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0149 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0154 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0158 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0162 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0164 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0166 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0168 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0175 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0176 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0177 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0179 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0182 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0184 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0185 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0187 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0188 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0189 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0190 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0192 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0213 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0238 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0240 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0241 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0644 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0646 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0648 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0649 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1084 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1802 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-2570 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5408 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5412 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5413 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5417 | 1 | -1 | 4 | |||||
TCGA-GBM | TCGA-06-6389 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0350 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0352 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0353 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0354 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0355 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0356 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0357 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0358 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0359 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0360 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0385 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0389 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0392 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0512 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0520 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0521 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0522 | -1 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0524 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0529 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0616 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0776 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1093 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1094 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1098 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1598 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1601 | 0 | -1 | -1 | |||||
TCGA-GBM | TCGA-12-1602 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-3650 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-0789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1456 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1794 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1825 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-3477 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-0963 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2624 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2631 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5951 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5954 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5958 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5960 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1834 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1838 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-2526 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4932 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-76-4934 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4935 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6191 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6193 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6280 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6282 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6285 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6656 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6657 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6661 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6662 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6663 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6664 | 0 | 0 | 4 | |||||
TCGA-LGG | TCGA-CS-4941 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4942 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4944 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5393 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-5395 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5396 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-CS-5397 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6186 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6188 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6290 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6665 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6666 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6667 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-6668 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-CS-6669 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5849 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-5851 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5852 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5854 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5871 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5874 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6397 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6399 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6400 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6404 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6405 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6407 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6410 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6542 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7008 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7010 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7014 | -1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7015 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7018 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7019 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7294 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-7298 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7300 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7302 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7309 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8162 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8164 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-8165 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8167 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8168 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-EZ-7265A | -1 | -1 | -1 | |||||
TCGA-LGG | TCGA-FG-5964 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-6688 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6691 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6692 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-7643 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-A713 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7473 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7616 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7680 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7684 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7686 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7692 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7693 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7694 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7856 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7860 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7874 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7879 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7882 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8105 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-8106 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8107 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8111 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8114 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8563 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A614 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0 | 2 |
Data_collection | Patient | IDH_mutated | Prediction_score_IDH_wildtype | Prediction_score_IDH_mutated | 1p19q_codeleted | Prediction_score_1p19q_codeleted | Prediction_score_1p19q_intact | Grade | Prediction_score_grade_2 | Prediction_score_grade_3 | Prediction_score_grade_4 | ||||||||||||
TCGA-GBM | TCGA-02-0003 | 0 | 099998915 | 10867886E-05 | 0 | 099996686 | 3308471E-05 | 4 | 7377526E-05 | 000074111245 | 099918514 | ||||||||||||
TCGA-GBM | TCGA-02-0006 | 0 | 042321962 | 05767803 | 0 | 068791837 | 031208166 | 4 | 060229343 | 026596427 | 013174225 | ||||||||||||
TCGA-GBM | TCGA-02-0009 | 0 | 099306935 | 0006930672 | 0 | 09906961 | 0009303949 | 4 | 0056565534 | 010282235 | 08406121 | ||||||||||||
TCGA-GBM | TCGA-02-0011 | 0 | 013531776 | 08646823 | 0 | 085318035 | 01468197 | 4 | 0015055533 | 092510724 | 005983725 | ||||||||||||
TCGA-GBM | TCGA-02-0027 | 0 | 09997279 | 000027212297 | 0 | 09986827 | 00013172914 | 4 | 00016104137 | 00038575265 | 0994532 | ||||||||||||
TCGA-GBM | TCGA-02-0033 | 0 | 099974436 | 000025564007 | 0 | 099940693 | 0000593021 | 4 | 00020670628 | 0003761288 | 09941717 | ||||||||||||
TCGA-GBM | TCGA-02-0034 | 0 | 091404164 | 008595832 | 0 | 089209336 | 01079066 | 4 | 00116944825 | 0061110377 | 092719513 | ||||||||||||
TCGA-GBM | TCGA-02-0037 | 0 | 09999577 | 42315594E-05 | 0 | 099992716 | 72827526E-05 | 4 | 82080274E-05 | 0009249337 | 09906686 | ||||||||||||
TCGA-GBM | TCGA-02-0046 | 0 | 0999129 | 00008710656 | 0 | 09989637 | 00010362669 | 4 | 0004290756 | 0022799779 | 097290945 | ||||||||||||
TCGA-GBM | TCGA-02-0047 | 0 | 099991703 | 83008505E-05 | 0 | 09999292 | 70863265E-05 | 4 | 000016252015 | 0040118434 | 095971906 | ||||||||||||
TCGA-GBM | TCGA-02-0048 | 0 | 09998785 | 000012148175 | 0 | 099959475 | 000040527192 | 4 | 00002215901 | 000039696065 | 09993814 | ||||||||||||
TCGA-GBM | TCGA-02-0054 | 0 | 09999831 | 1689829E-05 | 0 | 09999442 | 5583975E-05 | 4 | 00010063206 | 0060579527 | 093841416 | ||||||||||||
TCGA-GBM | TCGA-02-0059 | -1 | 09993749 | 000062511285 | 0 | 09996424 | 00003576683 | 4 | 00007046657 | 0010920537 | 09883748 | ||||||||||||
TCGA-GBM | TCGA-02-0060 | 0 | 07197039 | 028029615 | 0 | 09016612 | 009833879 | 4 | 017739706 | 03728545 | 04497484 | ||||||||||||
TCGA-GBM | TCGA-02-0064 | 0 | 09999083 | 9170197E-05 | 0 | 09995073 | 000049264234 | 4 | 000043781495 | 00028024286 | 099675983 | ||||||||||||
TCGA-GBM | TCGA-02-0068 | 0 | 099187535 | 0008124709 | 0 | 099528164 | 00047183693 | 4 | 00030539853 | 059695286 | 039999318 | ||||||||||||
TCGA-GBM | TCGA-02-0069 | 0 | 09890871 | 0010912909 | 0 | 099704784 | 0002952148 | 4 | 00057067247 | 0061368063 | 09329252 | ||||||||||||
TCGA-GBM | TCGA-02-0070 | 0 | 09940659 | 00059340666 | 0 | 0957794 | 0042206 | 4 | 0008216515 | 003556913 | 09562143 | ||||||||||||
TCGA-GBM | TCGA-02-0075 | 0 | 099933076 | 00006693099 | 0 | 099735296 | 00026470982 | 4 | 000044697264 | 00035736929 | 09959793 | ||||||||||||
TCGA-GBM | TCGA-02-0085 | 0 | 099114406 | 0008855922 | 0 | 09756698 | 002433019 | 4 | 00065203947 | 0035171553 | 095830804 | ||||||||||||
TCGA-GBM | TCGA-02-0086 | 0 | 099965334 | 000034666777 | 0 | 0998698 | 00013019645 | 4 | 000032699382 | 00018025768 | 099787045 | ||||||||||||
TCGA-GBM | TCGA-02-0087 | -1 | 09974885 | 00025114634 | 0 | 09990638 | 000093628286 | 4 | 0007505083 | 0008562708 | 098393226 | ||||||||||||
TCGA-GBM | TCGA-02-0102 | 0 | 09797647 | 0020235319 | 0 | 098292196 | 0017078074 | 4 | 003512482 | 03901857 | 05746895 | ||||||||||||
TCGA-GBM | TCGA-02-0106 | -1 | 099993694 | 6302759E-05 | 0 | 099980897 | 000019110431 | 4 | 60797247E-05 | 00008735659 | 09990657 | ||||||||||||
TCGA-GBM | TCGA-02-0116 | 0 | 09999778 | 22125667E-05 | 0 | 09996886 | 00003113695 | 4 | 000015498884 | 000051770627 | 09993273 | ||||||||||||
TCGA-GBM | TCGA-06-0119 | 0 | 09999362 | 63770494E-05 | 0 | 09999355 | 6452215E-05 | 4 | 000013225728 | 00028902534 | 099697745 | ||||||||||||
TCGA-GBM | TCGA-06-0122 | 0 | 09915298 | 0008470196 | 0 | 09859093 | 00140907345 | 4 | 00121390615 | 027333176 | 071452916 | ||||||||||||
TCGA-GBM | TCGA-06-0128 | 1 | 099988174 | 000011820537 | 0 | 099980634 | 000019373452 | 4 | 000016409029 | 0007865882 | 099197 | ||||||||||||
TCGA-GBM | TCGA-06-0130 | 0 | 099998784 | 12123987E-05 | 0 | 09999323 | 6775062E-05 | 4 | 80872844E-05 | 00026260202 | 099729306 | ||||||||||||
TCGA-GBM | TCGA-06-0132 | 0 | 09998566 | 000014341719 | 0 | 099988496 | 000011501736 | 4 | 000072843547 | 0005115947 | 099415565 | ||||||||||||
TCGA-GBM | TCGA-06-0133 | 0 | 097782 | 002218004 | 0 | 0993807 | 00061929906 | 4 | 0026753133 | 004659919 | 09266477 | ||||||||||||
TCGA-GBM | TCGA-06-0137 | 0 | 096448904 | 003551094 | 0 | 099403125 | 0005968731 | 4 | 000649511 | 038909483 | 060441005 | ||||||||||||
TCGA-GBM | TCGA-06-0138 | 0 | 09977743 | 00022256707 | 0 | 099736834 | 0002631674 | 4 | 00032954598 | 0011606657 | 09850979 | ||||||||||||
TCGA-GBM | TCGA-06-0139 | 0 | 09992649 | 00007350447 | 0 | 099898964 | 00010103129 | 4 | 00021781863 | 00069256434 | 099089617 | ||||||||||||
TCGA-GBM | TCGA-06-0142 | 0 | 099909425 | 00009057334 | 0 | 09985896 | 00014103584 | 4 | 0002598974 | 0046451908 | 09509491 | ||||||||||||
TCGA-GBM | TCGA-06-0145 | 0 | 099964654 | 000035350278 | 0 | 0999652 | 000034802416 | 4 | 00009068022 | 0021991275 | 0977102 | ||||||||||||
TCGA-GBM | TCGA-06-0149 | -1 | 09992161 | 00007839425 | 0 | 09981067 | 00018932257 | 4 | 00057726577 | 0013888515 | 09803388 | ||||||||||||
TCGA-GBM | TCGA-06-0154 | 0 | 099968064 | 000031937403 | 0 | 0999729 | 000027106237 | 4 | 000041507537 | 023430935 | 07652756 | ||||||||||||
TCGA-GBM | TCGA-06-0158 | 0 | 09999199 | 8014118E-05 | 0 | 099992514 | 74846226E-05 | 4 | 00026547876 | 020762624 | 0789719 | ||||||||||||
TCGA-GBM | TCGA-06-0162 | -1 | 099964297 | 00003569706 | 0 | 09997459 | 0000254147 | 4 | 000033955855 | 004318936 | 09564711 | ||||||||||||
TCGA-GBM | TCGA-06-0164 | -1 | 09983991 | 00016009645 | 0 | 09873262 | 0012673735 | 4 | 00016517473 | 00048346478 | 09935136 | ||||||||||||
TCGA-GBM | TCGA-06-0166 | 0 | 099991715 | 82846556E-05 | 0 | 0999554 | 00004459562 | 4 | 000013499439 | 0011635037 | 098823 | ||||||||||||
TCGA-GBM | TCGA-06-0168 | 0 | 09975561 | 00024438864 | 0 | 09964825 | 00035174883 | 4 | 0004766434 | 010448053 | 089075303 | ||||||||||||
TCGA-GBM | TCGA-06-0175 | -1 | 09996252 | 000037482675 | 0 | 09988098 | 00011902251 | 4 | 00026097735 | 004992068 | 094746953 | ||||||||||||
TCGA-GBM | TCGA-06-0176 | 0 | 099550986 | 00044901576 | 0 | 09998872 | 000011279297 | 4 | 0032868527 | 036690876 | 06002227 | ||||||||||||
TCGA-GBM | TCGA-06-0177 | -1 | 081774735 | 018225263 | 0 | 09946464 | 00053536464 | 4 | 0026683953 | 013013016 | 08431859 | ||||||||||||
TCGA-GBM | TCGA-06-0179 | -1 | 09997508 | 000024923254 | 0 | 09989778 | 00010222099 | 4 | 0002628482 | 0004127114 | 099324447 | ||||||||||||
TCGA-GBM | TCGA-06-0182 | -1 | 099999547 | 45838406E-06 | 0 | 099998736 | 12656287E-05 | 4 | 00002591103 | 000018499703 | 09995559 | ||||||||||||
TCGA-GBM | TCGA-06-0184 | 0 | 09935369 | 00064631375 | 0 | 099458355 | 00054164114 | 4 | 0023110552 | 0017436244 | 09594532 | ||||||||||||
TCGA-GBM | TCGA-06-0185 | 0 | 09999337 | 66310655E-05 | 0 | 099986255 | 000013738607 | 4 | 7657532E-05 | 0016089642 | 098383385 | ||||||||||||
TCGA-GBM | TCGA-06-0187 | 0 | 09991689 | 00008312097 | 0 | 099700147 | 00029984985 | 4 | 00020616595 | 0033111423 | 096482694 | ||||||||||||
TCGA-GBM | TCGA-06-0188 | 0 | 09883802 | 0011619771 | 0 | 09826743 | 0017325714 | 4 | 0013776424 | 0112841725 | 087338185 | ||||||||||||
TCGA-GBM | TCGA-06-0189 | 0 | 099906737 | 0000932636 | 0 | 09983865 | 00016135005 | 4 | 00022760795 | 00106745735 | 09870494 | ||||||||||||
TCGA-GBM | TCGA-06-0190 | 0 | 099954176 | 000045831292 | 0 | 09967013 | 00032986512 | 4 | 000040555766 | 0001246768 | 099834764 | ||||||||||||
TCGA-GBM | TCGA-06-0192 | 0 | 09997876 | 00002123566 | 0 | 09992735 | 00007264875 | 4 | 00004505576 | 00014473333 | 09981021 | ||||||||||||
TCGA-GBM | TCGA-06-0213 | 0 | 099986935 | 00001305845 | 0 | 099971646 | 000028351307 | 4 | 8755587E-05 | 00013480412 | 09985644 | ||||||||||||
TCGA-GBM | TCGA-06-0238 | 0 | 09999982 | 17603431E-06 | 0 | 09999894 | 10616134E-05 | 4 | 8076515E-05 | 56053756E-05 | 099986315 | ||||||||||||
TCGA-GBM | TCGA-06-0240 | 0 | 09989956 | 00010044163 | 0 | 099948466 | 00005152657 | 4 | 00016040986 | 021931975 | 077907616 | ||||||||||||
TCGA-GBM | TCGA-06-0241 | 0 | 099959785 | 000040211933 | 0 | 099910825 | 00008917038 | 4 | 00023411359 | 0007850656 | 098980826 | ||||||||||||
TCGA-GBM | TCGA-06-0644 | 0 | 09871044 | 0012895588 | 0 | 09859228 | 00140771745 | 4 | 0013671214 | 009819665 | 088813215 | ||||||||||||
TCGA-GBM | TCGA-06-0646 | 0 | 099959 | 00004100472 | 0 | 099936503 | 000063495064 | 4 | 00019223108 | 0040443853 | 095763385 | ||||||||||||
TCGA-GBM | TCGA-06-0648 | 0 | 09999709 | 29083441E-05 | 0 | 099982435 | 000017571273 | 4 | 000077678583 | 000038868992 | 099883455 | ||||||||||||
TCGA-GBM | TCGA-06-0649 | 0 | 09997805 | 000021952427 | 0 | 099951684 | 000048311835 | 4 | 0042641632 | 00058432207 | 095151514 | ||||||||||||
TCGA-GBM | TCGA-06-1084 | 0 | 099985826 | 000014174655 | 0 | 099968565 | 00003144242 | 4 | 00002676724 | 020492287 | 079480946 | ||||||||||||
TCGA-GBM | TCGA-06-1802 | -1 | 09991928 | 00008072305 | 0 | 09956176 | 0004382337 | 4 | 000043478087 | 00019495043 | 09976157 | ||||||||||||
TCGA-GBM | TCGA-06-2570 | 1 | 096841115 | 0031588882 | 0 | 09842457 | 0015754245 | 4 | 0015369608 | 0030956635 | 09536738 | ||||||||||||
TCGA-GBM | TCGA-06-5408 | 0 | 099857306 | 00014269598 | 0 | 09962638 | 00037362208 | 4 | 00027690146 | 0016195394 | 098103565 | ||||||||||||
TCGA-GBM | TCGA-06-5412 | 0 | 099366105 | 0006338921 | 0 | 099193794 | 0008061992 | 4 | 0011476759 | 006606435 | 09224589 | ||||||||||||
TCGA-GBM | TCGA-06-5413 | 0 | 09994105 | 000058955856 | 0 | 09983026 | 00016974095 | 4 | 00027100197 | 0021083053 | 097620696 | ||||||||||||
TCGA-GBM | TCGA-06-5417 | 1 | 01521267 | 08478733 | -1 | 03064492 | 06935508 | 4 | 013736826 | 037757674 | 048505494 | ||||||||||||
TCGA-GBM | TCGA-06-6389 | 1 | 099987435 | 000012558252 | 0 | 09997017 | 000029827762 | 4 | 00014020519 | 00020044278 | 099659353 | ||||||||||||
TCGA-GBM | TCGA-08-0350 | 0 | 019229275 | 08077072 | 0 | 0033211168 | 09667888 | 4 | 0051619414 | 022280572 | 072557485 | ||||||||||||
TCGA-GBM | TCGA-08-0352 | 0 | 099997497 | 25071595E-05 | 0 | 099992514 | 74846226E-05 | 4 | 000024192198 | 000048111935 | 099927694 | ||||||||||||
TCGA-GBM | TCGA-08-0353 | 0 | 09901496 | 0009850325 | 0 | 09967775 | 0003222484 | 4 | 00053748637 | 0004291497 | 09903336 | ||||||||||||
TCGA-GBM | TCGA-08-0354 | 0 | 076413894 | 023586108 | 0 | 07554566 | 024454337 | 4 | 008784444 | 02004897 | 071166587 | ||||||||||||
TCGA-GBM | TCGA-08-0355 | 0 | 09998349 | 000016506859 | 0 | 099984336 | 000015659066 | 4 | 000076689845 | 0023648744 | 09755844 | ||||||||||||
TCGA-GBM | TCGA-08-0356 | 0 | 097673583 | 0023264103 | 0 | 097773504 | 0022264915 | 4 | 001175834 | 0031075679 | 095716596 | ||||||||||||
TCGA-GBM | TCGA-08-0357 | 0 | 099509466 | 0004905406 | 0 | 099300176 | 00069982093 | 4 | 0005191745 | 0038681854 | 095612645 | ||||||||||||
TCGA-GBM | TCGA-08-0358 | 0 | 099999785 | 2199356E-06 | 0 | 099999034 | 9628425E-06 | 4 | 6113315E-06 | 00011283219 | 09988656 | ||||||||||||
TCGA-GBM | TCGA-08-0359 | 0 | 097885466 | 0021145396 | 0 | 09956006 | 00043994132 | 4 | 0009885523 | 0066605434 | 092350906 | ||||||||||||
TCGA-GBM | TCGA-08-0360 | 0 | 09922444 | 00077555366 | -1 | 09948704 | 00051296344 | 4 | 0013318472 | 003317344 | 095350814 | ||||||||||||
TCGA-GBM | TCGA-08-0385 | 0 | 099605453 | 00039454065 | -1 | 099686414 | 0003135836 | 4 | 00050293226 | 0029977333 | 096499336 | ||||||||||||
TCGA-GBM | TCGA-08-0389 | 0 | 099964714 | 000035281325 | 0 | 09991272 | 000087276706 | 4 | 00017554013 | 00024730961 | 099577147 | ||||||||||||
TCGA-GBM | TCGA-08-0390 | 0 | 099945146 | 000054847915 | 0 | 099936 | 00006399274 | 4 | 00036811908 | 00050958768 | 0991223 | ||||||||||||
TCGA-GBM | TCGA-08-0392 | 0 | 099962366 | 000037629317 | 0 | 09993575 | 00006424303 | 4 | 000036593352 | 0010291994 | 09893421 | ||||||||||||
TCGA-GBM | TCGA-08-0512 | -1 | 09982893 | 00017106998 | 0 | 099193794 | 0008061992 | 4 | 00016200381 | 00027773918 | 09956026 | ||||||||||||
TCGA-GBM | TCGA-08-0520 | -1 | 099603915 | 00039607873 | 0 | 09981933 | 00018066854 | 4 | 00007140295 | 0019064669 | 09802213 | ||||||||||||
TCGA-GBM | TCGA-08-0521 | -1 | 09975274 | 0002472623 | 0 | 099490017 | 00050998176 | 4 | 0001514669 | 0020103427 | 09783819 | ||||||||||||
TCGA-GBM | TCGA-08-0522 | -1 | 099960107 | 000039899128 | -1 | 09992053 | 00007947255 | 4 | 0000269389 | 0006173321 | 09935573 | ||||||||||||
TCGA-GBM | TCGA-08-0524 | -1 | 09964619 | 0003538086 | 0 | 099620515 | 0003794834 | 4 | 000019140428 | 0010096702 | 09897119 | ||||||||||||
TCGA-GBM | TCGA-08-0529 | -1 | 09996567 | 000034329997 | 0 | 099952066 | 00004793605 | 4 | 000032077235 | 0035970636 | 09637086 | ||||||||||||
TCGA-GBM | TCGA-12-0616 | 0 | 098521465 | 0014785408 | 0 | 098704207 | 001295789 | 4 | 001592791 | 012875569 | 08553164 | ||||||||||||
TCGA-GBM | TCGA-12-0776 | -1 | 099899167 | 00010083434 | 0 | 09987031 | 00012968953 | 4 | 0019219175 | 00637484 | 09170324 | ||||||||||||
TCGA-GBM | TCGA-12-0829 | 0 | 099913067 | 00008693674 | 0 | 099821776 | 00017821962 | 4 | 00021031094 | 0055067167 | 09428297 | ||||||||||||
TCGA-GBM | TCGA-12-1093 | 0 | 099992585 | 7411892E-05 | 0 | 09999448 | 5518923E-05 | 4 | 000046803855 | 0012115157 | 098741674 | ||||||||||||
TCGA-GBM | TCGA-12-1094 | -1 | 09980045 | 00019955388 | 0 | 09866105 | 0013389497 | 4 | 00053194338 | 001599471 | 097868586 | ||||||||||||
TCGA-GBM | TCGA-12-1098 | -1 | 09998406 | 000015936712 | 0 | 09977216 | 00022783307 | 4 | 000010218692 | 0035607774 | 09642901 | ||||||||||||
TCGA-GBM | TCGA-12-1598 | 0 | 096309197 | 0036908068 | 0 | 097933435 | 0020665688 | 4 | 0012952217 | 052912676 | 045792103 | ||||||||||||
TCGA-GBM | TCGA-12-1601 | 0 | 09875683 | 0012431651 | -1 | 0991891 | 0008108984 | -1 | 00118053425 | 0105477065 | 088271755 | ||||||||||||
TCGA-GBM | TCGA-12-1602 | 0 | 099830914 | 00016908031 | 0 | 099858415 | 00014158705 | 4 | 0008427611 | 0025996923 | 09655755 | ||||||||||||
TCGA-GBM | TCGA-12-3650 | 0 | 09761519 | 0023848088 | 0 | 097467697 | 0025323058 | 4 | 0010450666 | 043705726 | 05524921 | ||||||||||||
TCGA-GBM | TCGA-14-0789 | 0 | 099856466 | 00014353332 | 0 | 099666256 | 00033374047 | 4 | 0001406897 | 0008273975 | 099031913 | ||||||||||||
TCGA-GBM | TCGA-14-1456 | 1 | 006299064 | 093700933 | 0 | 08656222 | 013437784 | 4 | 016490369 | 047177824 | 036331803 | ||||||||||||
TCGA-GBM | TCGA-14-1794 | 0 | 08579393 | 014206071 | 0 | 09850429 | 0014957087 | 4 | 0023009384 | 009868736 | 08783033 | ||||||||||||
TCGA-GBM | TCGA-14-1825 | 0 | 099960107 | 000039899128 | 0 | 099968123 | 00003187511 | 4 | 0008552247 | 0010156045 | 09812918 | ||||||||||||
TCGA-GBM | TCGA-14-1829 | 0 | 090690076 | 009309922 | 0 | 09907856 | 0009214366 | 4 | 0008461936 | 0102735735 | 088880235 | ||||||||||||
TCGA-GBM | TCGA-14-3477 | 0 | 099796116 | 0002038787 | 0 | 09990728 | 00009271923 | 4 | 00032272525 | 0021644868 | 09751279 | ||||||||||||
TCGA-GBM | TCGA-19-0963 | -1 | 099876726 | 00012327607 | 0 | 09983612 | 00016388679 | 4 | 00031698826 | 013153598 | 086529416 | ||||||||||||
TCGA-GBM | TCGA-19-1390 | 0 | 099913234 | 00008676725 | 0 | 099703634 | 00029636684 | 4 | 00015592943 | 0026028048 | 097241265 | ||||||||||||
TCGA-GBM | TCGA-19-1789 | 0 | 09809491 | 00190509 | 0 | 09915216 | 0008478402 | 4 | 0038703684 | 014341596 | 08178804 | ||||||||||||
TCGA-GBM | TCGA-19-2624 | 0 | 07535573 | 024644265 | 0 | 09816127 | 0018387254 | 4 | 012311598 | 012769651 | 07491875 | ||||||||||||
TCGA-GBM | TCGA-19-2631 | 0 | 099860877 | 0001391234 | 0 | 09981178 | 00018821858 | 4 | 00009839778 | 001843531 | 09805807 | ||||||||||||
TCGA-GBM | TCGA-19-5951 | 0 | 09999031 | 9685608E-05 | 0 | 099977034 | 000022960825 | 4 | 00020246736 | 0004014765 | 09939606 | ||||||||||||
TCGA-GBM | TCGA-19-5954 | 0 | 099456257 | 00054374957 | 0 | 09968273 | 00031726828 | 4 | 00073725334 | 006310084 | 09295266 | ||||||||||||
TCGA-GBM | TCGA-19-5958 | 0 | 099999475 | 5234907E-06 | 0 | 0999941 | 58978338E-05 | 4 | 35422294E-05 | 86819025E-05 | 09998777 | ||||||||||||
TCGA-GBM | TCGA-19-5960 | 0 | 09683962 | 003160382 | 0 | 09013577 | 009864227 | 4 | 0011394806 | 018114014 | 08074651 | ||||||||||||
TCGA-GBM | TCGA-27-1834 | 0 | 099998164 | 18342893E-05 | 0 | 09999685 | 31446623E-05 | 4 | 8611921E-05 | 000031686216 | 0999597 | ||||||||||||
TCGA-GBM | TCGA-27-1838 | 0 | 09993625 | 000063743413 | 0 | 09940428 | 0005957154 | 4 | 00006736379 | 0007191195 | 099213517 | ||||||||||||
TCGA-GBM | TCGA-27-2526 | 0 | 099983776 | 000016219281 | 0 | 09996898 | 000031015594 | 4 | 000016658282 | 00006323714 | 09992011 | ||||||||||||
TCGA-GBM | TCGA-76-4932 | 0 | 09867389 | 0013261103 | -1 | 09949397 | 0005060332 | 4 | 00007321126 | 0003016794 | 099625117 | ||||||||||||
TCGA-GBM | TCGA-76-4934 | 0 | 099318933 | 0006810731 | 0 | 09995073 | 000049264234 | 4 | 00061555947 | 00070025027 | 098684186 | ||||||||||||
TCGA-GBM | TCGA-76-4935 | 0 | 074562997 | 025437003 | 0 | 098242307 | 001757688 | 4 | 076535034 | 006437644 | 017027317 | ||||||||||||
TCGA-GBM | TCGA-76-6191 | 0 | 09981067 | 00018932257 | 0 | 09970879 | 00029121784 | 4 | 00044340584 | 00096095055 | 098595643 | ||||||||||||
TCGA-GBM | TCGA-76-6193 | 0 | 09966168 | 0003383191 | 0 | 099850464 | 00014953383 | 4 | 00037061477 | 007873953 | 09175543 | ||||||||||||
TCGA-GBM | TCGA-76-6280 | 0 | 099948776 | 00005122569 | 0 | 099908185 | 000091819017 | 4 | 00001475792 | 000846075 | 099139166 | ||||||||||||
TCGA-GBM | TCGA-76-6282 | 0 | 0995906 | 0004093958 | 0 | 099861956 | 00013804223 | 4 | 00006694951 | 0009437619 | 09898929 | ||||||||||||
TCGA-GBM | TCGA-76-6285 | 0 | 099949074 | 00005092657 | 0 | 09971661 | 00028338495 | 4 | 00031175872 | 004005614 | 095682627 | ||||||||||||
TCGA-GBM | TCGA-76-6656 | 0 | 09996917 | 000030834455 | 0 | 09983897 | 00016103574 | 4 | 002648366 | 00017969633 | 09717193 | ||||||||||||
TCGA-GBM | TCGA-76-6657 | 0 | 099987245 | 000012755992 | 0 | 099951494 | 00004850083 | 4 | 000096620515 | 0005599633 | 09934342 | ||||||||||||
TCGA-GBM | TCGA-76-6661 | 0 | 093211424 | 006788577 | 0 | 09640178 | 0035982177 | 4 | 003490037 | 0026863772 | 09382358 | ||||||||||||
TCGA-GBM | TCGA-76-6662 | 0 | 096425414 | 0035745807 | 0 | 09963924 | 0003607617 | 4 | 002845819 | 002544755 | 09460942 | ||||||||||||
TCGA-GBM | TCGA-76-6663 | 0 | 088664144 | 0113358565 | 0 | 09984207 | 00015792594 | 4 | 0010206689 | 043740335 | 05523899 | ||||||||||||
TCGA-GBM | TCGA-76-6664 | 0 | 011047115 | 08895289 | 0 | 09559813 | 004401865 | 4 | 00049677677 | 08806894 | 011434281 | ||||||||||||
TCGA-LGG | TCGA-CS-4941 | 0 | 088931274 | 011068726 | 0 | 087037706 | 012962292 | 3 | 002865127 | 0048591908 | 092275685 | ||||||||||||
TCGA-LGG | TCGA-CS-4942 | 1 | 00031327847 | 099686724 | 0 | 096309197 | 0036908068 | 3 | 096261597 | 00148612335 | 0022522787 | ||||||||||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0005265965 | 099473405 | 0 | 09940544 | 00059455987 | 3 | 09439103 | 0023049146 | 003304057 | ||||||||||||
TCGA-LGG | TCGA-CS-4944 | 1 | 009363656 | 09063635 | 0 | 08755211 | 0124478824 | 2 | 034047556 | 033881712 | 03207073 | ||||||||||||
TCGA-LGG | TCGA-CS-5393 | 1 | 009623762 | 09037624 | 0 | 098178816 | 001821182 | 3 | 014111634 | 042021698 | 043866673 | ||||||||||||
TCGA-LGG | TCGA-CS-5395 | 0 | 08502822 | 014971776 | 0 | 09932025 | 00067975316 | 2 | 0052374925 | 018397054 | 076365453 | ||||||||||||
TCGA-LGG | TCGA-CS-5396 | 1 | 099839586 | 00016040892 | 1 | 099967945 | 000032062363 | 3 | 00016345463 | 029090768 | 07074577 | ||||||||||||
TCGA-LGG | TCGA-CS-5397 | 0 | 049304244 | 050695753 | 0 | 08829839 | 0117016025 | 3 | 038702008 | 021211159 | 040086827 | ||||||||||||
TCGA-LGG | TCGA-CS-6186 | 0 | 099913234 | 00008676725 | 0 | 099956185 | 000043818905 | 3 | 00008662089 | 016898473 | 083014905 | ||||||||||||
TCGA-LGG | TCGA-CS-6188 | 0 | 052768165 | 047231838 | 0 | 08584221 | 014157787 | 3 | 019437431 | 047675493 | 03288707 | ||||||||||||
TCGA-LGG | TCGA-CS-6290 | 1 | 09102666 | 008973339 | 0 | 09462997 | 0053700306 | 3 | 0104100704 | 025633416 | 06395651 | ||||||||||||
TCGA-LGG | TCGA-CS-6665 | 1 | 099600047 | 0003999501 | 0 | 099756086 | 00024391294 | 3 | 0011873978 | 001634113 | 097178483 | ||||||||||||
TCGA-LGG | TCGA-CS-6666 | 1 | 021655986 | 07834402 | 0 | 09327296 | 0067270435 | 3 | 017667453 | 036334327 | 045998225 | ||||||||||||
TCGA-LGG | TCGA-CS-6667 | 1 | 012061995 | 087938 | 0 | 095699733 | 0043002643 | 2 | 063733935 | 019323014 | 016943048 | ||||||||||||
TCGA-LGG | TCGA-CS-6668 | 1 | 0076787576 | 09232124 | 1 | 04240933 | 057590663 | 2 | 06810894 | 013706882 | 018184178 | ||||||||||||
TCGA-LGG | TCGA-CS-6669 | 0 | 08488156 | 01511844 | 0 | 094018847 | 005981148 | 2 | 0037862387 | 002352077 | 09386168 | ||||||||||||
TCGA-LGG | TCGA-DU-5849 | 1 | 005773187 | 094226813 | 1 | 08664153 | 013358466 | 2 | 072753835 | 015028271 | 012217898 | ||||||||||||
TCGA-LGG | TCGA-DU-5851 | 1 | 09963994 | 0003600603 | 0 | 099808073 | 00019192374 | 3 | 00060602655 | 012558761 | 08683521 | ||||||||||||
TCGA-LGG | TCGA-DU-5852 | 0 | 09998591 | 000014091856 | 0 | 099954873 | 000045121062 | 3 | 0002267452 | 00038046916 | 099392784 | ||||||||||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0010986943 | 09890131 | 0 | 09549844 | 0045015533 | 2 | 08603989 | 0077804394 | 0061796777 | ||||||||||||
TCGA-LGG | TCGA-DU-5854 | 0 | 09567354 | 0043264627 | 0 | 098768765 | 0012312326 | 3 | 01194655 | 027027336 | 06102612 | ||||||||||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0009312956 | 09906871 | 0 | 046602532 | 053397465 | 3 | 0008289882 | 097042197 | 0021288157 | ||||||||||||
TCGA-LGG | TCGA-DU-5871 | 1 | 005623634 | 09437636 | 0 | 09449439 | 005505607 | 2 | 042517176 | 020180763 | 037302068 | ||||||||||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0062359583 | 09376405 | 0 | 015278916 | 08472108 | 2 | 012133307 | 048199505 | 039667192 | ||||||||||||
TCGA-LGG | TCGA-DU-5874 | 1 | 022858672 | 077141327 | 1 | 06457066 | 03542934 | 2 | 058503634 | 020639434 | 02085693 | ||||||||||||
TCGA-LGG | TCGA-DU-6397 | 1 | 097691274 | 002308724 | 1 | 09908213 | 0009178773 | 3 | 00048094327 | 00412339 | 09539566 | ||||||||||||
TCGA-LGG | TCGA-DU-6399 | 1 | 00023920655 | 099760795 | 0 | 09970073 | 00029926652 | 2 | 098691386 | 0007037292 | 0006048777 | ||||||||||||
TCGA-LGG | TCGA-DU-6400 | 1 | 0030923586 | 09690764 | 1 | 037771282 | 06222872 | 2 | 09710506 | 0015339471 | 001360994 | ||||||||||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0014545513 | 098545444 | 0 | 045332992 | 054667014 | 2 | 0878585 | 006398724 | 005742785 | ||||||||||||
TCGA-LGG | TCGA-DU-6404 | 0 | 08563024 | 014369765 | 0 | 09857318 | 00142681915 | 3 | 0012578745 | 08931047 | 009431658 | ||||||||||||
TCGA-LGG | TCGA-DU-6405 | 0 | 094122344 | 0058776554 | 0 | 09657707 | 0034229323 | 3 | 0015099723 | 0858934 | 012596628 | ||||||||||||
TCGA-LGG | TCGA-DU-6407 | 1 | 00046772743 | 099532276 | 0 | 095787287 | 0042127114 | 2 | 095650303 | 0019410672 | 0024086302 | ||||||||||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0032852467 | 09671475 | 0 | 02978783 | 070212173 | 3 | 046377006 | 04552443 | 008098562 | ||||||||||||
TCGA-LGG | TCGA-DU-6410 | 1 | 084198 | 015801999 | 1 | 09610981 | 0038901985 | 3 | 0029748935 | 0547783 | 042246798 | ||||||||||||
TCGA-LGG | TCGA-DU-6542 | 1 | 099541724 | 0004582765 | 0 | 099690056 | 00030994152 | 3 | 00036504513 | 0033356518 | 0962993 | ||||||||||||
TCGA-LGG | TCGA-DU-7008 | 1 | 00027017966 | 09972982 | 0 | 09924154 | 0007584589 | 2 | 0945233 | 0033200152 | 0021566862 | ||||||||||||
TCGA-LGG | TCGA-DU-7010 | 1 | 09090629 | 0090937115 | 0 | 083999664 | 016000335 | 3 | 0011747591 | 011156695 | 08766855 | ||||||||||||
TCGA-LGG | TCGA-DU-7014 | -1 | 00067384504 | 09932615 | 0 | 09144437 | 008555635 | 2 | 090214694 | 005846623 | 003938676 | ||||||||||||
TCGA-LGG | TCGA-DU-7015 | 1 | 011059116 | 08894088 | 0 | 09457512 | 005424881 | 2 | 04990067 | 023008518 | 027090812 | ||||||||||||
TCGA-LGG | TCGA-DU-7018 | 1 | 06190684 | 038093168 | 1 | 09720721 | 0027927874 | 3 | 002608347 | 03462771 | 06276394 | ||||||||||||
TCGA-LGG | TCGA-DU-7019 | 1 | 006866228 | 09313377 | 0 | 068647516 | 031352484 | 3 | 06280373 | 02546188 | 011734395 | ||||||||||||
TCGA-LGG | TCGA-DU-7294 | 1 | 039513415 | 06048658 | 1 | 04910898 | 05089102 | 2 | 044678423 | 011827048 | 04349453 | ||||||||||||
TCGA-LGG | TCGA-DU-7298 | 1 | 002178117 | 097821885 | 0 | 058896303 | 041103697 | 3 | 04621931 | 040058115 | 013722575 | ||||||||||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0050494254 | 094950575 | 0 | 09805993 | 0019400762 | 3 | 088520575 | 003754964 | 0077244624 | ||||||||||||
TCGA-LGG | TCGA-DU-7300 | 1 | 020334144 | 07966585 | 1 | 021174264 | 078825736 | 3 | 06957292 | 014594184 | 015832895 | ||||||||||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0028517082 | 09714829 | 0 | 07594931 | 024050693 | 2 | 07559878 | 013617343 | 010783881 | ||||||||||||
TCGA-LGG | TCGA-DU-7302 | 1 | 007878401 | 092121595 | 1 | 097414124 | 0025858777 | 3 | 059945434 | 013100924 | 02695364 | ||||||||||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0049359404 | 09506406 | 0 | 09947084 | 0005291605 | 3 | 05746174 | 017312215 | 025226048 | ||||||||||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0774658 | 022534202 | 0 | 09720191 | 0027980946 | 2 | 007909051 | 04979186 | 042299092 | ||||||||||||
TCGA-LGG | TCGA-DU-7309 | 1 | 002068546 | 097931457 | 0 | 091696864 | 008303132 | 3 | 091011685 | 0041825026 | 0048058107 | ||||||||||||
TCGA-LGG | TCGA-DU-8162 | 0 | 019030987 | 08096902 | 0 | 084344435 | 015655571 | 3 | 06724078 | 015660264 | 017098951 | ||||||||||||
TCGA-LGG | TCGA-DU-8164 | 1 | 0026989132 | 09730109 | 1 | 06119184 | 038808158 | 2 | 078654927 | 011851947 | 00949313 | ||||||||||||
TCGA-LGG | TCGA-DU-8165 | 0 | 099918324 | 000081673806 | 0 | 09982692 | 00017308301 | 3 | 00077142627 | 001586733 | 097641844 | ||||||||||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0062617026 | 093738294 | 0 | 052265906 | 047734097 | 2 | 0571523 | 027175376 | 015672325 | ||||||||||||
TCGA-LGG | TCGA-DU-8167 | 1 | 008068282 | 09193171 | 0 | 08626991 | 013730097 | 2 | 07117111 | 014616342 | 014212546 | ||||||||||||
TCGA-LGG | TCGA-DU-8168 | 1 | 04501781 | 05498219 | 1 | 09405718 | 005942822 | 3 | 028535154 | 039651006 | 031813842 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 013576113 | 08642388 | 0 | 098667485 | 0013325148 | 3 | 06805368 | 011191124 | 020755199 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0038810804 | 09611892 | 0 | 094154674 | 005845324 | 2 | 07418394 | 01198958 | 013826479 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 036534345 | 06346565 | 0 | 097664696 | 0023353029 | 2 | 0076500095 | 07058904 | 021760948 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 057493186 | 042506814 | 0 | 08586593 | 014134066 | 3 | 024835269 | 018135522 | 05702921 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 017411166 | 082588834 | 0 | 08903419 | 0109658085 | 2 | 026840523 | 031951824 | 041207647 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 00015382263 | 099846184 | 0 | 09784259 | 0021574067 | 3 | 099424005 | 00014788082 | 0004281163 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 099497885 | 000502115 | 0 | 09904406 | 0009559399 | 3 | 00076062134 | 003340487 | 09589889 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 01338958 | 08661042 | 1 | 010181248 | 08981875 | 2 | 08703488 | 0033631936 | 0096019216 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 007097701 | 092902297 | 1 | 0049773447 | 09502266 | 2 | 08236395 | 0043779366 | 013258114 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 00054852334 | 09945148 | 1 | 00052813343 | 09947187 | 2 | 095740056 | 0030734295 | 0011865048 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 00015218158 | 099847823 | 0 | 09977216 | 00022783307 | 3 | 097611564 | 0011231668 | 0012652792 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 090418625 | 009581377 | 1 | 09320215 | 006797852 | 3 | 015433969 | 006605734 | 0779603 | ||||||||||||
TCGA-LGG | TCGA-EZ-7265A | -1 | 001654544 | 09834546 | -1 | 092290026 | 0077099696 | -1 | 091443384 | 0045349486 | 0040216673 | ||||||||||||
TCGA-LGG | TCGA-FG-5964 | 1 | 095945925 | 004054074 | 1 | 09480585 | 0051941562 | 2 | 0052469887 | 018844457 | 075908554 | ||||||||||||
TCGA-LGG | TCGA-FG-6688 | 0 | 041685596 | 0583144 | 0 | 0400786 | 0599214 | 3 | 032869554 | 028211078 | 03891937 | ||||||||||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0040960647 | 09590394 | 0 | 088871056 | 01112895 | 2 | 078484637 | 01247657 | 009038795 | ||||||||||||
TCGA-LGG | TCGA-FG-6691 | 1 | 00066411127 | 09933589 | 0 | 09705485 | 002945148 | 2 | 082394814 | 01353293 | 004072261 | ||||||||||||
TCGA-LGG | TCGA-FG-6692 | 0 | 099044985 | 0009550158 | 0 | 098370695 | 0016293105 | 3 | 002482948 | 023811981 | 07370507 | ||||||||||||
TCGA-LGG | TCGA-FG-7643 | 0 | 067991304 | 032008696 | 0 | 094600123 | 0053998843 | 2 | 032237333 | 020420441 | 047342223 | ||||||||||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 00037180893 | 09962819 | 0 | 098237246 | 0017627545 | 2 | 09685786 | 0019877713 | 0011543726 | ||||||||||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 0023916386 | 097608364 | 1 | 003330537 | 096669465 | 2 | 016134319 | 0751304 | 0087352775 | ||||||||||||
TCGA-LGG | TCGA-FG-A713 | 1 | 020932822 | 07906717 | 1 | 053740746 | 046259254 | 2 | 068370515 | 013241291 | 018388201 | ||||||||||||
TCGA-LGG | TCGA-HT-7473 | 1 | 026437023 | 073562974 | 0 | 09914391 | 0008560891 | 2 | 009070598 | 05834457 | 032584828 | ||||||||||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0014885316 | 09851147 | 0 | 093397486 | 006602513 | 3 | 09713343 | 0009202645 | 0019462984 | ||||||||||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0078306936 | 09216931 | 0 | 044295275 | 055704725 | 2 | 06683338 | 024550638 | 00861598 | ||||||||||||
TCGA-LGG | TCGA-HT-7616 | 1 | 0994089 | 0005911069 | 1 | 08912444 | 010875558 | 3 | 00015109215 | 00081261955 | 09903628 | ||||||||||||
TCGA-LGG | TCGA-HT-7680 | 0 | 01775255 | 08224745 | 0 | 079779327 | 020220678 | 2 | 06160002 | 021518312 | 016881672 | ||||||||||||
TCGA-LGG | TCGA-HT-7684 | 1 | 099250317 | 00074968883 | 0 | 09977216 | 00022783307 | 3 | 0001585032 | 0011880362 | 09865346 | ||||||||||||
TCGA-LGG | TCGA-HT-7686 | 1 | 043986762 | 05601324 | 0 | 09985134 | 00014866153 | 3 | 08800356 | 0017893802 | 010207067 | ||||||||||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0508178 | 049182203 | 0 | 09986749 | 00013250223 | 3 | 007351807 | 06479417 | 027854022 | ||||||||||||
TCGA-LGG | TCGA-HT-7692 | 1 | 0006764646 | 09932354 | 1 | 00017718028 | 099822825 | 2 | 084003216 | 009709251 | 00628754 | ||||||||||||
TCGA-LGG | TCGA-HT-7693 | 1 | 08835126 | 011648734 | 0 | 098880965 | 0011190402 | 2 | 00389769 | 060259813 | 035842496 | ||||||||||||
TCGA-LGG | TCGA-HT-7694 | 1 | 006299064 | 093700933 | 1 | 06663645 | 03336355 | 3 | 06368097 | 02515797 | 011161056 | ||||||||||||
TCGA-LGG | TCGA-HT-7855 | 1 | 013434944 | 086565053 | 0 | 06805072 | 031949285 | 3 | 03900612 | 035394293 | 02559958 | ||||||||||||
TCGA-LGG | TCGA-HT-7856 | 1 | 0037151825 | 09628482 | 1 | 045108467 | 05489153 | 3 | 0024809493 | 094240344 | 0032787096 | ||||||||||||
TCGA-LGG | TCGA-HT-7860 | 0 | 09996338 | 000036614697 | 0 | 099890125 | 00010987312 | 3 | 00023981468 | 004139189 | 095620996 | ||||||||||||
TCGA-LGG | TCGA-HT-7874 | 1 | 027373514 | 07262649 | 1 | 061277324 | 03872268 | 3 | 030690825 | 04321765 | 026091516 | ||||||||||||
TCGA-LGG | TCGA-HT-7879 | 1 | 006545533 | 09345446 | 0 | 07643643 | 023563562 | 3 | 07316188 | 01349482 | 0133433 | ||||||||||||
TCGA-LGG | TCGA-HT-7882 | 0 | 099826247 | 00017375927 | 0 | 099920684 | 0000793176 | 3 | 00026636408 | 0011237644 | 098609877 | ||||||||||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0045437213 | 09545628 | 0 | 09804874 | 0019512545 | 2 | 067944294 | 020575646 | 011480064 | ||||||||||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0090937115 | 09090629 | 0 | 08061669 | 019383314 | 2 | 069696444 | 017501967 | 012801588 | ||||||||||||
TCGA-LGG | TCGA-HT-8105 | 1 | 09291196 | 0070880495 | 1 | 09865976 | 0013402403 | 3 | 036353382 | 007970196 | 055676425 | ||||||||||||
TCGA-LGG | TCGA-HT-8106 | 1 | 09987081 | 00012918457 | 0 | 099922514 | 00007748164 | 3 | 0019909225 | 0057560045 | 09225307 | ||||||||||||
TCGA-LGG | TCGA-HT-8107 | 0 | 006150854 | 09384914 | 0 | 018944609 | 08105539 | 2 | 071847403 | 015055439 | 013097167 | ||||||||||||
TCGA-LGG | TCGA-HT-8111 | 1 | 062096643 | 037903354 | 0 | 09220272 | 007797278 | 3 | 00015017459 | 090417147 | 009432678 | ||||||||||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0003941571 | 099605846 | 0 | 00025608707 | 099743915 | 2 | 088384247 | 009021307 | 002594447 | ||||||||||||
TCGA-LGG | TCGA-HT-8114 | 1 | 09404078 | 005959219 | 0 | 09970708 | 00029292419 | 3 | 0015292862 | 028022403 | 07044831 | ||||||||||||
TCGA-LGG | TCGA-HT-8563 | 1 | 099999154 | 843094E-06 | 0 | 099999607 | 39515203E-06 | 3 | 32918017E-06 | 031742522 | 068257153 | ||||||||||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 06915494 | 030845058 | 0 | 045883363 | 054116637 | 3 | 012257236 | 02777765 | 059965116 | ||||||||||||
TCGA-LGG | TCGA-HT_A614 | 1 | 08180474 | 018195263 | 0 | 09584989 | 00415011 | 2 | 0067164555 | 0059489973 | 087334543 | ||||||||||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0035779487 | 09642206 | 0 | 07277821 | 0272218 | 2 | 07607039 | 014429174 | 009500429 |
Table 2 Evaluation results of the final model on the test set
Patientgroup
Task AUC Accuracy Sensitivity Specificity
All IDH 090 084 072 0931p19q 085 089 039 095Grade (IIIIIIV) 081 071 NA NAGrade II 091 086 075 089Grade III 069 075 017 094Grade IV 091 082 095 066LGG vs HGG 091 084 072 093
LGG IDH 081 074 073 0771p19q 073 076 039 089
HGG IDH 064 094 040 096
Abbreviations AUC area under receiver operating characteristic curve IDHisocitrate dehydrogenase LGG low grade glioma HGG high grade glioma
Figure 3 Receiver operating characteristic (ROC)-curves of the genetic andhistological features evaluated on the test set The crosses indicate the locationof the decision threshold for the reported accuracy sensitivity and specificity
8
Figure 4 DICE scores Hausdorff distances and volumetric similarity coeffi-cients for all patients in the test set The DICE score is a measure of theoverlap between the ground truth and predicted segmentation (where 1 indi-cates perfect overlap) The Hausdorff distance is a measure of the agreementbetween the boundaries of the ground truth and predicted segmentation (loweris better) The volumetric similarity coefficient is a measure of the agreementin volume (where 1 indicates perfect agreement)
9
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Saliency maps of a low grade glioma patient (TCGA-DU-6400) This is an IDH mu-tated 1p19q co-deleted grade II tumor The network focuses on a rim of brightnessin the T2w-FLAIR scan
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) Saliency maps of a high grade glioma patient (TCGA-06-0238) This is an IDHwildtype grade IV tumor The network focuses on enhancing spots around the necrosison the post-contrast T1w scan
Figure 5 Saliency maps of two patients from the test set showing areas thatare relevant for the prediction
10
24 Model robustness
By not excluding scans from our train set based on radiological characteristicswe were able to make our model robust to low scan quality as can be seen in anexample from the test set in Figure 6 Even though this example scan containedimaging artifacts our method was able to properly segment the tumor (DICEscore of 087) and correctly predict the tumor as an IDH wildtype grade IVtumor
Figure 6 Example of a T2w-FLAIR scan containing imaging artifacts and theautomatic segmentation (red overlay) made by our method It was correctlypredicted as an IDH wildtype grade IV glioma This is patient TCGA-06-5408from the TCGA-GBM collection
Finally we considered two examples of scans that were incorrectly predictedby our method see Figure 7 These two examples were chosen because ournetwork assigned high prediction scores to the wrong classes for these casesFigure 7a shows an example of a grade II IDH mutated 1p19q co-deletedglioma that was predicted as grade IV IDH wildtype by our method Ourmethodrsquos prediction was most likely caused by the hyperintensities in the post-contrast T1w scan being interpreted as contrast enhancement Since these hy-perintensities are also present in the pre-contrast T1w scan they are most likelycalcifications and the radiological appearance of this tumor is indicative of anoligodendroglioma Figure 7b shows an example of a grade IV IDH wildtypeglioma that was predicted as a grade III IDH mutated glioma by our method
11
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) TCGA-DU-6410 from the TCGA-LGG collection The ground truth histopatho-logical analysis indicated this glioma was grade II IDH mutated 1p19q co-deletedbut our method predicted it as a grade IV IDH wildtype
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(b) TCGA-76-7664 from the TCGA-HGG collection Histopathologically this gliomawas grade IV IDH wildtype but our method predicted it as grade III IDH mutated
Figure 7 Examples of scans that were incorrectly predicted by our method
12
3 Discussion
We have developed a method that can predict the IDH mutation status 1p19qco-deletion status and grade of glioma while simultaneously providing the tu-mor segmentation based on pre-operative MRI scans For the genetic andhistological feature predictions we achieved an AUC of 090 for the IDH mu-tation status prediction an AUC of 085 for the 1p19q co-deletion predictionand an AUC of 081 for the grade prediction in the test set
In an independent test set which contained data from 13 different instituteswe demonstrated that our method predicts these features with good overall per-formance we achieved an AUC of 090 for the IDH mutation status predictionan AUC of 085 for the 1p19q co-deletion prediction and an AUC of 081 forthe grade prediction and a mean whole tumor DICE score of 084 This per-formance on unseen data that was only used during the final evaluation of thealgorithm and that was purposefully not used to guide any decisions regardingthe method design shows the true generalizability of our method Using thelatest GPU capabilities we were able to train a large model which uses thefull 3D scan as input Furthermore by using the largest most diverse patientcohort to date we were able to make our method robust to the heterogeneitythat is naturally present in clinical imaging data such that it generalizes forbroad application in clinical practice
By using a multi-task network our method could learn the context betweendifferent features For example IDH wildtype and 1p19q co-deletion are mu-tually exclusive [21] If two separate methods had been used one to predict theIDH status and one to predict the 1p19q co-deletion status an IDH wildtypeglioma might be predicted to be 1p19q co-deleted which does not stroke withthe clinical reality Since our method learns both of these genetic features si-multaneously it correctly learned not to predict 1p19q co-deletion in tumorsthat were IDH wildtype there was only one patient in which our algorithm pre-dicted a tumor to be both IDH wildtype and 1p19q co-deleted Furthermoreby predicting the genetic and histological features individually instead of onlypredicting the WHO 2016 category it is possible to adopt updated guidelinessuch as cIMPACT-NOW future-proofing our method [22]
Some previous studies also used multi-task networks to predict the geneticand histological features of glioma [23 24 25] Tang et al [23] used a multi-tasknetwork that predicts multiple genetic features as well as the overall survivalof glioblastoma Since their method only works for glioblastoma patients thetumor grade must be known in advance complicating the use of their methodin the pre-operative setting when tumor grade is not yet known Furthermoretheir method requires a tumor segmentation prior to application of their methodwhich is a time-consuming expert task In a study by Xue et al [24] a multi-task network was used with a structure similar to the one proposed in thispaper to segment the tumor and predict the grade (LGG or HGG) and IDHmutation status However they do not predict the 1p19q co-deletion statusneeded for the WHO 2016 categorization Lastly Decuyper et al [25] useda multi-task network that predicts the IDH mutation and 1p19q co-deletion
13
status and the tumor grade (LGG or HGG) Their method requires a tumorsegmentation as input which they obtain from a U-Net that is applied earlierin their pipeline thus their method requires two networks instead of the singlenetwork we use in our method These differences aside the most importantlimitation of each of these studies is the lack of an independent test set forevaluating their results It is now considered essential that an independent testset is used to prevent an overly optimistic estimate of a methodrsquos performance[15 26 27 28] Thus our study improves on this previous work by providinga single network that combines the different tasks being trained on a moreextensive and diverse dataset not requiring a tumor segmentation as an in-put providing all information needed for the WHO 2016 categorization andcrucially by being evaluated in an independent test set
An important genetic feature that is not predicted by our method is theO6-methylguanine-methyltransferase (MGMT) methylation status Althoughthe MGMT methylation status is not part of the WHO 2016 categorizationit is part of clinical management guidelines and is an important prognosticmarker in glioblastoma [4] In the initial stages of this study we attemptedto predict the MGMT methylation status however the performance of thisprediction was poor Furthermore the methylation cutoff level which is usedto determine whether a tumor is MGMT methylated shows a wide varietybetween institutes leading to inconsistent results [29] We therefore opted not toinclude the MGMT prediction at all rather than to provide a poor prediction ofan unsharply defined parameter Although some methods attempted to predictthe MGMT status with varying degrees of success there is still an ongoingdiscussion on the validity of MR imaging features of the MGMT status [23 3031 32 33]
Our method shows good overall performance but there are noticeable per-formance differences between tumor categories For example when our methodpredicts a tumor as an IDH wildtype glioblastoma it is correct almost all of thetime On the other hand it has some difficulty differentiating IDH mutated1p19q co-deleted low-grade glioma from other low-grade glioma The sensitiv-ity for the prediction of grade III glioma was low which might be caused bythe lack of a central pathology review Because of this there were differences inmolecular testing and histological analysis and it is known that distinguishingbetween grade II and grade III has a poor observer reliability [34] Althoughour method can be relevant for certain subgroups our methodrsquos performancestill needs to be improved to ensure relevancy for the full patient population
In future work we aim to increase the performance of our method by includ-ing perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI)since there has been an increasing amount of evidence that these physiologi-cal imaging modalities contain additional information that correlates with thetumorrsquos genetic status and aggressiveness [35 36] They were not included inthis study since PWI and to a lesser extent DWI are not as ingrained in theclinical imaging routine as the structural scans used in this work [19 20] Thusincluding these modalities would limit our methodrsquos clinical applicability andsubstantially reduce the number of patients in the train and test set However
14
PWI and DWI are increasingly becoming more commonplace which will allowincluding these in future research and which might improve performance
In conclusion we have developed a non-invasive method that can predict theIDH mutation status 1p19q co-deletion status and grade of glioma while atthe same time segmenting the tumor based on pre-operative MRI scans withhigh overall performance Although the performance of our method might needto be improved before it will find widespread clinical acceptance we believe thatthis research is an important step forward in the field of radiomics Predict-ing multiple clinical features simultaneously steps away from the conventionalsingle-task methods and is more in line with the clinical practice where multipleclinical features are considered simultaneously and may even be related Fur-thermore by not limiting the patient population used to develop our method toa selection based on clinical or radiological characteristics we alleviate the needfor a priori (expert) knowledge which may not always be available Althoughsteps still have to be taken before radiomics will find its way into the clinicespecially in terms of performance our work provides a crucial step forward byresolving some of the hurdles of clinical implementation now and paving theway for a full transition in the future
4 Methods
41 Patient population
The train set was collected from four in-house datasets and five publicly avail-able datasets In-house datasets were collected from four different institutesErasmus MC (EMC) Haaglanden Medical Center (HMC) Amsterdam UMC(AUMC) [37] and University Medical Center Utrecht (UMCU) Four of the fivepublic datasets were collected from The Cancer Imaging Archive (TCIA) [38]the Repository of Molecular Brain Neoplasia Data (REMBRANDT) collection[39] the Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multi-forme (CPTAC-GBM) collection [40] the Ivy Glioblastoma Atlas Project (IvyGAP) collection [41 42] and the Brain-Tumor-Progression collection [43] Thefifth dataset was the 2019 Brain Tumor Segmentation challenge (BraTS) chal-lenge dataset [44 45 46] from which we excluded the patients that were alsoavailable in the TCGA-LGG and TCGA-GBM collections [47 48]
For the internal datasets from the EMC and the HMC manual segmentationswere available which were made by four different clinical experts For patientswhere segmentations from more than one observer were available we randomlypicked one of the segmentations to use in the train set The segmentationsfrom the AUMC data were made by a single observer of the study by Visseret al [37] From the public datasets only the BraTS dataset and the Brain-Tumor-Progression dataset provided manual segmentations Segmentations ofthe BraTS dataset as provided in the 2019 training and validation set wereused For the Brain-Tumor-Progression dataset the segmentations as providedin the TCIA data collection were used
15
Patients were included if pre-operative pre- and post-contrast T1w T2wand T2w-FLAIR scans were available no further inclusion criteria were setFor example patients were not excluded based on the radiological characteristicsof the scan such as low imaging quality or imaging artifacts or the gliomarsquosclinical characteristics such as the grade If multiple scans of the same contrasttype were available in a single scan session (eg multiple T2w scans) the scanupon which the segmentation was made was selected If no segmentation wasavailable or the segmentation was not made based on that scan contrast thescan with the highest axial resolution was used where a 3D acquisition waspreferred over a 2D acquisition
For the in-house data genetic and histological data were available for theEMC HMC and UMCU dataset which were obtained from analysis of tumortissue after biopsy or resection Genetic and histological data of the publicdatasets were also available for the REMBRANDT CPTAC-GBM and IvyGAP collections Data for the REMBRANDT and CPTAC-GBM collectionswas collected from the clinical data available at the TCIA [40 39] For theIvy GAP collection the genetic and histological data were obtained from theSwedish Institute at httpsivygapswedishorghome
As a test set we used the TCGA-LGG and TCGA-GBM collections from theTCIA [47 48] Genetic and histological labels were obtained from the clinicaldata available at the TCIA Segmentations were used as available from theTCIA based on the 2018 BraTS challenge [45 49 50] The inclusion criteriafor the patients included in the BraTS challenge were the same as our inclusioncriteria the presence of a pre-operative pre- and post-contrast T1w T2w andT2w-FLAIR scan Thus patients from the TCGA-LGG and TCGA-GBM wereincluded if a segmentation from the BraTS challenge was available Howeverfor three patients we found that although they did have manual segmentationsthey did not meet our inclusion requirements TCGA-08-0509 and TCGA-08-0510 from TCGA-GBM because they did not have a pre-contrast T1w scan andTCGA-FG-7634 from TCGA-LGG because there was no post-contrast T1wscan
42 Automatic segmentation in the train set
To present our method with a large diversity in scans we wanted to include asmany patients in the train set as possible from the different datasets Thereforewe performed automatic segmentation in patients that did not have manualsegmentations To this end we used an initial version of our network (presentedin Section 44) without the additional layers that were needed for the predictionof the genetic and histological features This network was initially trained usingall patients in the train set for whom a manual segmentation was availableand this trained network was then applied to all patients for which a manualsegmentation was not available The resulting automatic segmentations wereinspected and if their quality was acceptable they were added to the trainset The network was then trained again using this increased dataset and wasapplied to scans that did not yet have a segmentation of acceptable quality
16
This process was repeated until an acceptable segmentation was available forall patients which constituted our final complete train set
43 Pre-processing
For all datasets except for the BraTS dataset for which the scans were alreadyprovided in NIfTI format the scans were converted from DICOM format toNIfTI format using dcm2niix version v1020190410 [51] We then registeredall scans to the MNI152 T1w and T2w atlases version ICBM 2009a whichhad a resolution of 1x1x1 mm3 and a size of 197x233x189 voxels [52 53] Thescans were affinely registered using Elastix 50 [54 55] The pre- and post-contrast T1w scans were registered to the T1w atlas the T2w and T2w-FLAIRscans were registered to the T2w atlas When a manual segmentation wasavailable for patients from the in-house datasets the registration parametersthat resulted from registering the scan used during the segmentation were usedto transform the segmentation to the atlas In the case of the public datasetswe used the registration parameters of the T2w-FLAIR scans to transform thesegmentations
After the registration all scans were N4 bias field corrected using SimpleITKversion 124 [56] A brain mask was made for the atlas using HD-BET both forthe T1w atlas and the T2w atlas [57] This brain mask was used to skull stripall registered scans and crop them to a bounding box around the brain maskreducing the amount of background present in the scans resulting in a scan sizeof 152x182x145 voxels Subsequently the scans were normalized such that foreach scan the average image intensity was 0 and the standard deviation of theimage intensity was 1 within the brain mask Finally the background outsidethe brain mask was set to the minimum intensity value within the brain mask
Since the segmentation could sometimes be rugged at the edges after regis-tration especially when the segmentations were initially made on low-resolutionscans we smoothed the segmentation using a 3x3x3 median filter (this was onlydone in the train set) For segmentations that contained more than one la-bel eg when the tumor necrosis and enhancement were separately segmentedall labels were collapsed into a single label to obtain a single segmentation ofthe whole tumor The genetic and histological labels and the segmentations ofeach patient were one-hot encoded The four scans ground truth labels andsegmentation of each patient were then used as the input to the network
44 Model
We based the architecture of our model on the U-Net architecture with someadaptations made to allow for a full 3D input and the auxiliary tasks [58] Ournetwork architecture which we have named PrognosAIs Structure-Net or PS-Net for short can be seen in Figure 8
To use the full 3D scan as an input to the network we replaced the firstpooling layer that is usually present in the U-Net with a strided convolutionwith a kernel size of 9x9x9 and a stride of 3x3x3 In the upsampling branch of
17
32
32 64
128
256
512 256
7x8x7256 128
128 64
64 32
32 2
Segmentation
145x182x152
49x61x51
25x31x26
13x16x13
1472
512 2IDH
512 2
1p19q
512 3Grade
Batch normalization Concatenation Convolution amp ReLU3x3x3
Convolution amp Softmax1x1x1
(De)convolution amp ReLU9x9x9
stride 3x3x3
Dense amp ReLU Dense amp Softmax Dropout
Max pooling2x2x2
Up-convolution amp ReLU2x2x2
Global maxpooling
Figure 8 Overview of the PrognosAIs Structure-Net (PS-Net) architecture usedfor our model The numbers below the different layers indicate the number offilters dense units or features at that layer We have also indicated the featuremap size at the different depths of the network
the network the last up-convolution is replaced by a deconvolution with thesame kernel size and stride
At each depth of the network we have added global max-pooling layersdirectly after the dropout layer to obtain imaging features that can be used topredict the genetic and histological features We chose global pooling layers asthey do not introduce any additional parameters that need to be trained thuskeeping the memory required by our model manageable The features from thedifferent depths of the network were concatenated and fed into three differentdense layers one for each of the genetic and histological outputs
l2 kernel regularization was used in all convolutional layers except for thelast convolutional layer used for the output of the segmentation In total thismodel contained 27042473 trainable an 2944 non-trainable parameters
18
45 Model training
Training of the model was done on eight NVidia RTX2080Tirsquos with 11GB ofmemory using TensorFlow 220 [59] To be able to use the full 3D scan asinput to the network without running into memory issues we had to optimizethe memory efficiency of the model training Most importantly we used mixed-precision training which means that most of the variables of the model (such asthe weights) were stored in float16 which requires half the memory of float32which is typically used to store these variables [60] Only the last softmaxactivation layers of each output were stored as float32 We also stored ourpre-processed scans as float16 to further reduce memory usage
However even with these settings we could not use a batch size larger than1 It is known that a larger batch size is preferable as it increases the stabilityof the gradient updates and allows for a better estimation of the normalizationparameters in batch normalization layers [61] Therefore we distributed thetraining over the eight GPUs using the NCCL AllReduce algorithm whichcombines the gradients calculated on each GPU before calculating the updateto the model parameters [62] We also used synchronized batch normalizationlayers which synchronize the updates of their parameters over the distributedmodels In this way our model had a virtual batch size of eight for the gradientupdates and the batch normalization layers parameters
To provide more samples to the algorithm and prevent potential overtrain-ing we applied four types of data augmentation during training croppingrotation brightness shifts and contrast shifts Each augmentation was appliedwith a certain augmentation probability which determined the probability ofthat augmentation type being applied to a specific sample When an image wascropped a random number of voxels between 0 and 20 was cropped from eachdimension and filled with zeros For the random rotation an angle betweenminus30 and 30 degrees was selected from a uniform distribution for each dimen-sion The brightness shift was applied with a delta uniformly drawn between0 and 02 and the contrast shift factor was randomly drawn between 085 and115 We also introduced an augmentation factor which determines how ofteneach sample was parsed as an input sample during a single epoch where eachtime it could be augmented differently
For the IDH 1p19q and grade output we used a masked categorical cross-entropy loss and for the segmentation we used a DICE loss see Appendix E fordetails We used AdamW as an optimizer which has shown improved general-ization performance over Adam by introducing the weight decay parameter as aseparate parameter from the learning rate [63] The learning rate was automat-ically reduced by a factor of 025 if the loss did not improve during the last fiveepochs with a minimum learning rate of 1 middot 10minus11 The model could train for amaximum of 150 epochs and training was stopped early if the average loss overthe last five epochs did not improve Once the model was finished training theweights from the epoch with the lowest loss were restored
19
46 Hyperparameter tuning
Hyperparameters involved in the training of the model needed to be tuned toachieve the best performance We tuned a total of six hyper parameters the l2-norm the dropout rate the augmentation factor the augmentation probabilitythe optimizerrsquos initial learning rate and the optimizerrsquos weight decay A fulloverview of the trained parameters and the values tested for the different settingsis presented in Appendix F
To tune these hyperparameters we split the train set into a hyperparametertraining set (851282 patients of the full train data) and a hyperparametervalidation set (15226 patients of the full train data) Models were trained fordifferent hyperparameter settings via an exhaustive search using the hyperpa-rameter train set and then evaluated on the hyperparameter validation set Nodata augmentation was applied to the hyperparameter validation to ensure thatresults between trained models were comparable The hyperparameters that ledto the lowest overall loss in the hyperparameter validation set were chosen asthe optimal hyperparameters We trained the final model using these optimalhyperparameters and the full train set
47 Post-processing
The predictions of the network were post-processed to obtain the final predictedlabels and segmentations for the samples Since softmax activations were usedfor the genetic and histological outputs a prediction between 0 and 1 was out-putted for each class where the individual predictions summed to 1 The finalpredicted label was then considered as the class with the highest prediction scoreFor the prediction of LGG (grade IIIII) vs HGG (grade IV) the predictionscores of grade II and grade III were combined to obtain the prediction scorefor LGG the prediction score of grade IV was used as the prediction score forHGG If a segmentation contained multiple unconnected components we onlyretained the largest component to obtain a single whole tumor segmentation
48 Model evaluation
The performance of the final trained model was evaluated on the independenttest set comparing the predicted labels with the ground truth labels For thegenetic and histological features we evaluated the AUC the accuracy the sen-sitivity and the specificity using scikit-learn version 0231 for details see Ap-pendix G [64] We evaluated these metrics on the full test set and in subcate-gories relevant to the WHO 2016 guidelines We evaluated the IDH performanceseparately in the LGG (grade IIIII) and HGG (grade IV) subgroups the 1p19qperformance in LGG and we also evaluated the performance of distinguishingbetween LGG and HGG instead of predicting the individual grades
To evaluate the performance of the segmentation we calculated the DICEscores Hausdorff distances and volumetric similarity coefficient comparing theautomatic segmentation of our method and the manual ground truth segmen-
20
tations for all patients in the test set These metrics were calculated usingthe EvaluateSegmentation toolbox version 20170425 [65] for details see Ap-pendix G
To prevent an overly optimistic estimation of our modelrsquos predictive valuewe only evaluated our model on the test set once all hyperparameters werechosen and the final model was trained In this way the performance in thetest set did not influence decisions made during the development of the modelpreventing possible overfitting by fine-tuning to the test set
To gain insight into the model we made saliency maps that show whichparts of the scan contribute the most to the prediction of the CNN [66] Saliencymaps were made using tf-keras-vis 052 changing the activation function of alloutput layers from softmax to linear activations using SmoothGrad to reducethe noisiness of the saliency maps [66]
Another way to gain insight into the networkrsquos behavior is to visualize thefilter outputs of the convolutional layers as they can give some idea as to whatoperations the network applies to the scans We visualized the filter outputs ofthe last convolutional layers in the downsample and upsample path at the firstdepth (at an image size of 49x61x51) of our network These filter outputs werevisualized by passing a sample through the network and showing the convolu-tional layersrsquo outputs replacing the ReLU activation with linear activations
49 Data availability
An overview of the patients included from the public datasets used in the train-ing and testing of the algorithm and their ground truth label is available inAppendix H The data from the public datasets are available in TCIA un-der DOIs 107937K9TCIA2015588OZUZB 107937k9tcia20183rje41q1107937K9TCIA2016XLwaN6nL and 107937K9TCIA201815quzvnb Datafrom the BraTS are available at httpbraintumorsegmentationorg Datafrom the in-house datasets are not publicly available due to participant privacyand consent
410 Code availability
The code used in this paper is available on GitHub under an Apache 2 license athttpsgithubcomSvdvoortPrognosAIs_glioma This code includes thefull pipeline from registration of the patients to the final post-processing of thepredictions The trained model is also available on GitHub along with code toapply it to new patients
21
Appendices
A Confusion matrices
Tables 3 4 and 5 show the confusion matrices for the IDH 1p19q and gradepredictions and Table 6 shows the confusion matrix for the WHO 2016 subtypes
Table 4 shows that the algorithm mainly has difficulty recognizing 1p19qco-deleted tumors which are mostly predicted as 1p19q intact Table 5 showsthat most of the incorrectly predicted grade III tumors are predicted as gradeIV tumors
Table 6 shows that our algorithm often incorrectly predicts IDH-wildtypeastrocytoma as IDH-wildtype glioblastoma The latest cIMPACT-NOW guide-lines propose a new categorization in which IDH-wildtype astrocytoma thatshow either TERT promoter methylation or EFGR gene amplification or chro-mosome 7 gainchromsome 10 loss are classified as IDH-wildtype glioblastoma[22] This new categorization is proposed since the survival of patients withthose IDH-wildtype astrocytoma is similar to the survival of patients with IDH-wildtype glioblastoma [22] From the 13 IDH-wildtype astrocytoma that werewrongly predicted as IDH-wildtype glioblastoma 12 would actually be cate-gorized as IDH-wildtype glioblastoma under this new categorization Thusalthough our method wrongly predicted the WHO 2016 subtype it might ac-tually have picked up on imaging features related to the aggressiveness of thetumor which might lead to a better categorization
Table 3 Confusion matrix of the IDH predictions
Predicted
Wildtype Mutated
Actu
al
Wildtype 120 9
Mutated 25 63
Table 4 Confusion matrix of the 1p19q predictions
Predicted
Intact Co-deleted
Actu
al
Intact 197 10
Co-deleted 16 10
22
Table 5 Confusion matrix of the grade predictions
Predicted
Grade II Grade III Grade IV
Actu
al Grade II 35 6 6
Grade III 19 10 30
Grade IV 2 5 125
Table 6 Confusion matrix of the WHO 2016 predictions The rsquootherrsquo categoryindicates patients that were predicted as a non-existing WHO 2016 subtype forexample IDH wildtype 1p19q co-deleted tumors Only one patient (TCGA-HT-A5RC) was predicted as a non-existing category It was predicted as anIDH wildtype 1p19q co-deleted grade IV tumor
Predicted
Oligodendrogliom
a
IDH-m
utated
astrocytoma
IDH-w
ildtype
astrocytoma
IDH-m
utated
glioblastoma
IDH-w
ildtype
glioblastoma
Other
Actu
al
Oligodendroglioma 10 8 1 0 7 0
IDH-mutatedastrocytoma 6 34 4 3 10 0
IDH-wildtypeastrocytoma 1 2 3 2 13 1
IDH-mutatedglioblastoma 0 1 0 0 3 0
IDH-wildtypeglioblastoma 0 3 3 1 96 0
Oligodendroglioma are IDH-mutated 1p19q co-deleted grade IIIII giomaIDH-mutated astrocytoma are IDH-mutated 1p19q intact grade IIIII gliomaIDH-wildtype astrocytoma are IDH-wildtype 1p19q intact grade IIIII gliomaIDH-mutated glioblastoma are IDH-mutated grade IV gliomaIDH-wildtype glioblastoma are IDH-wildtype grade IV glioma
23
B Segmentation examples
To demonstrate the automatic segmentations made by our method we randomlyselected five patients from both the TCGA-LGG and the TCGA-GBM datasetThe scans and segmentations of the five patients from the TCGA-LGG datasetand the TCGA-GBM dataset are shown in Figures 9 and 10 respectively TheDICE score Hausdorff distance and volumetric similarity coefficient for thesepatients are given in Table 7 The method seems to mostly focus on the hyper-intensities of the T2w-FLAIR scan Despite the registrations issues that can beseen for the T2w scan in Figure 10d the tumor was still properly segmenteddemonstrating the robustness of our method
Patient DICE HD (mm) VSC
TCGA-LGG
TCGA-DU-7301 089 103 095TCGA-FG-5964 080 58 082TCGA-FG-A713 073 78 088TCGA-HT-7475 087 149 090TCGA-HT-8106 088 112 099
TCGA-GBM
TCGA-02-0037 082 226 099TCGA-08-0353 091 130 098TCGA-12-1094 090 73 093TCGA-14-3477 090 165 099TCGA-19-5951 073 197 073
Table 7 The DICE score Hausdorff distance (HD) and volumetric similaritycoefficient (VSC) for the randomly selected patients from the TCGA-LGG andTCGA-GBM data collections
24
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-DU-7301 from the TCGA-LGG data collection
(b) Patient TCGA-FG-5964 from the TCGA-LGG data collection
(c) Patient TCGA-FG-A713 from the TCGA-LGG data collection
(d) Patient TCGA-HT-7475 from the TCGA-LGG data collection
(e) Patient TCGA-HT-8106 from the TCGA-LGG data collection
Figure 9 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-LGG data collection
25
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Patient TCGA-02-0037 from the TCGA-GBM data collection
(b) Patient TCGA-08-0353 from the TCGA-GBM data collection
(c) Patient TCGA-12-1094 from the TCGA-GBM data collection
(d) Patient TCGA-14-3477 from the TCGA-GBM data collection
(e) Patient TCGA-19-5951 from the TCGA-GBM data collection
Figure 10 Examples of scans and automatic segmentations of five patients thatwere randomly selected from the TCGA-GBM data collection
26
C Prediction results in the test set
27
D Filter output visualizations
Figures 11 and 12 show the output of the convolution filters for the same LGGpatient as shown in Figure 5a and Figures 13 and 14 show the output of theconvolution filters for the same HGG patient as shown in Figure 5b Figures 11and 13 show the outputs of the last convolution layer in the downsample pathat the feature size of 49x61x51 (the fourth convolutional layer in the network)Figures 12 and 14 show the outputs of the last convolution layer in the upsamplepath at the feature size of 49x61x51 (the nineteenth convolutional layer in thenetwork)
Comparing Figure 11 to Figure 12 and Figure 13 to Figure 14 we can seethat the convolutional layers in the upsample path do not keep a lot of detailfor the healthy part of the brain as this region seems blurred However withinthe tumor different regions can still be distinguished The different parts ofthe tumor from the scans can also be seen such as the contrast-enhancing partand the high signal intensity on the T2w-FLAIR For the grade IV glioma inFigure 14 some filters such as filter 26 also seem to focus on the necrotic partof the tumor
28
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 11 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
29
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 12 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-DU-6400 This is an IDH mutated 1p19q co-deleted grade II glioma
30
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 13 Filter output visualizations of the last convolutional layer in thedownsample path of the network at feature map size 49x61x51 for patientTCGA-06-0238 This is an IDH wildtype grade IV glioma
31
Pre-contrast T1w Post-contrast T1w T2w T2w-FLAIR
(a) Scans used to derive the convolutional layer filter output visualizations
Filter 1Filter 1 Filter 2Filter 2 Filter 3Filter 3 Filter 4Filter 4 Filter 5Filter 5 Filter 6Filter 6 Filter 7Filter 7 Filter 8Filter 8
Filter 9Filter 9 Filter 10Filter 10 Filter 11Filter 11 Filter 12Filter 12 Filter 13Filter 13 Filter 14Filter 14 Filter 15Filter 15 Filter 16Filter 16
Filter 17Filter 17 Filter 18Filter 18 Filter 19Filter 19 Filter 20Filter 20 Filter 21Filter 21 Filter 22Filter 22 Filter 23Filter 23 Filter 24Filter 24
Filter 25Filter 25 Filter 26Filter 26 Filter 27Filter 27 Filter 28Filter 28 Filter 29Filter 29 Filter 30Filter 30 Filter 31Filter 31 Filter 32Filter 32
Filter 33Filter 33 Filter 34Filter 34 Filter 35Filter 35 Filter 36Filter 36 Filter 37Filter 37 Filter 38Filter 38 Filter 39Filter 39 Filter 40Filter 40
Filter 41Filter 41 Filter 42Filter 42 Filter 43Filter 43 Filter 44Filter 44 Filter 45Filter 45 Filter 46Filter 46 Filter 47Filter 47 Filter 48Filter 48
Filter 49Filter 49 Filter 50Filter 50 Filter 51Filter 51 Filter 52Filter 52 Filter 53Filter 53 Filter 54Filter 54 Filter 55Filter 55 Filter 56Filter 56
Filter 57Filter 57 Filter 58Filter 58 Filter 59Filter 59 Filter 60Filter 60 Filter 61Filter 61 Filter 62Filter 62 Filter 63Filter 63 Filter 64Filter 64
(b) Filter output visualizations
Figure 14 Filter output visualizations of the last convolutional layer in theupsample path of the network at feature map size 49x61x51 for patient TCGA-06-0238 This is an IDH wildtype grade IV glioma
32
E Training losses
During the training of the network we used masked categorical cross-entropy lossfor the IDH 1p19q and grade outputs The normal categorical cross-entropyloss is defined as
LCEbatch = minus 1
Nbatch
sumj
sumiisinC
yij log (yij) (1)
where LCEbatch is the total cross-entropy loss over a batch yij is the ground truth
label of sample j for class i yij is the prediction score for sample j for class iC is the set of classes and Nbatch is the number of samples in the batch Here itis assumed that the ground truth labels are one-hot-encoded thus yij is either0 or 1 for each class In our case the ground truth is not known for all sampleswhich can be incorporated in Equation (1) by setting yij to 0 for all classesfor a sample for which the ground truth is not known That sample wouldthen not contribute to the overall loss and would not contribute to the gradientupdate However this can skew the total loss over a batch since the loss is stillaveraged over the total number of samples in a batch regardless of whether theground truth is known resulting in a lower loss for batches that contained moresamples with unknown ground truth Therefore we used a masked categoricalcross-entropy loss
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
yij log (yij) (2)
where
microbatchj =
Nbatchsumij yij
sumi
yij (3)
is the batch weight for sample j In this way the total batch loss is only averagedover the samples that actually have a ground truth
Since there was an imbalance between the number of ground truth samplesfor each class we used class weights to compensate for this imbalance Thusthe loss becomes
LCEbatch = minus 1
Nbatch
sumj
microbatchj
sumiisinC
microclassi yij log (yij) (4)
where
microclassi =
N
Ni |C|(5)
is the class weight for class i N is the total number of samples with knownground truth Ni is the number of samples of class i and |C| is the number ofclasses By determining the class weight in this way we ensured that
microclassi Ni =
N
|C|= constant (6)
33
Thus each class would have the same contribution to the overall loss Theseclass weights were (individually) determined for the IDH output the 1p19qoutput and the grade output
For the segmentation output we used the DICE loss
LDICEbatch =
sumj
1minus 2 middotsumvoxels
k yjk middot yjksumvoxelsk yjk + yjk
(7)
where yjk is the ground truth label in voxel k of sample j and yjk is theprediction score outputted for voxel k of sample j
The total loss that was optimized for the model was a weighted sum of thefour individual losses
Ltotal =summ
micromLm (8)
with
microm =1
Xm (9)
where Lm is the loss for output m microm is the loss weight for loss m (either theIDH 1p19q grade or segmentation loss) and Xm is the number of sampleswith known ground truth for output m In this way we could counteract theeffect of certain outputs having more known labels than other outputs
34
F Parameter tuning
Table 8 Hyperparameters that were tuned and the values that were testedValues in bold show the selected values used in the final model
Tuning parameter Tested values
Dropout rate 015 02 025 030 035 040l2-norm 00001 000001 0000001Learning rate 001 0001 00001 000001 00000001Weight decay 0001 00001 000001Augmentation factor 1 2 3Augmentation probability 025 030 035 040 045
35
G Evaluation metrics
We calculated the AUC accuracy sensitivity and specificity metrics for thegenetic and histological features for the definitions of these metrics see [67]
For the IDH and 1p19q co-deletion outputs the IDH mutated and the1p19q co-deleted samples were regarded as the positive class respectively Sincethe grade was a multi-class problem no single positive class could be determinedFor the prediction of the individual grades that grade was seen as the positiveclass and all other grades as the negative class (eg in the case of the gradeIII prediction grade III was regarded as the positive class and grade II and IVwere regarded as the negative class) For the LGG vs HGG prediction LGGwas considered as the positive class and HGG as the negative class For theevaluation of these metrics for the genetic and histological features only thesubjects with known ground truth were taken into account
The overall AUC for the grade was a multi-class AUC determined in a one-vs-one approach comparing each class against the others in this way thismetric was insensitive to class imbalance [68] A multi-class accuracy was usedto determine the overall accuracy for the grade predictions [67]
To evaluate the performance of the automated segmentation we evaluatedthe DICE score the Hausdorff distance and the volumetric similarity coefficientThe DICE score is a measure of overlap between two segmentations where avalue of 1 indicates perfect overlap and the Hausdorff distance is a measureof the closeness of the borders of the segmentations The volumetric similaritycoefficient is a measure of the agreement between the volumes of two segmen-tations without taking account the actual location of the tumor where a valueof 1 indicates perfect agreement See [65] for the definitions of these metrics
36
H Ground truth labels of patients included frompublic datasets
Acknowledgments
Sebastian van der Voort and Fatih Incekara acknowledge funding by the DutchCancer Society (KWF project number EMCR 2015-7859)
Data used in this publication were generated by the National Cancer Insti-tute Clinical Proteomic Tumor Analysis Consortium (CPTAC)
The results published here are in whole or part based upon data generatedby the TCGA Research Network httpcancergenomenihgov
Author contributions
SRvdV FI WJN MS and SK contrived the study and designed theexperiments FI MMJW JWS RNT GJL PCDWH RSEAJPEV MJvdB and MS included patients in the different studiesSRvdV FI MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV MJvdB and MS collected the dataSRvdV carried out the experiments SRvdV FI MS and SK inter-preted the results SRvdV FI MJvdB MS and SK created the initialdraft of the paper MMJW GK RG JWS RNT GJL PCDWHRSE PJF HJD AJPEV WJN and MJvdB revised the paper
References
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[2] Hendrikus J Dubbink Peggy N Atmodimedjo Johan M Kros Pim JFrench Marc Sanson Ahmed Idbaih Pieter Wesseling Roelien EntingWim Spliet Cees Tijssen Winand N M Dinjens Thierry Gorlia andMartin J van den Bent Molecular classification of anaplastic oligoden-droglioma using next-generation sequencing a report of the prospectiverandomized EORTC brain tumor group 26951 phase III trial Neuro-Oncology 18(3)388ndash400 March 2016 ISSN 1522-8517 URL https
doiorg101093neuoncnov182
[3] Jeanette E Eckel-Passow Daniel H Lachance Annette M Molinaro Kyle MWalsh Paul A Decker Hugues Sicotte Melike Pekmezci Terri Rice Matt LKosel Ivan V Smirnov Gobinda Sarkar Alissa A Caron Thomas M
37
Kollmeyer Corinne E Praska Anisha R Chada Chandralekha Halder He-len M Hansen Lucie S McCoy Paige M Bracci Roxanne Marshall ShichunZheng Gerald F Reis Alexander R Pico Brian P OrsquoNeill Jan C BucknerCaterina Giannini Jason T Huse Arie Perry Tarik Tihan Mitchell SBerger Susan M Chang Michael D Prados Joseph Wiemels John KWiencke Margaret R Wrensch and Robert B Jenkins Glioma groupsbased on 1p19q IDH and TERT promoter mutations in tumors NewEngland Journal of Medicine 372(26)2499ndash2508 June 2015 ISSN 0028-4793 URL httpsdoiorg101056NEJMoa1407279
[4] David N Louis Arie Perry Guido Reifenberger Andreas von Deimling Do-minique Figarella-Branger Webster K Cavenee Hiroko Ohgaki Otmar DWiestler Paul Kleihues and David W Ellison The 2016 world health orga-nization classification of tumors of the central nervous system a summaryActa Neuropathologica 131(6)803ndash820 June 2016 ISSN 0001-6322 URLhttpsdoiorg101007s00401-016-1545-1
[5] Ching-Chang Chen Peng-Wei Hsu Tai-Wei Erich Wu Shih-Tseng LeeChen-Nen Chang Kuo-chen Wei Chih-Cheng Chuang Chieh-Tsai WuTai-Ngar Lui Yung-Hsin Hsu Tzu-Kang Lin Sai-Cheung Lee and Yin-Cheng Huang Stereotactic brain biopsy Single center retrospective anal-ysis of complications Clinical Neurology and Neurosurgery 111(10)835ndash839 December 2009 ISSN 0303-8467 URL httpsdoiorg101016
jclineuro200908013
[6] Robert J Jackson Gregory N Fuller Dima Abi-Said Frederick F LangZiya L Gokaslan Wei Ming Shi David M Wildrick and Raymond SawayaLimitations of stereotactic biopsy in the initial management of gliomasNeuro-Oncology 3(3)193ndash200 July 2001 ISSN 1522-8517 URL https
doiorg101093neuonc33193
[7] M Zhou J Scott B Chaudhury L Hall D Goldgof KW Yeom M IvY Ou J Kalpathy-Cramer S Napel R Gillies O Gevaert and R GatenbyRadiomics in brain tumor Image assessment quantitative feature de-scriptors and machine-learning approaches American Journal of Neu-roradiology 39(2)208ndash216 February 2018 ISSN 0195-6108 URL https
doiorg103174ajnrA5391
[8] Wenya Linda Bi Ahmed Hosny Matthew B Schabath Maryellen LGiger Nicolai J Birkbak Alireza Mehrtash Tavis Allison Omar ArnaoutChristopher Abbosh Ian F Dunn Raymond H Mak Rulla M TamimiClare M Tempany Charles Swanton Udo Hoffmann Lawrence H SchwartzRobert J Gillies Raymond Y Huang and Hugo J W L Aerts Artificialintelligence in cancer imaging Clinical challenges and applications CAA Cancer Journal for Clinicians 69(2)caac21552 February 2019 ISSN0007-9235 URL httpsdoiorg103322caac21552
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[9] Ahmad Chaddad Michael Jonathan Kucharczyk Paul Daniel SihamSabri Bertrand J Jean-Claude Tamim Niazi and Bassam AbdulkarimRadiomics in glioblastoma Current status and challenges facing clinicalimplementation Frontiers in Oncology 9374ndash374 May 2019 ISSN 2234-943X URL httpsdoiorg103389fonc201900374
[10] Marion Smits Imaging of oligodendroglioma The British Journal of Ra-diology 89(1060)20150857 April 2016 ISSN 0007-1285 URL https
doiorg101259bjr20150857
[11] Rachel L Delfanti David E Piccioni Jason Handwerker Naeim BahramiAnithaPriya Krishnan Roshan Karunamuni Jona A Hattangadi-GluthTyler M Seibert Ashwin Srikant Karra A Jones Vivian S Snyder An-ders M Dale Nathan S White Carrie R McDonald and Nikdokht FaridImaging correlates for the 2016 update on WHO classification of gradeIIIII gliomas implications for IDH 1p19q and ATRX status Journal ofNeuro-Oncology 135(3)601ndash609 December 2017 ISSN 0167-594X URLhttpsdoiorg101007s11060-017-2613-7
[12] Sonal Gore Tanay Chougule Jayant Jagtap Jitender Saini and MadhuraIngalhalikar A review of radiomics and deep predictive modeling in gliomacharacterization Academic Radiology July 2020 ISSN 1076-6332 URLhttpsdoiorg101016jacra202006016
[13] Hugo J W L Aerts Emmanuel Rios Velazquez Ralph T H LeijenaarChintan Parmar Patrick Grossmann Sara Carvalho Johan BussinkRene Monshouwer Benjamin Haibe-Kains Derek Rietveld Frank HoebersMichelle M Rietbergen C Rene Leemans Andre Dekker John Quacken-bush Robert J Gillies and Philippe Lambin Decoding tumour pheno-type by noninvasive imaging using a quantitative radiomics approach Na-ture Communications 5(1)4006 September 2014 ISSN 2041-1723 URLhttpsdoiorg101038ncomms5006
[14] Sarah Chihati and Djamel Gaceb A review of recent progress in deeplearning-based methods for MRI brain tumor segmentation In 202011th International Conference on Information and Communication Sys-tems (ICICS) pages 149ndash154 Institute of Electrical and Electronics Engi-neers (IEEE) April 2020 ISBN 9781728162270 URL httpsdoiorg
101109icics494692020239550
[15] Robert J Gillies Paul E Kinahan and Hedvig Hricak Radiomics Imagesare more than pictures they are data Radiology 278(2)563ndash577 Febru-ary 2016 ISSN 0033-8419 URL httpsdoiorg101148radiol
2015151169
[16] James H Thrall Xiang Li Quanzheng Li Cinthia Cruz Synho Do KeithDreyer and James Brink Artificial intelligence and machine learning in ra-diology Opportunities challenges pitfalls and criteria for success Journal
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of the American College of Radiology 15(3 Part B)504ndash508 March 2018ISSN 1546-1440 URL httpsdoiorg101016jjacr201712026
[17] Ahmed Hosny Chintan Parmar John Quackenbush Lawrence H Schwartzand Hugo J W L Aerts Artificial intelligence in radiology Nature ReviewsCancer 18(8)500ndash510 August 2018 ISSN 1474-175X URL https
doiorg101038s41568-018-0016-5
[18] Okan Kopuklu Neslihan Kose Ahmet Gunduz and Gerhard Rigoll Re-source efficient 3D convolutional neural networks In 2019 IEEECVFInternational Conference on Computer Vision Workshop (ICCVW) Insti-tute of Electrical and Electronics Engineers (IEEE) October 2019 ISBN9781728150239 URL httpsdoiorg101109iccvw201900240
[19] S C Thust S Heiland A Falini H R Jager A D Waldman P C SundgrenC Godi V K Katsaros A Ramos N Bargallo M W Vernooij T YousryM Bendszus and M Smits Glioma imaging in europe A survey of 220centres and recommendations for best clinical practice European Radiology28(8)3306ndash3317 August 2018 ISSN 0938-7994 URL httpsdoiorg
101007s00330-018-5314-5
[20] Christian F Freyschlag Sandro M Krieg Johannes Kerschbaumer DanielPinggera Marie-Therese Forster Dominik Cordier Marco Rossi GabrieleMiceli Alexandre Roux Andres Reyes Silvio Sarubbo Anja Smits JoannaSierpowska Pierre A Robe Geert-Jan Rutten Thomas Santarius TomaszMatys Marc Zanello Fabien Almairac Lydiane Mondot Asgeir S JakolaMaria Zetterling Adria Rofes Gord von Campe Remy Guillevin DanieleBagatto Vincent Lubrano Marion Rapp John Goodden Philip C De WittHamer Johan Pallud Lorenzo Bello Claudius Thome Hugues Duffauand Emmanuel Mandonnet Imaging practice in low-grade gliomas amongeuropean specialized centers and proposal for a minimum core of imagingJournal of Neuro-Oncology 139(3)699ndash711 September 2018 ISSN 0167-594X URL httpsdoiorg101007s11060-018-2916-3
[21] M Labussiere A Idbaih X-W Wang Y Marie B Boisselier C FaletS Paris J Laffaire C Carpentier E Criniere F Ducray S El HallaniK Mokhtari K Hoang-Xuan J-Y Delattre and M Sanson All the 1p19qcodeleted gliomas are mutated on IDH1 or IDH2 Neurology 74(23)1886ndash1890 June 2010 ISSN 0028-3878 URL httpsdoiorg101212WNL
0b013e3181e1cf3a
[22] David N Louis Pieter Wesseling Kenneth Aldape Daniel J Brat DavidCapper Ian A Cree Charles Eberhart Dominique Figarella-BrangerMaryam Fouladi Gregory N Fuller Caterina Giannini Christine Haber-ler Cynthia Hawkins Takashi Komori Johan M Kros HK Ng Brent AOrr Sung-Hye Park Werner Paulus Arie Perry Torsten Pietsch GuidoReifenberger Marc Rosenblum Brian Rous Felix Sahm Chitra SarkarDavid A Solomon Uri Tabori Martin J Bent Andreas Deimling Michael
40
Weller Valerie A White and David W Ellison cIMPACT-NOW up-date 6 new entity and diagnostic principle recommendations of thecIMPACT-Utrecht meeting on future CNS tumor classification and grad-ing Brain Pathology 30(4)844ndash856 July 2020 ISSN 1015-6305 URLhttpsdoiorg101111bpa12832
[23] Zhenyu Tang Yuyun Xu Zhicheng Jiao Junfeng Lu Lei Jin Abudumi-jiti Aibaidula Jinsong Wu Qian Wang Han Zhang and Dinggang ShenPre-operative overall survival time prediction for glioblastoma patients us-ing deep learning on both imaging phenotype and genotype In Ding-gang Shen Tianming Liu Terry M Peters Lawrence H Staib CarolineEssert Sean Zhou Pew-Thian Yap and Ali Khan editors Lecture Notesin Computer Science pages 415ndash422 Springer Science and Business Me-dia LLC 2019 ISBN 9783030322380 URL httpsdoiorg101007
978-3-030-32239-7_46
[24] Zhiyuan Xue Bowen Xin Dingqian Wang and Xiuying Wang Radiomics-enhanced multi-task neural network for non-invasive glioma subtypingand segmentation In Hassan Mohy-ud Din and Saima Rathore editorsRadiomics and Radiogenomics in Neuro-oncology pages 81ndash90 SpringerScience and Business Media LLC 2020 ISBN 9783030401238 URLhttpsdoiorg101007978-3-030-40124-5_9
[25] Milan Decuyper Stijn Bonte Karel Deblaere and Roel Van Holen Au-tomated MRI based pipeline for glioma segmentation and prediction ofgrade IDH mutation and 1p19q co-deletion 2020 Preprint at https
arxivorgabs200511965
[26] Stefania Rizzo Francesca Botta Sara Raimondi Daniela Origgi Cris-tiana Fanciullo Alessio Giuseppe Morganti and Massimo Bellomi Ra-diomics the facts and the challenges of image analysis European Ra-diology Experimental 2(1)36 December 2018 ISSN 2509-9280 URLhttpsdoiorg101186s41747-018-0068-z
[27] Philipp Lohmann Norbert Galldiks Martin Kocher Alexander HeinzelChristian P Filss Carina Stegmayr Felix M Mottaghy Gereon R FinkN Jon Shah and Karl-Josef Langen Radiomics in neuro-oncology Basicsworkflow and applications Methods June 2020 ISSN 1046-2023 URLhttpsdoiorg101016jymeth202006003
[28] Stephen S F Yip and Hugo J W L Aerts Applications and limitationsof radiomics Physics in Medicine and Biology 61(13)R150ndashR166 July2016 ISSN 0031-9155 URL httpsdoiorg1010880031-915561
13r150
[29] Annika Malmstrom Ma lgorzata Lysiak Bjarne Winther Kristensen Eliz-abeth Hovey Roger Henriksson and Peter Soderkvist Do we really knowwho has an MGMT methylated glioma Results of an international survey
41
regarding use of MGMT analyses for glioma Neuro-Oncology Practice 7(1)68ndash76 09 2019 ISSN 2054-2577 URL httpsdoiorg101093
nopnpz039
[30] Takahiro Sasaki Manabu Kinoshita Koji Fujita Junya Fukai NobuhideHayashi Yuji Uematsu Yoshiko Okita Masahiro Nonaka Shusuke Mo-riuchi Takehiro Uda Naohiro Tsuyuguchi Hideyuki Arita Kanji MoriKenichi Ishibashi Koji Takano Namiko Nishida Tomoko Shofuda EmaYoshioka Daisuke Kanematsu Yoshinori Kodama Masayuki ManoNaoyuki Nakao and Yonehiro Kanemura Radiomics and MGMT pro-moter methylation for prognostication of newly diagnosed glioblastomaScientific Reports 9(1)14435 December 2019 ISSN 2045-2322 URLhttpsdoiorg101038s41598-019-50849-y
[31] A Gupta A Prager RJ Young W Shi AMP Omuro and JJ GraberDiffusion-weighted MR imaging and MGMT methylation status in glioblas-toma A reappraisal of the role of preoperative quantitative ADC measure-ments American Journal of Neuroradiology 34(1)E10ndashE11 January 2013ISSN 0195-6108 URL httpsdoiorg103174ajnrA3467
[32] JA Carrillo A Lai PL Nghiemphu HJ Kim HS Phillips S KharbandaP Moftakhar S Lalaezari W Yong BM Ellingson TF Cloughesy andWB Pope Relationship between tumor enhancement edema IDH1 muta-tional status MGMT promoter methylation and survival in glioblastomaAmerican Journal of Neuroradiology 33(7)1349ndash1355 August 2012 ISSN0195-6108 URL httpsdoiorg103174ajnrA2950
[33] Vilde Elisabeth Mikkelsen Hong Yan Dai Anne Line Stensjoslashen Erik Mag-nus Berntsen Oslashyvind Salvesen Ole Solheim and Sverre Helge TorpMGMT promoter methylation status is not related to histological or radio-logical features in IDH wild-type glioblastomas Journal of Neuropathologyamp Experimental Neurology 79(8)855ndash862 August 2020 ISSN 0022-3069URL httpsdoiorg101093jnennlaa060
[34] Martin J van den Bent Interobserver variation of the histopathologicaldiagnosis in clinical trials on glioma a clinicianrsquos perspective Acta Neu-ropathologica 120(3)297ndash304 September 2010 ISSN 1432-0533 URLhttpsdoiorg101007s00401-010-0725-7
[35] Ji Eun Park Ho Sung Kim Youngheun Jo Roh-Eul Yoo Seung Hong ChoiSoo Jung Nam and Jeong Hoon Kim Radiomics prognostication modelin glioblastoma using diffusion- and perfusion-weighted MRI ScientificReports 10(1)4250 December 2020 ISSN 2045-2322 URL httpsdoi
org101038s41598-020-61178-w
[36] Minjae Kim So Yeong Jung Ji Eun Park Yeongheun Jo Seo YoungPark Soo Jung Nam Jeong Hoon Kim and Ho Sung Kim Diffusion-and perfusion-weighted MRI radiomics model may predict isocitrate dehy-drogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade
42
glioma European Radiology 30(4)2142ndash2151 April 2020 ISSN 0938-7994URL httpsdoiorg101007s00330-019-06548-3
[37] M Visser DMJ Muller RJM van Duijn M Smits N Verburg EJ HendriksRJA Nabuurs JCJ Bot RS Eijgelaar M Witte MB van Herk F BarkhofPC de WittHamer and JC de Munck Inter-rater agreement in glioma seg-mentations on longitudinal MRI NeuroImage Clinical 22101727 2019ISSN 2213-1582 URL httpsdoiorg101016jnicl2019101727
[38] Kenneth Clark Bruce Vendt Kirk Smith John Freymann Justin KirbyPaul Koppel Stephen Moore Stanley Phillips David Maffitt MichaelPringle Lawrence Tarbox and Fred Prior The cancer imaging archive(TCIA) Maintaining and operating a public information repository Jour-nal of Digital Imaging 26(6)1045ndash1057 December 2013 ISSN 0897-1889URL httpsdoiorg101007s10278-013-9622-7
[39] Lisa Scarpace Adam E Flanders Rajan Jain Tom Mikkelsen and David WAndrews Data from REMBRANDT The Cancer Imaging Archive 2015URL httpsdoiorg107937K9TCIA2015588OZUZB
[40] National Cancer Institute Clinical Proteomic Tumor Analysis Consortium(CPTAC) Radiology data from the clinical proteomic tumor analysisconsortium glioblastoma multiforme CPTAC-GBM collection The CancerImaging Archive 2018 URL httpsdoiorg107937k9tcia2018
3rje41q1
[41] Nameeta Shah Xu Feng Michael Lankerovich Ralph B Puchalski andBart Keogh Data from Ivy GAP The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016XLwaN6nL
[42] Ralph B Puchalski Nameeta Shah Jeremy Miller Rachel Dalley Steve RNomura Jae-Guen Yoon Kimberly A Smith Michael Lankerovich DarrenBertagnolli Kris Bickley Andrew F Boe Krissy Brouner Stephanie But-ler Shiella Caldejon Mike Chapin Suvro Datta Nick Dee Tsega DestaTim Dolbeare Nadezhda Dotson Amanda Ebbert David Feng Xu FengMichael Fisher Garrett Gee Jeff Goldy Lindsey Gourley Benjamin WGregor Guangyu Gu Nika Hejazinia John Hohmann Parvinder HothiRobert Howard Kevin Joines Ali Kriedberg Leonard Kuan Chris LauFelix Lee Hwahyung Lee Tracy Lemon Fuhui Long Naveed Mastan ErikaMott Chantal Murthy Kiet Ngo Eric Olson Melissa Reding Zack RileyDavid Rosen David Sandman Nadiya Shapovalova Clifford R Slaughter-beck Andrew Sodt Graham Stockdale Aaron Szafer Wayne WakemanPaul E Wohnoutka Steven J White Don Marsh Robert C RostomilyLydia Ng Chinh Dang Allan Jones Bart Keogh Haley R GittlemanJill S Barnholtz-Sloan Patrick J Cimino Megha S Uppin C Dirk KeeneFarrokh R Farrokhi Justin D Lathia Michael E Berens Antonio IavaroneAmy Bernard Ed Lein John W Phillips Steven W Rostad Charles Cobbs
43
Michael J Hawrylycz and Greg D Foltz An anatomic transcriptional at-las of human glioblastoma Science 360(6389)660ndash663 May 2018 ISSN0036-8075 URL httpsdoiorg101126scienceaaf2666
[43] Kathleen Schmainda and Melissa Prah Data from Brain-Tumor-Progression The Cancer Imaging Archive 2018 URL httpsdoiorg
107937K9TCIA201815quzvnb
[44] B H Menze A Jakab S Bauer J Kalpathy-Cramer K FarahaniJ Kirby Y Burren N Porz J Slotboom R Wiest L Lanczi E GerstnerM Weber T Arbel B B Avants N Ayache P Buendia D L CollinsN Cordier J J Corso A Criminisi T Das H Delingette C Demi-ralp C R Durst M Dojat S Doyle J Festa F Forbes E GeremiaB Glocker P Golland X Guo A Hamamci K M IftekharuddinR Jena N M John E Konukoglu D Lashkari J A Mariz R MeierS Pereira D Precup S J Price T R Raviv S M S Reza M RyanD Sarikaya L Schwartz H Shin J Shotton C A Silva N SousaN K Subbanna G Szekely T J Taylor O M Thomas N J Tusti-son G Unal F Vasseur M Wintermark D H Ye L Zhao B ZhaoD Zikic M Prastawa M Reyes and K Van Leemput The mul-timodal brain tumor image segmentation benchmark (BRATS) IEEETransactions on Medical Imaging 34(10)1993ndash2024 2015 URL https
doiorg101109TMI20142377694
[45] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin S Kirby John B Freymann Keyvan Farahani and ChristosDavatzikos Advancing the cancer genome atlas glioma MRI collectionswith expert segmentation labels and radiomic features Scientific Data 4(1)170117 December 2017 ISSN 2052-4463 URL httpsdoiorg10
1038sdata2017117
[46] Spyridon Bakas Mauricio Reyes Andras Jakab Stefan Bauer MarkusRempfler Alessandro Crimi Russell Takeshi Shinohara Christoph BergerSung Min Ha Martin Rozycki Marcel Prastawa Esther Alberts JanaLipkova John Freymann Justin Kirby Michel Bilello Hassan Fathallah-Shaykh Roland Wiest Jan Kirschke Benedikt Wiestler Rivka ColenAikaterini Kotrotsou Pamela Lamontagne Daniel Marcus MikhailMilchenko Arash Nazeri Marc-Andre Weber Abhishek Mahajan UjjwalBaid Elizabeth Gerstner Dongjin Kwon Gagan Acharya Manu Agar-wal Mahbubul Alam Alberto Albiol Antonio Albiol Francisco J AlbiolVarghese Alex Nigel Allinson Pedro H A Amorim Abhijit AmrutkarGanesh Anand Simon Andermatt Tal Arbel Pablo Arbelaez Aaron Av-ery Muneeza Azmat Pranjal B W Bai Subhashis Banerjee Bill BarthThomas Batchelder Kayhan Batmanghelich Enzo Battistella AndrewBeers Mikhail Belyaev Martin Bendszus Eze Benson Jose Bernal Ha-landur Nagaraja Bharath George Biros Sotirios Bisdas James BrownMariano Cabezas Shilei Cao Jorge M Cardoso Eric N Carver Adria
44
Casamitjana Laura Silvana Castillo Marcel Cata Philippe Cattin Al-bert Cerigues Vinicius S Chagas Siddhartha Chandra Yi-Ju ChangShiyu Chang Ken Chang Joseph Chazalon Shengcong Chen Wei ChenJefferson W Chen Zhaolin Chen Kun Cheng Ahana Roy ChoudhuryRoger Chylla Albert Clerigues Steven Colleman Ramiro German Ro-driguez Colmeiro Marc Combalia Anthony Costa Xiaomeng Cui Zhen-zhen Dai Lutao Dai Laura Alexandra Daza Eric Deutsch ChangxingDing Chao Dong Shidu Dong Wojciech Dudzik Zach Eaton-Rosen GaryEgan Guilherme Escudero Theo Estienne Richard Everson JonathanFabrizio Yong Fan Longwei Fang Xue Feng Enzo Ferrante Lucas Fi-don Martin Fischer Andrew P French Naomi Fridman Huan Fu DavidFuentes Yaozong Gao Evan Gates David Gering Amir Gholami WilliGierke Ben Glocker Mingming Gong Sandra Gonzalez-Villa T Gros-ges Yuanfang Guan Sheng Guo Sudeep Gupta Woo-Sup Han Il SongHan Konstantin Harmuth Huiguang He Aura Hernandez-Sabate EvelynHerrmann Naveen Himthani Winston Hsu Cheyu Hsu Xiaojun Hu Xi-aobin Hu Yan Hu Yifan Hu Rui Hua Teng-Yi Huang Weilin HuangSabine Van Huffel Quan Huo Vivek HV Khan M Iftekharuddin FabianIsensee Mobarakol Islam Aaron S Jackson Sachin R Jambawalikar An-drew Jesson Weijian Jian Peter Jin V Jeya Maria Jose Alain JungoB Kainz Konstantinos Kamnitsas Po-Yu Kao Ayush Karnawat ThomasKellermeier Adel Kermi Kurt Keutzer Mohamed Tarek Khadir Mahen-dra Khened Philipp Kickingereder Geena Kim Nik King Haley KnappUrspeter Knecht Lisa Kohli Deren Kong Xiangmao Kong Simon Kop-pers Avinash Kori Ganapathy Krishnamurthi Egor Krivov Piyush Ku-mar Kaisar Kushibar Dmitrii Lachinov Tryphon Lambrou Joon LeeChengen Lee Yuehchou Lee M Lee Szidonia Lefkovits Laszlo LefkovitsJames Levitt Tengfei Li Hongwei Li Wenqi Li Hongyang Li XiaochuanLi Yuexiang Li Heng Li Zhenye Li Xiaoyu Li Zeju Li XiaoGang LiWenqi Li Zheng-Shen Lin Fengming Lin Pietro Lio Chang Liu Bo-qiang Liu Xiang Liu Mingyuan Liu Ju Liu Luyan Liu Xavier LladoMarc Moreno Lopez Pablo Ribalta Lorenzo Zhentai Lu Lin Luo ZhigangLuo Jun Ma Kai Ma Thomas Mackie Anant Madabushi Issam Mah-moudi Klaus H Maier-Hein Pradipta Maji CP Mammen Andreas MangB S Manjunath Michal Marcinkiewicz S McDonagh Stephen McKennaRichard McKinley Miriam Mehl Sachin Mehta Raghav Mehta RaphaelMeier Christoph Meinel Dorit Merhof Craig Meyer Robert Miller Sush-mita Mitra Aliasgar Moiyadi David Molina-Garcia Miguel A B Mon-teiro Grzegorz Mrukwa Andriy Myronenko Jakub Nalepa Thuyen NgoDong Nie Holly Ning Chen Niu Nicholas K Nuechterlein Eric Oer-mann Arlindo Oliveira Diego D C Oliveira Arnau Oliver AlexanderF I Osman Yu-Nian Ou Sebastien Ourselin Nikos Paragios Moo SungPark Brad Paschke J Gregory Pauloski Kamlesh Pawar Nick PawlowskiLinmin Pei Suting Peng Silvio M Pereira Julian Perez-Beteta Vic-tor M Perez-Garcia Simon Pezold Bao Pham Ashish Phophalia GemmaPiella G N Pillai Marie Piraud Maxim Pisov Anmol Popli Michael P
45
Pound Reza Pourreza Prateek Prasanna Vesna Prkovska Tony P Prid-more Santi Puch Elodie Puybareau Buyue Qian Xu Qiao MartinRajchl Swapnil Rane Michael Rebsamen Hongliang Ren Xuhua RenKarthik Revanuru Mina Rezaei Oliver Rippel Luis Carlos Rivera Char-lotte Robert Bruce Rosen Daniel Rueckert Mohammed Safwan MostafaSalem Joaquim Salvi Irina Sanchez Irina Sanchez Heitor M Santos Em-mett Sartor Dawid Schellingerhout Klaudius Scheufele Matthew R ScottArtur A Scussel Sara Sedlar Juan Pablo Serrano-Rubio N Jon ShahNameetha Shah Mazhar Shaikh B Uma Shankar Zeina Shboul HaipengShen Dinggang Shen Linlin Shen Haocheng Shen Varun Shenoy FengShi Hyung Eun Shin Hai Shu Diana Sima M Sinclair Orjan SmedbyJames M Snyder Mohammadreza Soltaninejad Guidong Song MehulSoni Jean Stawiaski Shashank Subramanian Li Sun Roger Sun JiaweiSun Kay Sun Yu Sun Guoxia Sun Shuang Sun Yannick R Suter Las-zlo Szilagyi Sanjay Talbar Dacheng Tao Dacheng Tao Zhongzhao TengSiddhesh Thakur Meenakshi H Thakur Sameer Tharakan Pallavi Ti-wari Guillaume Tochon Tuan Tran Yuhsiang M Tsai Kuan-Lun TsengTran Anh Tuan Vadim Turlapov Nicholas Tustison Maria VakalopoulouSergi Valverde Rami Vanguri Evgeny Vasiliev Jonathan Ventura LuisVera Tom Vercauteren C A Verrastro Lasitha Vidyaratne Veronica Vi-laplana Ajeet Vivekanandan Guotai Wang Qian Wang Chiatse J WangWeichung Wang Duo Wang Ruixuan Wang Yuanyuan Wang ChunliangWang Guotai Wang Ning Wen Xin Wen Leon Weninger Wolfgang WickShaocheng Wu Qiang Wu Yihong Wu Yong Xia Yanwu Xu XiaowenXu Peiyuan Xu Tsai-Ling Yang Xiaoping Yang Hao-Yu Yang JunlinYang Haojin Yang Guang Yang Hongdou Yao Xujiong Ye ChangchangYin Brett Young-Moxon Jinhua Yu Xiangyu Yue Songtao Zhang AngelaZhang Kun Zhang Xuejie Zhang Lichi Zhang Xiaoyue Zhang YazhuoZhang Lei Zhang Jianguo Zhang Xiang Zhang Tianhao Zhang SichengZhao Yu Zhao Xiaomei Zhao Liang Zhao Yefeng Zheng Liming ZhongChenhong Zhou Xiaobing Zhou Fan Zhou Hongtu Zhu Jin Zhu YingZhuge Weiwei Zong Jayashree Kalpathy-Cramer Keyvan Farahani Chris-tos Davatzikos Koen van Leemput and Bjoern Menze Identifying the bestmachine learning algorithms for brain tumor segmentation progression as-sessment and overall survival prediction in the BRATS challenge 2018Preprint at httpsarxivorgabs181102629
[47] Nancy Pedano Adam E Flanders Lisa Scarpace Tom Mikkelsen Jen-nifer M Eschbacher Beth Hermes Victor Sisneros Jill Barnholtz-Sloanand Quinn Ostrom Radiology data from the cancer genome atlas lowgrade glioma [TCGA-LGG] collection The Cancer Imaging Archive 2016URL httpsdoiorg107937K9TCIA2016L4LTD3TK
[48] Lisa Scarpace Tom Mikkelsen Soonmee Cha Sujaya Rao SangeetaTekchandani David Gutman Joel H Saltz Bradley J Erickson NancyPedano Adam E Flanders Jill Barnholtz-Sloan Quinn Ostrom DanielBarboriak and Laura J Pierce Radiology data from the cancer genome
46
atlas glioblastoma multiforme [TCGA-GBM] collection The Cancer Imag-ing Archive 2016 URL httpsdoiorg107937K9TCIA2016
RNYFUYE9
[49] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello MartinRozycki Justin Kirby John Freymann Keyvan Farahani and ChristosDavatzikos Segmentation labels and radiomic features for the pre-operativescans of the TCGA-LGG collection [data set] The Cancer Imaging Archive2017 URL httpsdoiorg107937K9TCIA2017GJQ7R0EF
[50] Spyridon Bakas Hamed Akbari Aristeidis Sotiras Michel Bilello Mar-tin Rozycki Justin Kirby John Freymann Keyvan Farahani and Chris-tos Davatzikos Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection [data set] The CancerImaging Archive 2017 URL httpsdoiorg107937K9TCIA2017
KLXWJJ1Q
[51] Xiangrui Li Paul S Morgan John Ashburner Jolinda Smith and Christo-pher Rorden The first step for neuroimaging data analysis DICOM toNIfTI conversion Journal of Neuroscience Methods 26447ndash56 May 2016ISSN 0165-0270 URL httpsdoiorg101016jjneumeth201603
001
[52] Vladimir Fonov Alan C Evans Kelly Botteron C Robert Almli Robert CMcKinstry and D Louis Collins Unbiased average age-appropriate at-lases for pediatric studies NeuroImage 54(1)313ndash327 January 2011ISSN 1053-8119 URL httpsdoiorg101016jneuroimage2010
07033
[53] VS Fonov AC Evans RC McKinstry CR Almli and DL Collins Unbiasednonlinear average age-appropriate brain templates from birth to adulthoodNeuroImage 47S102 July 2009 ISSN 1053-8119 URL httpsdoi
org101016S1053-8119(09)70884-5 Organization for Human BrainMapping 2009 Annual Meeting
[54] S Klein M Staring K Murphy MA Viergever and J Pluim elastix Atoolbox for intensity-based medical image registration IEEE Transactionson Medical Imaging 29(1)196ndash205 January 2010 ISSN 0278-0062 URLhttpsdoiorg101109TMI20092035616
[55] Denis Shamonin Fast parallel image registration on CPU and GPU fordiagnostic classification of alzheimerrsquos disease Frontiers in Neuroinfor-matics 750 2013 ISSN 1662-5196 URL httpsdoiorg103389
fninf201300050
[56] Bradley C Lowekamp David T Chen Luis Ibanez and Daniel Blezek Thedesign of SimpleITK Frontiers in Neuroinformatics 745 2013 ISSN1662-5196 URL httpsdoiorg103389fninf201300045
47
[57] Fabian Isensee Marianne Schell Irada Pflueger Gianluca Brugnara DavidBonekamp Ulf Neuberger Antje Wick Heinz-Peter Schlemmer SabineHeiland Wolfgang Wick Martin Bendszus Klaus H Maier-Hein andPhilipp Kickingereder Automated brain extraction of multisequence MRIusing artificial neural networks Human Brain Mapping 40(17)4952ndash4964December 2019 ISSN 1065-9471 URL httpsdoiorg101002hbm
24750
[58] Olaf Ronneberger Invited talk U-net convolutional networks for biomed-ical image segmentation In geb Fritzsche K Maier-Hein geb Lehmann TDeserno Heinz Handels and Thomas Tolxdorff editors Bildverarbeitungfur die Medizin 2017 Informatik aktuell pages 3ndash3 Springer Scienceand Business Media LLC 2017 ISBN 9783662543443 URL https
doiorg101007978-3-662-54345-0_3
[59] Martın Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy DavisJeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving MichaelIsard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry MooreDerek G Murray Benoit Steiner Paul Tucker Vijay Vasudevan PeteWarden Martin Wicke Yuan Yu and Xiaoqiang Zheng Tensor-Flow A system for large-scale machine learning In 12th USENIXSymposium on Operating Systems Design and Implementation (OSDI16) pages 265ndash283 USENIX Association November 2016 ISBN978-1-931971-33-1 URL httpswwwusenixorgconferenceosdi16
technical-sessionspresentationabadi
[60] Dipankar Das Naveen Mellempudi Dheevatsa Mudigere Dhiraj KalamkarSasikanth Avancha Kunal Banerjee Srinivas Sridharan KarthikVaidyanathan Bharat Kaul Evangelos Georganas Alexander HeineckePradeep Dubey Jesus Corbal Nikita Shustrov Roma Dubtsov EvaristFomenko and Vadim Pirogov Mixed precision training of convolutionalneural networks using integer operations In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=H135uzZ0-
[61] Samuel L Smith Pieter-Jan Kindermans and Quoc V Le Donrsquot decaythe learning rate increase the batch size In International Conference onLearning Representations 2018 URL httpsopenreviewnetforum
id=B1Yy1BxCZ
[62] Cliff Woolley NCCL accelarated multi-GPU collective communicationsURL httpsimagesnvidiacomeventssc15pdfsNCCL-Woolley
pdf Accessed on 2020-09-30
[63] Ilya Loshchilov and Frank Hutter Decoupled weight decay regularization2017 Preprint at httpsarxivorgabs171105101
48
[64] F Pedregosa G Varoquaux A Gramfort V Michel B Thirion O GriselM Blondel P Prettenhofer R Weiss V Dubourg J Vanderplas A Pas-sos D Cournapeau M Brucher M Perrot and E Duchesnay Scikit-learn Machine learning in python Journal of Machine Learning Re-search 122825ndash2830 2011 URL httpswwwjmlrorgpapersv12
pedregosa11ahtml
[65] Abdel Aziz Taha and Allan Hanbury Metrics for evaluating 3d medicalimage segmentation analysis selection and tool BMC Medical Imaging15(1)29 August 2015 ISSN 1471-2342 URL httpsdoiorg101186
s12880-015-0068-x
[66] Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viegas and MartinWattenberg SmoothGrad removing noise by adding noise 2017 Preprintat httpsarxivorgabs170603825
[67] Alaa Tharwat Classification assessment methods Applied Computing andInformatics 2018 ISSN 2210-8327 URL httpsdoiorg101016j
aci201808003
[68] David J Hand and Robert J Till A simple generalisation of the areaunder the ROC curve for multiple class classification problems MachineLearning 45(2)171ndash186 November 2001 ISSN 1573-0565 URL https
doiorg101023A1010920819831
49
Data Collection | Patient | IDH_mutated | 1p19q_codeleted | Grade | |||||
BTumorP | PGBM-001 | -1 | -1 | -1 | |||||
BTumorP | PGBM-002 | -1 | -1 | -1 | |||||
BTumorP | PGBM-003 | -1 | -1 | -1 | |||||
BTumorP | PGBM-004 | -1 | -1 | -1 | |||||
BTumorP | PGBM-005 | -1 | -1 | -1 | |||||
BTumorP | PGBM-006 | -1 | -1 | -1 | |||||
BTumorP | PGBM-007 | -1 | -1 | -1 | |||||
BTumorP | PGBM-008 | -1 | -1 | -1 | |||||
BTumorP | PGBM-009 | -1 | -1 | -1 | |||||
BTumorP | PGBM-010 | -1 | -1 | -1 | |||||
BTumorP | PGBM-011 | -1 | -1 | -1 | |||||
BTumorP | PGBM-012 | -1 | -1 | -1 | |||||
BTumorP | PGBM-013 | -1 | -1 | -1 | |||||
BTumorP | PGBM-014 | -1 | -1 | -1 | |||||
BTumorP | PGBM-015 | -1 | -1 | -1 | |||||
BTumorP | PGBM-016 | -1 | -1 | -1 | |||||
BTumorP | PGBM-017 | -1 | -1 | -1 | |||||
BTumorP | PGBM-018 | -1 | -1 | -1 | |||||
BTumorP | PGBM-019 | -1 | -1 | -1 | |||||
BTumorP | PGBM-020 | -1 | -1 | -1 | |||||
BraTS | 2013_0 | -1 | -1 | -1 | |||||
BraTS | 2013_10 | -1 | -1 | -1 | |||||
BraTS | 2013_11 | -1 | -1 | -1 | |||||
BraTS | 2013_12 | -1 | -1 | -1 | |||||
BraTS | 2013_13 | -1 | -1 | -1 | |||||
BraTS | 2013_14 | -1 | -1 | -1 | |||||
BraTS | 2013_15 | -1 | -1 | -1 | |||||
BraTS | 2013_16 | -1 | -1 | -1 | |||||
BraTS | 2013_17 | -1 | -1 | -1 | |||||
BraTS | 2013_18 | -1 | -1 | -1 | |||||
BraTS | 2013_19 | -1 | -1 | -1 | |||||
BraTS | 2013_1 | -1 | -1 | -1 | |||||
BraTS | 2013_20 | -1 | -1 | -1 | |||||
BraTS | 2013_21 | -1 | -1 | -1 | |||||
BraTS | 2013_22 | -1 | -1 | -1 | |||||
BraTS | 2013_23 | -1 | -1 | -1 | |||||
BraTS | 2013_24 | -1 | -1 | -1 | |||||
BraTS | 2013_25 | -1 | -1 | -1 | |||||
BraTS | 2013_26 | -1 | -1 | -1 | |||||
BraTS | 2013_27 | -1 | -1 | -1 | |||||
BraTS | 2013_28 | -1 | -1 | -1 | |||||
BraTS | 2013_29 | -1 | -1 | -1 | |||||
BraTS | 2013_2 | -1 | -1 | -1 | |||||
BraTS | 2013_3 | -1 | -1 | -1 | |||||
BraTS | 2013_4 | -1 | -1 | -1 | |||||
BraTS | 2013_5 | -1 | -1 | -1 | |||||
BraTS | 2013_6 | -1 | -1 | -1 | |||||
BraTS | 2013_7 | -1 | -1 | -1 | |||||
BraTS | 2013_8 | -1 | -1 | -1 | |||||
BraTS | 2013_9 | -1 | -1 | -1 | |||||
BraTS | CBICA_AAB | -1 | -1 | -1 | |||||
BraTS | CBICA_AAG | -1 | -1 | -1 | |||||
BraTS | CBICA_AAL | -1 | -1 | -1 | |||||
BraTS | CBICA_AAP | -1 | -1 | -1 | |||||
BraTS | CBICA_ABB | -1 | -1 | -1 | |||||
BraTS | CBICA_ABE | -1 | -1 | -1 | |||||
BraTS | CBICA_ABM | -1 | -1 | -1 | |||||
BraTS | CBICA_ABN | -1 | -1 | -1 | |||||
BraTS | CBICA_ABO | -1 | -1 | -1 | |||||
BraTS | CBICA_ABY | -1 | -1 | -1 | |||||
BraTS | CBICA_ALN | -1 | -1 | -1 | |||||
BraTS | CBICA_ALU | -1 | -1 | -1 | |||||
BraTS | CBICA_ALX | -1 | -1 | -1 | |||||
BraTS | CBICA_AME | -1 | -1 | -1 | |||||
BraTS | CBICA_AMH | -1 | -1 | -1 | |||||
BraTS | CBICA_ANG | -1 | -1 | -1 | |||||
BraTS | CBICA_ANI | -1 | -1 | -1 | |||||
BraTS | CBICA_ANP | -1 | -1 | -1 | |||||
BraTS | CBICA_ANV | -1 | -1 | -1 | |||||
BraTS | CBICA_ANZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AOC | -1 | -1 | -1 | |||||
BraTS | CBICA_AOD | -1 | -1 | -1 | |||||
BraTS | CBICA_AOH | -1 | -1 | -1 | |||||
BraTS | CBICA_AOO | -1 | -1 | -1 | |||||
BraTS | CBICA_AOP | -1 | -1 | -1 | |||||
BraTS | CBICA_AOS | -1 | -1 | -1 | |||||
BraTS | CBICA_AOZ | -1 | -1 | -1 | |||||
BraTS | CBICA_APK | -1 | -1 | -1 | |||||
BraTS | CBICA_APR | -1 | -1 | -1 | |||||
BraTS | CBICA_APY | -1 | -1 | -1 | |||||
BraTS | CBICA_APZ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQA | -1 | -1 | -1 | |||||
BraTS | CBICA_AQD | -1 | -1 | -1 | |||||
BraTS | CBICA_AQG | -1 | -1 | -1 | |||||
BraTS | CBICA_AQJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQN | -1 | -1 | -1 | |||||
BraTS | CBICA_AQO | -1 | -1 | -1 | |||||
BraTS | CBICA_AQP | -1 | -1 | -1 | |||||
BraTS | CBICA_AQQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AQR | -1 | -1 | -1 | |||||
BraTS | CBICA_AQT | -1 | -1 | -1 | |||||
BraTS | CBICA_AQU | -1 | -1 | -1 | |||||
BraTS | CBICA_AQV | -1 | -1 | -1 | |||||
BraTS | CBICA_AQY | -1 | -1 | -1 | |||||
BraTS | CBICA_AQZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ARF | -1 | -1 | -1 | |||||
BraTS | CBICA_ARW | -1 | -1 | -1 | |||||
BraTS | CBICA_ARZ | -1 | -1 | -1 | |||||
BraTS | CBICA_ASA | -1 | -1 | -1 | |||||
BraTS | CBICA_ASE | -1 | -1 | -1 | |||||
BraTS | CBICA_ASF | -1 | -1 | -1 | |||||
BraTS | CBICA_ASG | -1 | -1 | -1 | |||||
BraTS | CBICA_ASH | -1 | -1 | -1 | |||||
BraTS | CBICA_ASK | -1 | -1 | -1 | |||||
BraTS | CBICA_ASN | -1 | -1 | -1 | |||||
BraTS | CBICA_ASO | -1 | -1 | -1 | |||||
BraTS | CBICA_ASR | -1 | -1 | -1 | |||||
BraTS | CBICA_ASU | -1 | -1 | -1 | |||||
BraTS | CBICA_ASV | -1 | -1 | -1 | |||||
BraTS | CBICA_ASW | -1 | -1 | -1 | |||||
BraTS | CBICA_ASY | -1 | -1 | -1 | |||||
BraTS | CBICA_ATB | -1 | -1 | -1 | |||||
BraTS | CBICA_ATD | -1 | -1 | -1 | |||||
BraTS | CBICA_ATF | -1 | -1 | -1 | |||||
BraTS | CBICA_ATN | -1 | -1 | -1 | |||||
BraTS | CBICA_ATP | -1 | -1 | -1 | |||||
BraTS | CBICA_ATV | -1 | -1 | -1 | |||||
BraTS | CBICA_ATX | -1 | -1 | -1 | |||||
BraTS | CBICA_AUA | -1 | -1 | -1 | |||||
BraTS | CBICA_AUN | -1 | -1 | -1 | |||||
BraTS | CBICA_AUQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AUR | -1 | -1 | -1 | |||||
BraTS | CBICA_AUW | -1 | -1 | -1 | |||||
BraTS | CBICA_AUX | -1 | -1 | -1 | |||||
BraTS | CBICA_AVB | -1 | -1 | -1 | |||||
BraTS | CBICA_AVF | -1 | -1 | -1 | |||||
BraTS | CBICA_AVG | -1 | -1 | -1 | |||||
BraTS | CBICA_AVJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AVT | -1 | -1 | -1 | |||||
BraTS | CBICA_AVV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWG | -1 | -1 | -1 | |||||
BraTS | CBICA_AWH | -1 | -1 | -1 | |||||
BraTS | CBICA_AWI | -1 | -1 | -1 | |||||
BraTS | CBICA_AWV | -1 | -1 | -1 | |||||
BraTS | CBICA_AWX | -1 | -1 | -1 | |||||
BraTS | CBICA_AXJ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXL | -1 | -1 | -1 | |||||
BraTS | CBICA_AXM | -1 | -1 | -1 | |||||
BraTS | CBICA_AXN | -1 | -1 | -1 | |||||
BraTS | CBICA_AXO | -1 | -1 | -1 | |||||
BraTS | CBICA_AXQ | -1 | -1 | -1 | |||||
BraTS | CBICA_AXW | -1 | -1 | -1 | |||||
BraTS | CBICA_AYA | -1 | -1 | -1 | |||||
BraTS | CBICA_AYC | -1 | -1 | -1 | |||||
BraTS | CBICA_AYG | -1 | -1 | -1 | |||||
BraTS | CBICA_AYI | -1 | -1 | -1 | |||||
BraTS | CBICA_AYU | -1 | -1 | -1 | |||||
BraTS | CBICA_AYW | -1 | -1 | -1 | |||||
BraTS | CBICA_AZD | -1 | -1 | -1 | |||||
BraTS | CBICA_AZH | -1 | -1 | -1 | |||||
BraTS | CBICA_BAN | -1 | -1 | -1 | |||||
BraTS | CBICA_BAP | -1 | -1 | -1 | |||||
BraTS | CBICA_BAX | -1 | -1 | -1 | |||||
BraTS | CBICA_BBG | -1 | -1 | -1 | |||||
BraTS | CBICA_BCF | -1 | -1 | -1 | |||||
BraTS | CBICA_BCL | -1 | -1 | -1 | |||||
BraTS | CBICA_BDK | -1 | -1 | -1 | |||||
BraTS | CBICA_BEM | -1 | -1 | -1 | |||||
BraTS | CBICA_BFB | -1 | -1 | -1 | |||||
BraTS | CBICA_BFP | -1 | -1 | -1 | |||||
BraTS | CBICA_BGE | -1 | -1 | -1 | |||||
BraTS | CBICA_BGG | -1 | -1 | -1 | |||||
BraTS | CBICA_BGN | -1 | -1 | -1 | |||||
BraTS | CBICA_BGO | -1 | -1 | -1 | |||||
BraTS | CBICA_BGR | -1 | -1 | -1 | |||||
BraTS | CBICA_BGT | -1 | -1 | -1 | |||||
BraTS | CBICA_BGW | -1 | -1 | -1 | |||||
BraTS | CBICA_BGX | -1 | -1 | -1 | |||||
BraTS | CBICA_BHB | -1 | -1 | -1 | |||||
BraTS | CBICA_BHK | -1 | -1 | -1 | |||||
BraTS | CBICA_BHM | -1 | -1 | -1 | |||||
BraTS | CBICA_BHQ | -1 | -1 | -1 | |||||
BraTS | CBICA_BHV | -1 | -1 | -1 | |||||
BraTS | CBICA_BHZ | -1 | -1 | -1 | |||||
BraTS | CBICA_BIC | -1 | -1 | -1 | |||||
BraTS | CBICA_BJY | -1 | -1 | -1 | |||||
BraTS | CBICA_BKV | -1 | -1 | -1 | |||||
BraTS | CBICA_BLJ | -1 | -1 | -1 | |||||
BraTS | CBICA_BNR | -1 | -1 | -1 | |||||
BraTS | TMC_6290 | -1 | -1 | -1 | |||||
BraTS | TMC_6643 | -1 | -1 | -1 | |||||
BraTS | TMC_9043 | -1 | -1 | -1 | |||||
BraTS | TMC_11964 | -1 | -1 | -1 | |||||
BraTS | TMC_12866 | -1 | -1 | -1 | |||||
BraTS | TMC_15477 | -1 | -1 | -1 | |||||
BraTS | TMC_21360 | -1 | -1 | -1 | |||||
BraTS | TMC_27374 | -1 | -1 | -1 | |||||
BraTS | TMC_30014 | -1 | -1 | -1 | |||||
CPTAC-GBM | C3L-00016 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00019 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00265 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00278 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00349 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00424 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00429 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00506 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00528 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00591 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00631 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00636 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00671 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00674 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-00677 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01045 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01046 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01142 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01156 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-01327 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02041 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02465 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02504 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02704 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02706 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02707 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-02708 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03260 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03266 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03727 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03728 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03747 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-03748 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3L-04084 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00661 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00662 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00663 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-00665 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01192 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01196 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01505 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01849 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01851 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-01852 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02255 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02256 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-02286 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03001 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03003 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-03755 | -1 | -1 | 4 | |||||
CPTAC-GBM | C3N-04686 | -1 | -1 | 4 | |||||
IvyGAP | W10 | 1 | 1 | 4 | |||||
IvyGAP | W11 | 0 | 0 | 4 | |||||
IvyGAP | W12 | 0 | 0 | 4 | |||||
IvyGAP | W13 | 0 | 0 | 4 | |||||
IvyGAP | W16 | 0 | 0 | 4 | |||||
IvyGAP | W18 | 0 | 0 | 4 | |||||
IvyGAP | W19 | 0 | 0 | 4 | |||||
IvyGAP | W1 | 0 | 0 | 4 | |||||
IvyGAP | W20 | 0 | 0 | 4 | |||||
IvyGAP | W21 | 0 | 0 | 4 | |||||
IvyGAP | W22 | 0 | 0 | -1 | |||||
IvyGAP | W26 | 0 | -1 | 4 | |||||
IvyGAP | W29 | 0 | 0 | 4 | |||||
IvyGAP | W2 | 0 | 1 | 4 | |||||
IvyGAP | W30 | 0 | 0 | 4 | |||||
IvyGAP | W31 | 1 | 1 | 4 | |||||
IvyGAP | W32 | 0 | 0 | 4 | |||||
IvyGAP | W33 | 0 | 0 | 4 | |||||
IvyGAP | W34 | 0 | 0 | 4 | |||||
IvyGAP | W35 | 1 | 0 | 3 | |||||
IvyGAP | W36 | 0 | 0 | 4 | |||||
IvyGAP | W38 | 0 | 0 | 4 | |||||
IvyGAP | W39 | 0 | 0 | 4 | |||||
IvyGAP | W3 | 1 | 0 | 4 | |||||
IvyGAP | W40 | 0 | 0 | 4 | |||||
IvyGAP | W42 | 0 | -1 | 4 | |||||
IvyGAP | W43 | 0 | -1 | 4 | |||||
IvyGAP | W45 | -1 | -1 | 4 | |||||
IvyGAP | W48 | 0 | -1 | 4 | |||||
IvyGAP | W4 | 1 | 0 | 4 | |||||
IvyGAP | W50 | 0 | -1 | 3 | |||||
IvyGAP | W53 | 1 | -1 | 4 | |||||
IvyGAP | W54 | 0 | -1 | 4 | |||||
IvyGAP | W55 | 0 | -1 | 4 | |||||
IvyGAP | W5 | 0 | 0 | 4 | |||||
IvyGAP | W6 | 0 | 0 | 4 | |||||
IvyGAP | W7 | 0 | 0 | 4 | |||||
IvyGAP | W8 | 0 | 0 | 4 | |||||
IvyGAP | W9 | 0 | 0 | 4 | |||||
REMBRANDT | 900-00-5299 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5303 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5308 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5316 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5317 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5332 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5339 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5341 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5342 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5346 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5380 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5381 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5382 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5385 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5396 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5404 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5412 | -1 | -1 | -1 | |||||
REMBRANDT | 900-00-5414 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5458 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5459 | -1 | -1 | 3 | |||||
REMBRANDT | 900-00-5462 | -1 | -1 | 4 | |||||
REMBRANDT | 900-00-5468 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5476 | -1 | -1 | 2 | |||||
REMBRANDT | 900-00-5477 | -1 | -1 | 2 | |||||
REMBRANDT | HF0763 | -1 | -1 | -1 | |||||
REMBRANDT | HF0828 | -1 | -1 | 3 | |||||
REMBRANDT | HF0835 | -1 | -1 | 2 | |||||
REMBRANDT | HF0855 | -1 | -1 | 2 | |||||
REMBRANDT | HF0868 | -1 | -1 | -1 | |||||
REMBRANDT | HF0883 | -1 | -1 | -1 | |||||
REMBRANDT | HF0899 | -1 | -1 | 2 | |||||
REMBRANDT | HF0920 | -1 | -1 | 2 | |||||
REMBRANDT | HF0931 | -1 | -1 | 2 | |||||
REMBRANDT | HF0953 | -1 | -1 | 2 | |||||
REMBRANDT | HF0960 | -1 | -1 | 2 | |||||
REMBRANDT | HF0966 | -1 | -1 | 3 | |||||
REMBRANDT | HF0986 | -1 | -1 | 4 | |||||
REMBRANDT | HF0990 | -1 | -1 | 4 | |||||
REMBRANDT | HF1000 | -1 | -1 | 2 | |||||
REMBRANDT | HF1058 | -1 | -1 | 4 | |||||
REMBRANDT | HF1059 | -1 | -1 | 3 | |||||
REMBRANDT | HF1071 | -1 | -1 | 4 | |||||
REMBRANDT | HF1077 | -1 | -1 | 4 | |||||
REMBRANDT | HF1078 | -1 | -1 | 4 | |||||
REMBRANDT | HF1097 | -1 | -1 | 4 | |||||
REMBRANDT | HF1113 | -1 | -1 | -1 | |||||
REMBRANDT | HF1122 | -1 | -1 | 4 | |||||
REMBRANDT | HF1136 | -1 | -1 | 3 | |||||
REMBRANDT | HF1139 | -1 | -1 | 4 | |||||
REMBRANDT | HF1150 | -1 | -1 | 3 | |||||
REMBRANDT | HF1156 | -1 | -1 | 2 | |||||
REMBRANDT | HF1167 | -1 | -1 | 2 | |||||
REMBRANDT | HF1185 | -1 | -1 | 3 | |||||
REMBRANDT | HF1191 | -1 | -1 | 4 | |||||
REMBRANDT | HF1199 | -1 | -1 | -1 | |||||
REMBRANDT | HF1219 | -1 | -1 | 3 | |||||
REMBRANDT | HF1227 | -1 | -1 | 2 | |||||
REMBRANDT | HF1232 | -1 | -1 | 3 | |||||
REMBRANDT | HF1235 | -1 | -1 | 2 | |||||
REMBRANDT | HF1242 | -1 | -1 | 3 | |||||
REMBRANDT | HF1246 | -1 | -1 | 2 | |||||
REMBRANDT | HF1264 | -1 | -1 | 2 | |||||
REMBRANDT | HF1269 | -1 | -1 | 4 | |||||
REMBRANDT | HF1280 | -1 | -1 | 3 | |||||
REMBRANDT | HF1292 | -1 | -1 | 4 | |||||
REMBRANDT | HF1293 | -1 | -1 | -1 | |||||
REMBRANDT | HF1297 | -1 | -1 | 4 | |||||
REMBRANDT | HF1300 | -1 | -1 | -1 | |||||
REMBRANDT | HF1307 | -1 | -1 | -1 | |||||
REMBRANDT | HF1316 | -1 | -1 | 2 | |||||
REMBRANDT | HF1318 | -1 | -1 | -1 | |||||
REMBRANDT | HF1325 | -1 | -1 | 2 | |||||
REMBRANDT | HF1331 | -1 | -1 | -1 | |||||
REMBRANDT | HF1334 | -1 | -1 | 2 | |||||
REMBRANDT | HF1344 | -1 | -1 | 2 | |||||
REMBRANDT | HF1345 | -1 | -1 | 2 | |||||
REMBRANDT | HF1357 | -1 | -1 | 3 | |||||
REMBRANDT | HF1381 | -1 | -1 | 2 | |||||
REMBRANDT | HF1397 | -1 | -1 | 4 | |||||
REMBRANDT | HF1398 | -1 | -1 | 3 | |||||
REMBRANDT | HF1407 | -1 | -1 | 2 | |||||
REMBRANDT | HF1409 | -1 | -1 | 3 | |||||
REMBRANDT | HF1420 | -1 | -1 | -1 | |||||
REMBRANDT | HF1429 | -1 | -1 | -1 | |||||
REMBRANDT | HF1433 | -1 | -1 | 2 | |||||
REMBRANDT | HF1437 | -1 | -1 | -1 | |||||
REMBRANDT | HF1442 | -1 | -1 | 2 | |||||
REMBRANDT | HF1458 | -1 | -1 | 3 | |||||
REMBRANDT | HF1463 | -1 | -1 | 2 | |||||
REMBRANDT | HF1489 | -1 | -1 | 2 | |||||
REMBRANDT | HF1490 | -1 | -1 | 3 | |||||
REMBRANDT | HF1493 | -1 | -1 | -1 | |||||
REMBRANDT | HF1510 | -1 | -1 | -1 | |||||
REMBRANDT | HF1511 | -1 | -1 | 2 | |||||
REMBRANDT | HF1517 | -1 | -1 | 4 | |||||
REMBRANDT | HF1538 | -1 | -1 | 4 | |||||
REMBRANDT | HF1551 | -1 | -1 | 2 | |||||
REMBRANDT | HF1553 | -1 | -1 | 2 | |||||
REMBRANDT | HF1560 | -1 | -1 | 4 | |||||
REMBRANDT | HF1568 | -1 | -1 | 2 | |||||
REMBRANDT | HF1587 | -1 | -1 | 3 | |||||
REMBRANDT | HF1588 | -1 | -1 | 2 | |||||
REMBRANDT | HF1606 | -1 | -1 | 2 | |||||
REMBRANDT | HF1613 | -1 | -1 | 3 | |||||
REMBRANDT | HF1628 | -1 | -1 | 4 | |||||
REMBRANDT | HF1652 | -1 | -1 | -1 | |||||
REMBRANDT | HF1677 | -1 | -1 | 2 | |||||
REMBRANDT | HF1702 | -1 | -1 | 3 | |||||
REMBRANDT | HF1708 | -1 | -1 | 2 | |||||
TCGA-GBM | TCGA-02-0003 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0006 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0009 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0011 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0027 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0033 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0034 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0037 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0046 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0047 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0048 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0054 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0059 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0060 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0064 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0068 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0069 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0070 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0075 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0085 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0086 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0087 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0102 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0106 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-02-0116 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0119 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0122 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0128 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0130 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0132 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0133 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0137 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0138 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0139 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0142 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0145 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0149 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0154 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0158 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0162 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0164 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0166 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0168 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0175 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0176 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0177 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0179 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0182 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0184 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0185 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0187 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0188 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0189 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0190 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0192 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0213 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0238 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0240 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0241 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0644 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0646 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0648 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-0649 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1084 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-1802 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-2570 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5408 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5412 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5413 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-06-5417 | 1 | -1 | 4 | |||||
TCGA-GBM | TCGA-06-6389 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0350 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0352 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0353 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0354 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0355 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0356 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0357 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0358 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0359 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0360 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0385 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0389 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0392 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0512 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0520 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0521 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0522 | -1 | -1 | 4 | |||||
TCGA-GBM | TCGA-08-0524 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-08-0529 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0616 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0776 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-0829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1093 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1094 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1098 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1598 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-1601 | 0 | -1 | -1 | |||||
TCGA-GBM | TCGA-12-1602 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-12-3650 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-0789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1456 | 1 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1794 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1825 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-1829 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-14-3477 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-0963 | -1 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1390 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-1789 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2624 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-2631 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5951 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5954 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5958 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-19-5960 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1834 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-1838 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-27-2526 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4932 | 0 | -1 | 4 | |||||
TCGA-GBM | TCGA-76-4934 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-4935 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6191 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6193 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6280 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6282 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6285 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6656 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6657 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6661 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6662 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6663 | 0 | 0 | 4 | |||||
TCGA-GBM | TCGA-76-6664 | 0 | 0 | 4 | |||||
TCGA-LGG | TCGA-CS-4941 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4942 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-4944 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5393 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-5395 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-5396 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-CS-5397 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6186 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6188 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6290 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6665 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6666 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-CS-6667 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-CS-6668 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-CS-6669 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5849 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-5851 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5852 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5854 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-5871 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-5874 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6397 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6399 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6400 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6404 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6405 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6407 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-6410 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-6542 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7008 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7010 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7014 | -1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7015 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7018 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7019 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7294 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-7298 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7300 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7302 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-7309 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8162 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8164 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-8165 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8167 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-8168 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-EZ-7265A | -1 | -1 | -1 | |||||
TCGA-LGG | TCGA-FG-5964 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-6688 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6691 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-6692 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-FG-7643 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-FG-A713 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7473 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7616 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7680 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7684 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7686 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7692 | 1 | 1 | 2 | |||||
TCGA-LGG | TCGA-HT-7693 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-7694 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7855 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7856 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7860 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7874 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-7879 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7882 | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8105 | 1 | 1 | 3 | |||||
TCGA-LGG | TCGA-HT-8106 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8107 | 0 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8111 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-8114 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-8563 | 1 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 0 | 3 | |||||
TCGA-LGG | TCGA-HT-A614 | 1 | 0 | 2 | |||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0 | 2 |
Data_collection | Patient | IDH_mutated | Prediction_score_IDH_wildtype | Prediction_score_IDH_mutated | 1p19q_codeleted | Prediction_score_1p19q_codeleted | Prediction_score_1p19q_intact | Grade | Prediction_score_grade_2 | Prediction_score_grade_3 | Prediction_score_grade_4 | ||||||||||||
TCGA-GBM | TCGA-02-0003 | 0 | 099998915 | 10867886E-05 | 0 | 099996686 | 3308471E-05 | 4 | 7377526E-05 | 000074111245 | 099918514 | ||||||||||||
TCGA-GBM | TCGA-02-0006 | 0 | 042321962 | 05767803 | 0 | 068791837 | 031208166 | 4 | 060229343 | 026596427 | 013174225 | ||||||||||||
TCGA-GBM | TCGA-02-0009 | 0 | 099306935 | 0006930672 | 0 | 09906961 | 0009303949 | 4 | 0056565534 | 010282235 | 08406121 | ||||||||||||
TCGA-GBM | TCGA-02-0011 | 0 | 013531776 | 08646823 | 0 | 085318035 | 01468197 | 4 | 0015055533 | 092510724 | 005983725 | ||||||||||||
TCGA-GBM | TCGA-02-0027 | 0 | 09997279 | 000027212297 | 0 | 09986827 | 00013172914 | 4 | 00016104137 | 00038575265 | 0994532 | ||||||||||||
TCGA-GBM | TCGA-02-0033 | 0 | 099974436 | 000025564007 | 0 | 099940693 | 0000593021 | 4 | 00020670628 | 0003761288 | 09941717 | ||||||||||||
TCGA-GBM | TCGA-02-0034 | 0 | 091404164 | 008595832 | 0 | 089209336 | 01079066 | 4 | 00116944825 | 0061110377 | 092719513 | ||||||||||||
TCGA-GBM | TCGA-02-0037 | 0 | 09999577 | 42315594E-05 | 0 | 099992716 | 72827526E-05 | 4 | 82080274E-05 | 0009249337 | 09906686 | ||||||||||||
TCGA-GBM | TCGA-02-0046 | 0 | 0999129 | 00008710656 | 0 | 09989637 | 00010362669 | 4 | 0004290756 | 0022799779 | 097290945 | ||||||||||||
TCGA-GBM | TCGA-02-0047 | 0 | 099991703 | 83008505E-05 | 0 | 09999292 | 70863265E-05 | 4 | 000016252015 | 0040118434 | 095971906 | ||||||||||||
TCGA-GBM | TCGA-02-0048 | 0 | 09998785 | 000012148175 | 0 | 099959475 | 000040527192 | 4 | 00002215901 | 000039696065 | 09993814 | ||||||||||||
TCGA-GBM | TCGA-02-0054 | 0 | 09999831 | 1689829E-05 | 0 | 09999442 | 5583975E-05 | 4 | 00010063206 | 0060579527 | 093841416 | ||||||||||||
TCGA-GBM | TCGA-02-0059 | -1 | 09993749 | 000062511285 | 0 | 09996424 | 00003576683 | 4 | 00007046657 | 0010920537 | 09883748 | ||||||||||||
TCGA-GBM | TCGA-02-0060 | 0 | 07197039 | 028029615 | 0 | 09016612 | 009833879 | 4 | 017739706 | 03728545 | 04497484 | ||||||||||||
TCGA-GBM | TCGA-02-0064 | 0 | 09999083 | 9170197E-05 | 0 | 09995073 | 000049264234 | 4 | 000043781495 | 00028024286 | 099675983 | ||||||||||||
TCGA-GBM | TCGA-02-0068 | 0 | 099187535 | 0008124709 | 0 | 099528164 | 00047183693 | 4 | 00030539853 | 059695286 | 039999318 | ||||||||||||
TCGA-GBM | TCGA-02-0069 | 0 | 09890871 | 0010912909 | 0 | 099704784 | 0002952148 | 4 | 00057067247 | 0061368063 | 09329252 | ||||||||||||
TCGA-GBM | TCGA-02-0070 | 0 | 09940659 | 00059340666 | 0 | 0957794 | 0042206 | 4 | 0008216515 | 003556913 | 09562143 | ||||||||||||
TCGA-GBM | TCGA-02-0075 | 0 | 099933076 | 00006693099 | 0 | 099735296 | 00026470982 | 4 | 000044697264 | 00035736929 | 09959793 | ||||||||||||
TCGA-GBM | TCGA-02-0085 | 0 | 099114406 | 0008855922 | 0 | 09756698 | 002433019 | 4 | 00065203947 | 0035171553 | 095830804 | ||||||||||||
TCGA-GBM | TCGA-02-0086 | 0 | 099965334 | 000034666777 | 0 | 0998698 | 00013019645 | 4 | 000032699382 | 00018025768 | 099787045 | ||||||||||||
TCGA-GBM | TCGA-02-0087 | -1 | 09974885 | 00025114634 | 0 | 09990638 | 000093628286 | 4 | 0007505083 | 0008562708 | 098393226 | ||||||||||||
TCGA-GBM | TCGA-02-0102 | 0 | 09797647 | 0020235319 | 0 | 098292196 | 0017078074 | 4 | 003512482 | 03901857 | 05746895 | ||||||||||||
TCGA-GBM | TCGA-02-0106 | -1 | 099993694 | 6302759E-05 | 0 | 099980897 | 000019110431 | 4 | 60797247E-05 | 00008735659 | 09990657 | ||||||||||||
TCGA-GBM | TCGA-02-0116 | 0 | 09999778 | 22125667E-05 | 0 | 09996886 | 00003113695 | 4 | 000015498884 | 000051770627 | 09993273 | ||||||||||||
TCGA-GBM | TCGA-06-0119 | 0 | 09999362 | 63770494E-05 | 0 | 09999355 | 6452215E-05 | 4 | 000013225728 | 00028902534 | 099697745 | ||||||||||||
TCGA-GBM | TCGA-06-0122 | 0 | 09915298 | 0008470196 | 0 | 09859093 | 00140907345 | 4 | 00121390615 | 027333176 | 071452916 | ||||||||||||
TCGA-GBM | TCGA-06-0128 | 1 | 099988174 | 000011820537 | 0 | 099980634 | 000019373452 | 4 | 000016409029 | 0007865882 | 099197 | ||||||||||||
TCGA-GBM | TCGA-06-0130 | 0 | 099998784 | 12123987E-05 | 0 | 09999323 | 6775062E-05 | 4 | 80872844E-05 | 00026260202 | 099729306 | ||||||||||||
TCGA-GBM | TCGA-06-0132 | 0 | 09998566 | 000014341719 | 0 | 099988496 | 000011501736 | 4 | 000072843547 | 0005115947 | 099415565 | ||||||||||||
TCGA-GBM | TCGA-06-0133 | 0 | 097782 | 002218004 | 0 | 0993807 | 00061929906 | 4 | 0026753133 | 004659919 | 09266477 | ||||||||||||
TCGA-GBM | TCGA-06-0137 | 0 | 096448904 | 003551094 | 0 | 099403125 | 0005968731 | 4 | 000649511 | 038909483 | 060441005 | ||||||||||||
TCGA-GBM | TCGA-06-0138 | 0 | 09977743 | 00022256707 | 0 | 099736834 | 0002631674 | 4 | 00032954598 | 0011606657 | 09850979 | ||||||||||||
TCGA-GBM | TCGA-06-0139 | 0 | 09992649 | 00007350447 | 0 | 099898964 | 00010103129 | 4 | 00021781863 | 00069256434 | 099089617 | ||||||||||||
TCGA-GBM | TCGA-06-0142 | 0 | 099909425 | 00009057334 | 0 | 09985896 | 00014103584 | 4 | 0002598974 | 0046451908 | 09509491 | ||||||||||||
TCGA-GBM | TCGA-06-0145 | 0 | 099964654 | 000035350278 | 0 | 0999652 | 000034802416 | 4 | 00009068022 | 0021991275 | 0977102 | ||||||||||||
TCGA-GBM | TCGA-06-0149 | -1 | 09992161 | 00007839425 | 0 | 09981067 | 00018932257 | 4 | 00057726577 | 0013888515 | 09803388 | ||||||||||||
TCGA-GBM | TCGA-06-0154 | 0 | 099968064 | 000031937403 | 0 | 0999729 | 000027106237 | 4 | 000041507537 | 023430935 | 07652756 | ||||||||||||
TCGA-GBM | TCGA-06-0158 | 0 | 09999199 | 8014118E-05 | 0 | 099992514 | 74846226E-05 | 4 | 00026547876 | 020762624 | 0789719 | ||||||||||||
TCGA-GBM | TCGA-06-0162 | -1 | 099964297 | 00003569706 | 0 | 09997459 | 0000254147 | 4 | 000033955855 | 004318936 | 09564711 | ||||||||||||
TCGA-GBM | TCGA-06-0164 | -1 | 09983991 | 00016009645 | 0 | 09873262 | 0012673735 | 4 | 00016517473 | 00048346478 | 09935136 | ||||||||||||
TCGA-GBM | TCGA-06-0166 | 0 | 099991715 | 82846556E-05 | 0 | 0999554 | 00004459562 | 4 | 000013499439 | 0011635037 | 098823 | ||||||||||||
TCGA-GBM | TCGA-06-0168 | 0 | 09975561 | 00024438864 | 0 | 09964825 | 00035174883 | 4 | 0004766434 | 010448053 | 089075303 | ||||||||||||
TCGA-GBM | TCGA-06-0175 | -1 | 09996252 | 000037482675 | 0 | 09988098 | 00011902251 | 4 | 00026097735 | 004992068 | 094746953 | ||||||||||||
TCGA-GBM | TCGA-06-0176 | 0 | 099550986 | 00044901576 | 0 | 09998872 | 000011279297 | 4 | 0032868527 | 036690876 | 06002227 | ||||||||||||
TCGA-GBM | TCGA-06-0177 | -1 | 081774735 | 018225263 | 0 | 09946464 | 00053536464 | 4 | 0026683953 | 013013016 | 08431859 | ||||||||||||
TCGA-GBM | TCGA-06-0179 | -1 | 09997508 | 000024923254 | 0 | 09989778 | 00010222099 | 4 | 0002628482 | 0004127114 | 099324447 | ||||||||||||
TCGA-GBM | TCGA-06-0182 | -1 | 099999547 | 45838406E-06 | 0 | 099998736 | 12656287E-05 | 4 | 00002591103 | 000018499703 | 09995559 | ||||||||||||
TCGA-GBM | TCGA-06-0184 | 0 | 09935369 | 00064631375 | 0 | 099458355 | 00054164114 | 4 | 0023110552 | 0017436244 | 09594532 | ||||||||||||
TCGA-GBM | TCGA-06-0185 | 0 | 09999337 | 66310655E-05 | 0 | 099986255 | 000013738607 | 4 | 7657532E-05 | 0016089642 | 098383385 | ||||||||||||
TCGA-GBM | TCGA-06-0187 | 0 | 09991689 | 00008312097 | 0 | 099700147 | 00029984985 | 4 | 00020616595 | 0033111423 | 096482694 | ||||||||||||
TCGA-GBM | TCGA-06-0188 | 0 | 09883802 | 0011619771 | 0 | 09826743 | 0017325714 | 4 | 0013776424 | 0112841725 | 087338185 | ||||||||||||
TCGA-GBM | TCGA-06-0189 | 0 | 099906737 | 0000932636 | 0 | 09983865 | 00016135005 | 4 | 00022760795 | 00106745735 | 09870494 | ||||||||||||
TCGA-GBM | TCGA-06-0190 | 0 | 099954176 | 000045831292 | 0 | 09967013 | 00032986512 | 4 | 000040555766 | 0001246768 | 099834764 | ||||||||||||
TCGA-GBM | TCGA-06-0192 | 0 | 09997876 | 00002123566 | 0 | 09992735 | 00007264875 | 4 | 00004505576 | 00014473333 | 09981021 | ||||||||||||
TCGA-GBM | TCGA-06-0213 | 0 | 099986935 | 00001305845 | 0 | 099971646 | 000028351307 | 4 | 8755587E-05 | 00013480412 | 09985644 | ||||||||||||
TCGA-GBM | TCGA-06-0238 | 0 | 09999982 | 17603431E-06 | 0 | 09999894 | 10616134E-05 | 4 | 8076515E-05 | 56053756E-05 | 099986315 | ||||||||||||
TCGA-GBM | TCGA-06-0240 | 0 | 09989956 | 00010044163 | 0 | 099948466 | 00005152657 | 4 | 00016040986 | 021931975 | 077907616 | ||||||||||||
TCGA-GBM | TCGA-06-0241 | 0 | 099959785 | 000040211933 | 0 | 099910825 | 00008917038 | 4 | 00023411359 | 0007850656 | 098980826 | ||||||||||||
TCGA-GBM | TCGA-06-0644 | 0 | 09871044 | 0012895588 | 0 | 09859228 | 00140771745 | 4 | 0013671214 | 009819665 | 088813215 | ||||||||||||
TCGA-GBM | TCGA-06-0646 | 0 | 099959 | 00004100472 | 0 | 099936503 | 000063495064 | 4 | 00019223108 | 0040443853 | 095763385 | ||||||||||||
TCGA-GBM | TCGA-06-0648 | 0 | 09999709 | 29083441E-05 | 0 | 099982435 | 000017571273 | 4 | 000077678583 | 000038868992 | 099883455 | ||||||||||||
TCGA-GBM | TCGA-06-0649 | 0 | 09997805 | 000021952427 | 0 | 099951684 | 000048311835 | 4 | 0042641632 | 00058432207 | 095151514 | ||||||||||||
TCGA-GBM | TCGA-06-1084 | 0 | 099985826 | 000014174655 | 0 | 099968565 | 00003144242 | 4 | 00002676724 | 020492287 | 079480946 | ||||||||||||
TCGA-GBM | TCGA-06-1802 | -1 | 09991928 | 00008072305 | 0 | 09956176 | 0004382337 | 4 | 000043478087 | 00019495043 | 09976157 | ||||||||||||
TCGA-GBM | TCGA-06-2570 | 1 | 096841115 | 0031588882 | 0 | 09842457 | 0015754245 | 4 | 0015369608 | 0030956635 | 09536738 | ||||||||||||
TCGA-GBM | TCGA-06-5408 | 0 | 099857306 | 00014269598 | 0 | 09962638 | 00037362208 | 4 | 00027690146 | 0016195394 | 098103565 | ||||||||||||
TCGA-GBM | TCGA-06-5412 | 0 | 099366105 | 0006338921 | 0 | 099193794 | 0008061992 | 4 | 0011476759 | 006606435 | 09224589 | ||||||||||||
TCGA-GBM | TCGA-06-5413 | 0 | 09994105 | 000058955856 | 0 | 09983026 | 00016974095 | 4 | 00027100197 | 0021083053 | 097620696 | ||||||||||||
TCGA-GBM | TCGA-06-5417 | 1 | 01521267 | 08478733 | -1 | 03064492 | 06935508 | 4 | 013736826 | 037757674 | 048505494 | ||||||||||||
TCGA-GBM | TCGA-06-6389 | 1 | 099987435 | 000012558252 | 0 | 09997017 | 000029827762 | 4 | 00014020519 | 00020044278 | 099659353 | ||||||||||||
TCGA-GBM | TCGA-08-0350 | 0 | 019229275 | 08077072 | 0 | 0033211168 | 09667888 | 4 | 0051619414 | 022280572 | 072557485 | ||||||||||||
TCGA-GBM | TCGA-08-0352 | 0 | 099997497 | 25071595E-05 | 0 | 099992514 | 74846226E-05 | 4 | 000024192198 | 000048111935 | 099927694 | ||||||||||||
TCGA-GBM | TCGA-08-0353 | 0 | 09901496 | 0009850325 | 0 | 09967775 | 0003222484 | 4 | 00053748637 | 0004291497 | 09903336 | ||||||||||||
TCGA-GBM | TCGA-08-0354 | 0 | 076413894 | 023586108 | 0 | 07554566 | 024454337 | 4 | 008784444 | 02004897 | 071166587 | ||||||||||||
TCGA-GBM | TCGA-08-0355 | 0 | 09998349 | 000016506859 | 0 | 099984336 | 000015659066 | 4 | 000076689845 | 0023648744 | 09755844 | ||||||||||||
TCGA-GBM | TCGA-08-0356 | 0 | 097673583 | 0023264103 | 0 | 097773504 | 0022264915 | 4 | 001175834 | 0031075679 | 095716596 | ||||||||||||
TCGA-GBM | TCGA-08-0357 | 0 | 099509466 | 0004905406 | 0 | 099300176 | 00069982093 | 4 | 0005191745 | 0038681854 | 095612645 | ||||||||||||
TCGA-GBM | TCGA-08-0358 | 0 | 099999785 | 2199356E-06 | 0 | 099999034 | 9628425E-06 | 4 | 6113315E-06 | 00011283219 | 09988656 | ||||||||||||
TCGA-GBM | TCGA-08-0359 | 0 | 097885466 | 0021145396 | 0 | 09956006 | 00043994132 | 4 | 0009885523 | 0066605434 | 092350906 | ||||||||||||
TCGA-GBM | TCGA-08-0360 | 0 | 09922444 | 00077555366 | -1 | 09948704 | 00051296344 | 4 | 0013318472 | 003317344 | 095350814 | ||||||||||||
TCGA-GBM | TCGA-08-0385 | 0 | 099605453 | 00039454065 | -1 | 099686414 | 0003135836 | 4 | 00050293226 | 0029977333 | 096499336 | ||||||||||||
TCGA-GBM | TCGA-08-0389 | 0 | 099964714 | 000035281325 | 0 | 09991272 | 000087276706 | 4 | 00017554013 | 00024730961 | 099577147 | ||||||||||||
TCGA-GBM | TCGA-08-0390 | 0 | 099945146 | 000054847915 | 0 | 099936 | 00006399274 | 4 | 00036811908 | 00050958768 | 0991223 | ||||||||||||
TCGA-GBM | TCGA-08-0392 | 0 | 099962366 | 000037629317 | 0 | 09993575 | 00006424303 | 4 | 000036593352 | 0010291994 | 09893421 | ||||||||||||
TCGA-GBM | TCGA-08-0512 | -1 | 09982893 | 00017106998 | 0 | 099193794 | 0008061992 | 4 | 00016200381 | 00027773918 | 09956026 | ||||||||||||
TCGA-GBM | TCGA-08-0520 | -1 | 099603915 | 00039607873 | 0 | 09981933 | 00018066854 | 4 | 00007140295 | 0019064669 | 09802213 | ||||||||||||
TCGA-GBM | TCGA-08-0521 | -1 | 09975274 | 0002472623 | 0 | 099490017 | 00050998176 | 4 | 0001514669 | 0020103427 | 09783819 | ||||||||||||
TCGA-GBM | TCGA-08-0522 | -1 | 099960107 | 000039899128 | -1 | 09992053 | 00007947255 | 4 | 0000269389 | 0006173321 | 09935573 | ||||||||||||
TCGA-GBM | TCGA-08-0524 | -1 | 09964619 | 0003538086 | 0 | 099620515 | 0003794834 | 4 | 000019140428 | 0010096702 | 09897119 | ||||||||||||
TCGA-GBM | TCGA-08-0529 | -1 | 09996567 | 000034329997 | 0 | 099952066 | 00004793605 | 4 | 000032077235 | 0035970636 | 09637086 | ||||||||||||
TCGA-GBM | TCGA-12-0616 | 0 | 098521465 | 0014785408 | 0 | 098704207 | 001295789 | 4 | 001592791 | 012875569 | 08553164 | ||||||||||||
TCGA-GBM | TCGA-12-0776 | -1 | 099899167 | 00010083434 | 0 | 09987031 | 00012968953 | 4 | 0019219175 | 00637484 | 09170324 | ||||||||||||
TCGA-GBM | TCGA-12-0829 | 0 | 099913067 | 00008693674 | 0 | 099821776 | 00017821962 | 4 | 00021031094 | 0055067167 | 09428297 | ||||||||||||
TCGA-GBM | TCGA-12-1093 | 0 | 099992585 | 7411892E-05 | 0 | 09999448 | 5518923E-05 | 4 | 000046803855 | 0012115157 | 098741674 | ||||||||||||
TCGA-GBM | TCGA-12-1094 | -1 | 09980045 | 00019955388 | 0 | 09866105 | 0013389497 | 4 | 00053194338 | 001599471 | 097868586 | ||||||||||||
TCGA-GBM | TCGA-12-1098 | -1 | 09998406 | 000015936712 | 0 | 09977216 | 00022783307 | 4 | 000010218692 | 0035607774 | 09642901 | ||||||||||||
TCGA-GBM | TCGA-12-1598 | 0 | 096309197 | 0036908068 | 0 | 097933435 | 0020665688 | 4 | 0012952217 | 052912676 | 045792103 | ||||||||||||
TCGA-GBM | TCGA-12-1601 | 0 | 09875683 | 0012431651 | -1 | 0991891 | 0008108984 | -1 | 00118053425 | 0105477065 | 088271755 | ||||||||||||
TCGA-GBM | TCGA-12-1602 | 0 | 099830914 | 00016908031 | 0 | 099858415 | 00014158705 | 4 | 0008427611 | 0025996923 | 09655755 | ||||||||||||
TCGA-GBM | TCGA-12-3650 | 0 | 09761519 | 0023848088 | 0 | 097467697 | 0025323058 | 4 | 0010450666 | 043705726 | 05524921 | ||||||||||||
TCGA-GBM | TCGA-14-0789 | 0 | 099856466 | 00014353332 | 0 | 099666256 | 00033374047 | 4 | 0001406897 | 0008273975 | 099031913 | ||||||||||||
TCGA-GBM | TCGA-14-1456 | 1 | 006299064 | 093700933 | 0 | 08656222 | 013437784 | 4 | 016490369 | 047177824 | 036331803 | ||||||||||||
TCGA-GBM | TCGA-14-1794 | 0 | 08579393 | 014206071 | 0 | 09850429 | 0014957087 | 4 | 0023009384 | 009868736 | 08783033 | ||||||||||||
TCGA-GBM | TCGA-14-1825 | 0 | 099960107 | 000039899128 | 0 | 099968123 | 00003187511 | 4 | 0008552247 | 0010156045 | 09812918 | ||||||||||||
TCGA-GBM | TCGA-14-1829 | 0 | 090690076 | 009309922 | 0 | 09907856 | 0009214366 | 4 | 0008461936 | 0102735735 | 088880235 | ||||||||||||
TCGA-GBM | TCGA-14-3477 | 0 | 099796116 | 0002038787 | 0 | 09990728 | 00009271923 | 4 | 00032272525 | 0021644868 | 09751279 | ||||||||||||
TCGA-GBM | TCGA-19-0963 | -1 | 099876726 | 00012327607 | 0 | 09983612 | 00016388679 | 4 | 00031698826 | 013153598 | 086529416 | ||||||||||||
TCGA-GBM | TCGA-19-1390 | 0 | 099913234 | 00008676725 | 0 | 099703634 | 00029636684 | 4 | 00015592943 | 0026028048 | 097241265 | ||||||||||||
TCGA-GBM | TCGA-19-1789 | 0 | 09809491 | 00190509 | 0 | 09915216 | 0008478402 | 4 | 0038703684 | 014341596 | 08178804 | ||||||||||||
TCGA-GBM | TCGA-19-2624 | 0 | 07535573 | 024644265 | 0 | 09816127 | 0018387254 | 4 | 012311598 | 012769651 | 07491875 | ||||||||||||
TCGA-GBM | TCGA-19-2631 | 0 | 099860877 | 0001391234 | 0 | 09981178 | 00018821858 | 4 | 00009839778 | 001843531 | 09805807 | ||||||||||||
TCGA-GBM | TCGA-19-5951 | 0 | 09999031 | 9685608E-05 | 0 | 099977034 | 000022960825 | 4 | 00020246736 | 0004014765 | 09939606 | ||||||||||||
TCGA-GBM | TCGA-19-5954 | 0 | 099456257 | 00054374957 | 0 | 09968273 | 00031726828 | 4 | 00073725334 | 006310084 | 09295266 | ||||||||||||
TCGA-GBM | TCGA-19-5958 | 0 | 099999475 | 5234907E-06 | 0 | 0999941 | 58978338E-05 | 4 | 35422294E-05 | 86819025E-05 | 09998777 | ||||||||||||
TCGA-GBM | TCGA-19-5960 | 0 | 09683962 | 003160382 | 0 | 09013577 | 009864227 | 4 | 0011394806 | 018114014 | 08074651 | ||||||||||||
TCGA-GBM | TCGA-27-1834 | 0 | 099998164 | 18342893E-05 | 0 | 09999685 | 31446623E-05 | 4 | 8611921E-05 | 000031686216 | 0999597 | ||||||||||||
TCGA-GBM | TCGA-27-1838 | 0 | 09993625 | 000063743413 | 0 | 09940428 | 0005957154 | 4 | 00006736379 | 0007191195 | 099213517 | ||||||||||||
TCGA-GBM | TCGA-27-2526 | 0 | 099983776 | 000016219281 | 0 | 09996898 | 000031015594 | 4 | 000016658282 | 00006323714 | 09992011 | ||||||||||||
TCGA-GBM | TCGA-76-4932 | 0 | 09867389 | 0013261103 | -1 | 09949397 | 0005060332 | 4 | 00007321126 | 0003016794 | 099625117 | ||||||||||||
TCGA-GBM | TCGA-76-4934 | 0 | 099318933 | 0006810731 | 0 | 09995073 | 000049264234 | 4 | 00061555947 | 00070025027 | 098684186 | ||||||||||||
TCGA-GBM | TCGA-76-4935 | 0 | 074562997 | 025437003 | 0 | 098242307 | 001757688 | 4 | 076535034 | 006437644 | 017027317 | ||||||||||||
TCGA-GBM | TCGA-76-6191 | 0 | 09981067 | 00018932257 | 0 | 09970879 | 00029121784 | 4 | 00044340584 | 00096095055 | 098595643 | ||||||||||||
TCGA-GBM | TCGA-76-6193 | 0 | 09966168 | 0003383191 | 0 | 099850464 | 00014953383 | 4 | 00037061477 | 007873953 | 09175543 | ||||||||||||
TCGA-GBM | TCGA-76-6280 | 0 | 099948776 | 00005122569 | 0 | 099908185 | 000091819017 | 4 | 00001475792 | 000846075 | 099139166 | ||||||||||||
TCGA-GBM | TCGA-76-6282 | 0 | 0995906 | 0004093958 | 0 | 099861956 | 00013804223 | 4 | 00006694951 | 0009437619 | 09898929 | ||||||||||||
TCGA-GBM | TCGA-76-6285 | 0 | 099949074 | 00005092657 | 0 | 09971661 | 00028338495 | 4 | 00031175872 | 004005614 | 095682627 | ||||||||||||
TCGA-GBM | TCGA-76-6656 | 0 | 09996917 | 000030834455 | 0 | 09983897 | 00016103574 | 4 | 002648366 | 00017969633 | 09717193 | ||||||||||||
TCGA-GBM | TCGA-76-6657 | 0 | 099987245 | 000012755992 | 0 | 099951494 | 00004850083 | 4 | 000096620515 | 0005599633 | 09934342 | ||||||||||||
TCGA-GBM | TCGA-76-6661 | 0 | 093211424 | 006788577 | 0 | 09640178 | 0035982177 | 4 | 003490037 | 0026863772 | 09382358 | ||||||||||||
TCGA-GBM | TCGA-76-6662 | 0 | 096425414 | 0035745807 | 0 | 09963924 | 0003607617 | 4 | 002845819 | 002544755 | 09460942 | ||||||||||||
TCGA-GBM | TCGA-76-6663 | 0 | 088664144 | 0113358565 | 0 | 09984207 | 00015792594 | 4 | 0010206689 | 043740335 | 05523899 | ||||||||||||
TCGA-GBM | TCGA-76-6664 | 0 | 011047115 | 08895289 | 0 | 09559813 | 004401865 | 4 | 00049677677 | 08806894 | 011434281 | ||||||||||||
TCGA-LGG | TCGA-CS-4941 | 0 | 088931274 | 011068726 | 0 | 087037706 | 012962292 | 3 | 002865127 | 0048591908 | 092275685 | ||||||||||||
TCGA-LGG | TCGA-CS-4942 | 1 | 00031327847 | 099686724 | 0 | 096309197 | 0036908068 | 3 | 096261597 | 00148612335 | 0022522787 | ||||||||||||
TCGA-LGG | TCGA-CS-4943 | 1 | 0005265965 | 099473405 | 0 | 09940544 | 00059455987 | 3 | 09439103 | 0023049146 | 003304057 | ||||||||||||
TCGA-LGG | TCGA-CS-4944 | 1 | 009363656 | 09063635 | 0 | 08755211 | 0124478824 | 2 | 034047556 | 033881712 | 03207073 | ||||||||||||
TCGA-LGG | TCGA-CS-5393 | 1 | 009623762 | 09037624 | 0 | 098178816 | 001821182 | 3 | 014111634 | 042021698 | 043866673 | ||||||||||||
TCGA-LGG | TCGA-CS-5395 | 0 | 08502822 | 014971776 | 0 | 09932025 | 00067975316 | 2 | 0052374925 | 018397054 | 076365453 | ||||||||||||
TCGA-LGG | TCGA-CS-5396 | 1 | 099839586 | 00016040892 | 1 | 099967945 | 000032062363 | 3 | 00016345463 | 029090768 | 07074577 | ||||||||||||
TCGA-LGG | TCGA-CS-5397 | 0 | 049304244 | 050695753 | 0 | 08829839 | 0117016025 | 3 | 038702008 | 021211159 | 040086827 | ||||||||||||
TCGA-LGG | TCGA-CS-6186 | 0 | 099913234 | 00008676725 | 0 | 099956185 | 000043818905 | 3 | 00008662089 | 016898473 | 083014905 | ||||||||||||
TCGA-LGG | TCGA-CS-6188 | 0 | 052768165 | 047231838 | 0 | 08584221 | 014157787 | 3 | 019437431 | 047675493 | 03288707 | ||||||||||||
TCGA-LGG | TCGA-CS-6290 | 1 | 09102666 | 008973339 | 0 | 09462997 | 0053700306 | 3 | 0104100704 | 025633416 | 06395651 | ||||||||||||
TCGA-LGG | TCGA-CS-6665 | 1 | 099600047 | 0003999501 | 0 | 099756086 | 00024391294 | 3 | 0011873978 | 001634113 | 097178483 | ||||||||||||
TCGA-LGG | TCGA-CS-6666 | 1 | 021655986 | 07834402 | 0 | 09327296 | 0067270435 | 3 | 017667453 | 036334327 | 045998225 | ||||||||||||
TCGA-LGG | TCGA-CS-6667 | 1 | 012061995 | 087938 | 0 | 095699733 | 0043002643 | 2 | 063733935 | 019323014 | 016943048 | ||||||||||||
TCGA-LGG | TCGA-CS-6668 | 1 | 0076787576 | 09232124 | 1 | 04240933 | 057590663 | 2 | 06810894 | 013706882 | 018184178 | ||||||||||||
TCGA-LGG | TCGA-CS-6669 | 0 | 08488156 | 01511844 | 0 | 094018847 | 005981148 | 2 | 0037862387 | 002352077 | 09386168 | ||||||||||||
TCGA-LGG | TCGA-DU-5849 | 1 | 005773187 | 094226813 | 1 | 08664153 | 013358466 | 2 | 072753835 | 015028271 | 012217898 | ||||||||||||
TCGA-LGG | TCGA-DU-5851 | 1 | 09963994 | 0003600603 | 0 | 099808073 | 00019192374 | 3 | 00060602655 | 012558761 | 08683521 | ||||||||||||
TCGA-LGG | TCGA-DU-5852 | 0 | 09998591 | 000014091856 | 0 | 099954873 | 000045121062 | 3 | 0002267452 | 00038046916 | 099392784 | ||||||||||||
TCGA-LGG | TCGA-DU-5853 | 1 | 0010986943 | 09890131 | 0 | 09549844 | 0045015533 | 2 | 08603989 | 0077804394 | 0061796777 | ||||||||||||
TCGA-LGG | TCGA-DU-5854 | 0 | 09567354 | 0043264627 | 0 | 098768765 | 0012312326 | 3 | 01194655 | 027027336 | 06102612 | ||||||||||||
TCGA-LGG | TCGA-DU-5855 | 1 | 0009312956 | 09906871 | 0 | 046602532 | 053397465 | 3 | 0008289882 | 097042197 | 0021288157 | ||||||||||||
TCGA-LGG | TCGA-DU-5871 | 1 | 005623634 | 09437636 | 0 | 09449439 | 005505607 | 2 | 042517176 | 020180763 | 037302068 | ||||||||||||
TCGA-LGG | TCGA-DU-5872 | 1 | 0062359583 | 09376405 | 0 | 015278916 | 08472108 | 2 | 012133307 | 048199505 | 039667192 | ||||||||||||
TCGA-LGG | TCGA-DU-5874 | 1 | 022858672 | 077141327 | 1 | 06457066 | 03542934 | 2 | 058503634 | 020639434 | 02085693 | ||||||||||||
TCGA-LGG | TCGA-DU-6397 | 1 | 097691274 | 002308724 | 1 | 09908213 | 0009178773 | 3 | 00048094327 | 00412339 | 09539566 | ||||||||||||
TCGA-LGG | TCGA-DU-6399 | 1 | 00023920655 | 099760795 | 0 | 09970073 | 00029926652 | 2 | 098691386 | 0007037292 | 0006048777 | ||||||||||||
TCGA-LGG | TCGA-DU-6400 | 1 | 0030923586 | 09690764 | 1 | 037771282 | 06222872 | 2 | 09710506 | 0015339471 | 001360994 | ||||||||||||
TCGA-LGG | TCGA-DU-6401 | 1 | 0014545513 | 098545444 | 0 | 045332992 | 054667014 | 2 | 0878585 | 006398724 | 005742785 | ||||||||||||
TCGA-LGG | TCGA-DU-6404 | 0 | 08563024 | 014369765 | 0 | 09857318 | 00142681915 | 3 | 0012578745 | 08931047 | 009431658 | ||||||||||||
TCGA-LGG | TCGA-DU-6405 | 0 | 094122344 | 0058776554 | 0 | 09657707 | 0034229323 | 3 | 0015099723 | 0858934 | 012596628 | ||||||||||||
TCGA-LGG | TCGA-DU-6407 | 1 | 00046772743 | 099532276 | 0 | 095787287 | 0042127114 | 2 | 095650303 | 0019410672 | 0024086302 | ||||||||||||
TCGA-LGG | TCGA-DU-6408 | 1 | 0032852467 | 09671475 | 0 | 02978783 | 070212173 | 3 | 046377006 | 04552443 | 008098562 | ||||||||||||
TCGA-LGG | TCGA-DU-6410 | 1 | 084198 | 015801999 | 1 | 09610981 | 0038901985 | 3 | 0029748935 | 0547783 | 042246798 | ||||||||||||
TCGA-LGG | TCGA-DU-6542 | 1 | 099541724 | 0004582765 | 0 | 099690056 | 00030994152 | 3 | 00036504513 | 0033356518 | 0962993 | ||||||||||||
TCGA-LGG | TCGA-DU-7008 | 1 | 00027017966 | 09972982 | 0 | 09924154 | 0007584589 | 2 | 0945233 | 0033200152 | 0021566862 | ||||||||||||
TCGA-LGG | TCGA-DU-7010 | 1 | 09090629 | 0090937115 | 0 | 083999664 | 016000335 | 3 | 0011747591 | 011156695 | 08766855 | ||||||||||||
TCGA-LGG | TCGA-DU-7014 | -1 | 00067384504 | 09932615 | 0 | 09144437 | 008555635 | 2 | 090214694 | 005846623 | 003938676 | ||||||||||||
TCGA-LGG | TCGA-DU-7015 | 1 | 011059116 | 08894088 | 0 | 09457512 | 005424881 | 2 | 04990067 | 023008518 | 027090812 | ||||||||||||
TCGA-LGG | TCGA-DU-7018 | 1 | 06190684 | 038093168 | 1 | 09720721 | 0027927874 | 3 | 002608347 | 03462771 | 06276394 | ||||||||||||
TCGA-LGG | TCGA-DU-7019 | 1 | 006866228 | 09313377 | 0 | 068647516 | 031352484 | 3 | 06280373 | 02546188 | 011734395 | ||||||||||||
TCGA-LGG | TCGA-DU-7294 | 1 | 039513415 | 06048658 | 1 | 04910898 | 05089102 | 2 | 044678423 | 011827048 | 04349453 | ||||||||||||
TCGA-LGG | TCGA-DU-7298 | 1 | 002178117 | 097821885 | 0 | 058896303 | 041103697 | 3 | 04621931 | 040058115 | 013722575 | ||||||||||||
TCGA-LGG | TCGA-DU-7299 | 1 | 0050494254 | 094950575 | 0 | 09805993 | 0019400762 | 3 | 088520575 | 003754964 | 0077244624 | ||||||||||||
TCGA-LGG | TCGA-DU-7300 | 1 | 020334144 | 07966585 | 1 | 021174264 | 078825736 | 3 | 06957292 | 014594184 | 015832895 | ||||||||||||
TCGA-LGG | TCGA-DU-7301 | 1 | 0028517082 | 09714829 | 0 | 07594931 | 024050693 | 2 | 07559878 | 013617343 | 010783881 | ||||||||||||
TCGA-LGG | TCGA-DU-7302 | 1 | 007878401 | 092121595 | 1 | 097414124 | 0025858777 | 3 | 059945434 | 013100924 | 02695364 | ||||||||||||
TCGA-LGG | TCGA-DU-7304 | 1 | 0049359404 | 09506406 | 0 | 09947084 | 0005291605 | 3 | 05746174 | 017312215 | 025226048 | ||||||||||||
TCGA-LGG | TCGA-DU-7306 | 1 | 0774658 | 022534202 | 0 | 09720191 | 0027980946 | 2 | 007909051 | 04979186 | 042299092 | ||||||||||||
TCGA-LGG | TCGA-DU-7309 | 1 | 002068546 | 097931457 | 0 | 091696864 | 008303132 | 3 | 091011685 | 0041825026 | 0048058107 | ||||||||||||
TCGA-LGG | TCGA-DU-8162 | 0 | 019030987 | 08096902 | 0 | 084344435 | 015655571 | 3 | 06724078 | 015660264 | 017098951 | ||||||||||||
TCGA-LGG | TCGA-DU-8164 | 1 | 0026989132 | 09730109 | 1 | 06119184 | 038808158 | 2 | 078654927 | 011851947 | 00949313 | ||||||||||||
TCGA-LGG | TCGA-DU-8165 | 0 | 099918324 | 000081673806 | 0 | 09982692 | 00017308301 | 3 | 00077142627 | 001586733 | 097641844 | ||||||||||||
TCGA-LGG | TCGA-DU-8166 | 1 | 0062617026 | 093738294 | 0 | 052265906 | 047734097 | 2 | 0571523 | 027175376 | 015672325 | ||||||||||||
TCGA-LGG | TCGA-DU-8167 | 1 | 008068282 | 09193171 | 0 | 08626991 | 013730097 | 2 | 07117111 | 014616342 | 014212546 | ||||||||||||
TCGA-LGG | TCGA-DU-8168 | 1 | 04501781 | 05498219 | 1 | 09405718 | 005942822 | 3 | 028535154 | 039651006 | 031813842 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TP | 1 | 013576113 | 08642388 | 0 | 098667485 | 0013325148 | 3 | 06805368 | 011191124 | 020755199 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TR | 1 | 0038810804 | 09611892 | 0 | 094154674 | 005845324 | 2 | 07418394 | 01198958 | 013826479 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TS | 1 | 036534345 | 06346565 | 0 | 097664696 | 0023353029 | 2 | 0076500095 | 07058904 | 021760948 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TT | 0 | 057493186 | 042506814 | 0 | 08586593 | 014134066 | 3 | 024835269 | 018135522 | 05702921 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TU | 1 | 017411166 | 082588834 | 0 | 08903419 | 0109658085 | 2 | 026840523 | 031951824 | 041207647 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TW | 1 | 00015382263 | 099846184 | 0 | 09784259 | 0021574067 | 3 | 099424005 | 00014788082 | 0004281163 | ||||||||||||
TCGA-LGG | TCGA-DU-A5TY | 0 | 099497885 | 000502115 | 0 | 09904406 | 0009559399 | 3 | 00076062134 | 003340487 | 09589889 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S2 | 1 | 01338958 | 08661042 | 1 | 010181248 | 08981875 | 2 | 08703488 | 0033631936 | 0096019216 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S3 | 1 | 007097701 | 092902297 | 1 | 0049773447 | 09502266 | 2 | 08236395 | 0043779366 | 013258114 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S6 | 1 | 00054852334 | 09945148 | 1 | 00052813343 | 09947187 | 2 | 095740056 | 0030734295 | 0011865048 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S7 | 1 | 00015218158 | 099847823 | 0 | 09977216 | 00022783307 | 3 | 097611564 | 0011231668 | 0012652792 | ||||||||||||
TCGA-LGG | TCGA-DU-A6S8 | 1 | 090418625 | 009581377 | 1 | 09320215 | 006797852 | 3 | 015433969 | 006605734 | 0779603 | ||||||||||||
TCGA-LGG | TCGA-EZ-7265A | -1 | 001654544 | 09834546 | -1 | 092290026 | 0077099696 | -1 | 091443384 | 0045349486 | 0040216673 | ||||||||||||
TCGA-LGG | TCGA-FG-5964 | 1 | 095945925 | 004054074 | 1 | 09480585 | 0051941562 | 2 | 0052469887 | 018844457 | 075908554 | ||||||||||||
TCGA-LGG | TCGA-FG-6688 | 0 | 041685596 | 0583144 | 0 | 0400786 | 0599214 | 3 | 032869554 | 028211078 | 03891937 | ||||||||||||
TCGA-LGG | TCGA-FG-6689 | 1 | 0040960647 | 09590394 | 0 | 088871056 | 01112895 | 2 | 078484637 | 01247657 | 009038795 | ||||||||||||
TCGA-LGG | TCGA-FG-6691 | 1 | 00066411127 | 09933589 | 0 | 09705485 | 002945148 | 2 | 082394814 | 01353293 | 004072261 | ||||||||||||
TCGA-LGG | TCGA-FG-6692 | 0 | 099044985 | 0009550158 | 0 | 098370695 | 0016293105 | 3 | 002482948 | 023811981 | 07370507 | ||||||||||||
TCGA-LGG | TCGA-FG-7643 | 0 | 067991304 | 032008696 | 0 | 094600123 | 0053998843 | 2 | 032237333 | 020420441 | 047342223 | ||||||||||||
TCGA-LGG | TCGA-FG-A4MT | 1 | 00037180893 | 09962819 | 0 | 098237246 | 0017627545 | 2 | 09685786 | 0019877713 | 0011543726 | ||||||||||||
TCGA-LGG | TCGA-FG-A6IZ | 1 | 0023916386 | 097608364 | 1 | 003330537 | 096669465 | 2 | 016134319 | 0751304 | 0087352775 | ||||||||||||
TCGA-LGG | TCGA-FG-A713 | 1 | 020932822 | 07906717 | 1 | 053740746 | 046259254 | 2 | 068370515 | 013241291 | 018388201 | ||||||||||||
TCGA-LGG | TCGA-HT-7473 | 1 | 026437023 | 073562974 | 0 | 09914391 | 0008560891 | 2 | 009070598 | 05834457 | 032584828 | ||||||||||||
TCGA-LGG | TCGA-HT-7475 | 1 | 0014885316 | 09851147 | 0 | 093397486 | 006602513 | 3 | 09713343 | 0009202645 | 0019462984 | ||||||||||||
TCGA-LGG | TCGA-HT-7602 | 1 | 0078306936 | 09216931 | 0 | 044295275 | 055704725 | 2 | 06683338 | 024550638 | 00861598 | ||||||||||||
TCGA-LGG | TCGA-HT-7616 | 1 | 0994089 | 0005911069 | 1 | 08912444 | 010875558 | 3 | 00015109215 | 00081261955 | 09903628 | ||||||||||||
TCGA-LGG | TCGA-HT-7680 | 0 | 01775255 | 08224745 | 0 | 079779327 | 020220678 | 2 | 06160002 | 021518312 | 016881672 | ||||||||||||
TCGA-LGG | TCGA-HT-7684 | 1 | 099250317 | 00074968883 | 0 | 09977216 | 00022783307 | 3 | 0001585032 | 0011880362 | 09865346 | ||||||||||||
TCGA-LGG | TCGA-HT-7686 | 1 | 043986762 | 05601324 | 0 | 09985134 | 00014866153 | 3 | 08800356 | 0017893802 | 010207067 | ||||||||||||
TCGA-LGG | TCGA-HT-7690 | 1 | 0508178 | 049182203 | 0 | 09986749 | 00013250223 | 3 | 007351807 | 06479417 | 027854022 | ||||||||||||
TCGA-LGG | TCGA-HT-7692 | 1 | 0006764646 | 09932354 | 1 | 00017718028 | 099822825 | 2 | 084003216 | 009709251 | 00628754 | ||||||||||||
TCGA-LGG | TCGA-HT-7693 | 1 | 08835126 | 011648734 | 0 | 098880965 | 0011190402 | 2 | 00389769 | 060259813 | 035842496 | ||||||||||||
TCGA-LGG | TCGA-HT-7694 | 1 | 006299064 | 093700933 | 1 | 06663645 | 03336355 | 3 | 06368097 | 02515797 | 011161056 | ||||||||||||
TCGA-LGG | TCGA-HT-7855 | 1 | 013434944 | 086565053 | 0 | 06805072 | 031949285 | 3 | 03900612 | 035394293 | 02559958 | ||||||||||||
TCGA-LGG | TCGA-HT-7856 | 1 | 0037151825 | 09628482 | 1 | 045108467 | 05489153 | 3 | 0024809493 | 094240344 | 0032787096 | ||||||||||||
TCGA-LGG | TCGA-HT-7860 | 0 | 09996338 | 000036614697 | 0 | 099890125 | 00010987312 | 3 | 00023981468 | 004139189 | 095620996 | ||||||||||||
TCGA-LGG | TCGA-HT-7874 | 1 | 027373514 | 07262649 | 1 | 061277324 | 03872268 | 3 | 030690825 | 04321765 | 026091516 | ||||||||||||
TCGA-LGG | TCGA-HT-7879 | 1 | 006545533 | 09345446 | 0 | 07643643 | 023563562 | 3 | 07316188 | 01349482 | 0133433 | ||||||||||||
TCGA-LGG | TCGA-HT-7882 | 0 | 099826247 | 00017375927 | 0 | 099920684 | 0000793176 | 3 | 00026636408 | 0011237644 | 098609877 | ||||||||||||
TCGA-LGG | TCGA-HT-7884 | 1 | 0045437213 | 09545628 | 0 | 09804874 | 0019512545 | 2 | 067944294 | 020575646 | 011480064 | ||||||||||||
TCGA-LGG | TCGA-HT-8018 | 1 | 0090937115 | 09090629 | 0 | 08061669 | 019383314 | 2 | 069696444 | 017501967 | 012801588 | ||||||||||||
TCGA-LGG | TCGA-HT-8105 | 1 | 09291196 | 0070880495 | 1 | 09865976 | 0013402403 | 3 | 036353382 | 007970196 | 055676425 | ||||||||||||
TCGA-LGG | TCGA-HT-8106 | 1 | 09987081 | 00012918457 | 0 | 099922514 | 00007748164 | 3 | 0019909225 | 0057560045 | 09225307 | ||||||||||||
TCGA-LGG | TCGA-HT-8107 | 0 | 006150854 | 09384914 | 0 | 018944609 | 08105539 | 2 | 071847403 | 015055439 | 013097167 | ||||||||||||
TCGA-LGG | TCGA-HT-8111 | 1 | 062096643 | 037903354 | 0 | 09220272 | 007797278 | 3 | 00015017459 | 090417147 | 009432678 | ||||||||||||
TCGA-LGG | TCGA-HT-8113 | 1 | 0003941571 | 099605846 | 0 | 00025608707 | 099743915 | 2 | 088384247 | 009021307 | 002594447 | ||||||||||||
TCGA-LGG | TCGA-HT-8114 | 1 | 09404078 | 005959219 | 0 | 09970708 | 00029292419 | 3 | 0015292862 | 028022403 | 07044831 | ||||||||||||
TCGA-LGG | TCGA-HT-8563 | 1 | 099999154 | 843094E-06 | 0 | 099999607 | 39515203E-06 | 3 | 32918017E-06 | 031742522 | 068257153 | ||||||||||||
TCGA-LGG | TCGA-HT-A5RC | 0 | 06915494 | 030845058 | 0 | 045883363 | 054116637 | 3 | 012257236 | 02777765 | 059965116 | ||||||||||||
TCGA-LGG | TCGA-HT_A614 | 1 | 08180474 | 018195263 | 0 | 09584989 | 00415011 | 2 | 0067164555 | 0059489973 | 087334543 | ||||||||||||
TCGA-LGG | TCGA-HT-A61A | 1 | 0035779487 | 09642206 | 0 | 07277821 | 0272218 | 2 | 07607039 | 014429174 | 009500429 |