Measurement Science and Engineering FUNDING PROVIDED BY NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY Informatics Services for Statistically Valid and

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FUNDING PROVIDED BY

NATIONAL INSTITUTE OF

STANDARDS AND

TECHNOLOGY

Informatics Services for Statistically Valid and Clinically Meaningful Discovery and

Performance Assessment of Quantitative Imaging Biomarkers

Kick-off RetreatOctober 13, 2010

Andrew J. Buckler, MSPrincipal Investigator

Agenda: Informational Part10:00 AM 1. Welcome and Opening Comments Ram Sriram, PhD

2. Intro ITL Program at NIST Mary Brady, MS

10:20 AM 1. Summary of Grant Andrew Buckler, MS2. Review of Kick-off Objectives3. QIBA and Special Report on pathways4. Test Bed Application

11:00 AM Kitware Rick Avila, MS

11:20 PM Consideration of physical standards projects Bill Ott, PhD

11:40 AM FDA Perspective on Quantitative Imaging Nick Petrick, PhDand the evaluation of performance

12:00 PM Lunch

12:45 PM 1. UMD SOM Grant Objectives Eliot Siegel, MD2. NBIA and AVT

October 13, 2010 Informatics Services for Quantitative Imaging 2

Imaging Biomarkers Must Be…

• Objective and unbiased

• “Proven” – in 2 senses:– Analytical Performance

• Accurate• Precise / Reproducible

– Clinical Relevance• Correlation with clinical measure for intended use

– Clinical care (“Cleared” or “Approved” by FDA - CDRH)– Clinical trials (“Qualified” by FDA - CDER)


Performance Assessment

Metrology view:• A measurement result is

complete only with a quantitative statement of its reproducibility.

• The reproducibility is required to assess whether the result is adequate for its intended purpose and how it compares with alternative methods.

Applied to QI:• “Qualification for Use”

establishes the clinical and regulatory relevance for the intended application of the biomarker:– In a defined patient

population– For a specific mechanism

of action

Another way of saying “qualified for use” is “fit for purpose”

June 2010 Buckler Biomedical LLC 4October 13, 2010 4Informatics Services for Quantitative Imaging

May 2010 Buckler Biomedical LLC 5

What is the potential to decrease time needed on trial (or enable more adaptive designs)?

What is the technical variability in the measurements?

What is the potential to increase statistical power (or decrease enrollment for same power)?

…not to mention, does it correlate to clinical outcomes?

Key Questions in Assessing Performance

October 13, 2010 5Informatics Services for Quantitative Imaging

Reference object(s) and Support Material(s) for Experimentation and Quality Control (QC)

• Includes phantoms traceable to recognized physical standards as well as controlled digital reference objects.

• Both healthy and disease states must be reflected across the range expected in the full target population.

• Serves as the basis for “stand-alone” assessment.

• Implementation of a comprehensive QC program, including data analysis and reporting requirements.


• Describe the processing hardware and software, highlighting potential weaknesses.

• Define operating points, thresholds, and use protocols for acquisition, analysis, interpretation, and QC.

September 2010 Buckler Biomedical Associates LLC 7

• Develop/merge training and test datasets.

• Algorithms included must be pre-specified before the study data is analyzed.

JointDecision

Decision

Decision

Calibration

Transmission Medium

AnatomicalRegion

Source ofIllumination SENSOR

SpecificTarget

Source ofIllumination SENSOR

Calibration

Processing &Info.Extraction

Processing &Info.Extraction

Data

Association


Formal Marker Definition to Enable Testable Hypotheses

• Assess intrinsic scanner variability, minimum detectable change, and limits of quantification.

• Include biological subject variability.• Collect and summarize data to support proposed cut-points

(i.e., decision thresholds).


• Prospective trials or collections of samples may be necessary for certain intended-use claims or to exclude biases.

length

densityNormal distribution

σLyNA/S

σExperts

Preferred/expected results:σLyNA/S <σExperts

Δ XLyNA/S/Xexperts à 0

XLyNA/S

XExperts


Systems Engineering and Metrology-based Characterization

• Performance data on a properly-sized data set that represents a true patient population on which the test will be used.


• For most novel devices or imaging agents, this is the pivotal clinical study that will establish whether performance is adequate.

machine viewhuman exp

ert view

Transformation

Modality Environment

Therapy DecisionEnvironment

Patient Patient

Transformation Transformation

FeedbackTherapy-

Machine Human Observer


Intra- and Inter-reader Test-retest Statistics in Real Operating Conditions

Specific Aims

1. Define use model and requirements (to cover both development and testing workflows).– Develop use cases that rationalize needs and activities of stakeholders.– Inventory of existing solutions and propose architectural approach.

2. Design and build services based on Aim 1.– Develop a demonstrator. – Develop the services.

3. Apply services for proficiency testing with NIST as neutral broker.– Develop well-characterized standard methods, validation procedures, and

reference materials or objects (phantoms) traceable to NIST-maintained standards.

– Develop a sequestered database for stand-alone assessment of clinical performance of an imaging device (including software) and services to test appropriate performance criteria.


Approach: Use Case Analysis and Requirements Flowdown

The way most users probably think of it

(Abstracted to facilitate translation into UML)

(Specific mapping to technology solutions)

Enterprise Use Case

Basic Story Board

System Use Cases


Approach: Architecture and Design


Approach: Relationship among Groups and Roles/Responsibilities


Trusted Broker

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QIBA Methods

Training set Test setQualification project teams

Suppliers

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Briefing document and full data

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Use Case 1: Algorithm Development Supported by Data Sets Utilized for Training

Use Case 2: FDA Qualification of imaging biomarkers as clinical endpoints in clinical trials

Use Case 3: Comparative evaluation of imaging biomarker performance vs. gold standards or otherwise accepted markers

Use Case 4: FDA Approval or Clearance of imaging tests with strong clinical claims for market

Grant Deliverables

Aim 1:– Structured and Unstructured Documentation: Basic Story Board– Structured and Unstructured Documentation: Enterprise Use Case– Structured and Unstructured Documentation: System Use Cases

Aim 2:– Open-source Software: Demonstrator– Open-source Software: Profile Editor– Open-source Software: MIDAS interfaced with the NBIA database– Open-source Software: Batch processing workflow– Open-source Software: “Dashboard” in MIDAS– Open-source Software: Integrate the “R” statistical package

Aim 3:– Procedures: standard methods, validation procedures, and reference materials– Database: Sequestered Test Set


Grant Timeline


Review of Kick-off Objectives

• Understand what we’re working to achieve• Establish working relationships• Identify the stakeholders• Dive in to the first activities:

– Use case analysis– Demonstration architecture

• Develop a high-level milestone plan• Leave knowing what to do next


QIBA

• Validation and qualification of requires rigorous application of metrology, medical insight, and collaborative action across diverse stakeholder groups.

• Standardization, coordination with regulatory agencies, and a compliance mechanism are the means to incentivize industry.

• QIBA is established to bring critical mass to this activity.

• Mission: Improve value and practicality of quantitative imaging biomarkers by reducing variability across devices, patients, and time.

Build “measuring devices” rather than “imaging devices”.

“Industrialize” imaging biomarkers

• An organizing principle, step-wise methodology, and a procedural template including qualification roadmap have been established.


Ground work

Profile Claims

Clinical Context

Profile Details

With an IHE-likecertificationreputation


The QIBA Process Draws from Precedent but is Based on Science

QIBA Profile Content

Claims: Detect tumor response with twice the sensitivity of RECIST for Lung nodules > 1cm …

Actors Table CT Acquisition System Measurement Software Radiologist …

Activity Definitions … Calibration / QA Patient Preparation Image Acquisition Reconstruction Post-Processing Analysis / Measurement Reading / Interpretation …

User Perspective:

Will it do what I need?

What/who do I needto get started?

What do I have to do(procedures, training,performance targets)

to achieve the Claims?

Vendor View:

Why do you want me to do this?

Which of my productsare affected?

What do I have to implement;(features, capabilities,

performance targets)How will I be tested?

Details:


Profile Details (what equipment and users must do to achieve it)The Profile defines the following roles and several transactions and activities they participate in:

Acquisition System Measurement System Measurer ...

Activity: Acquisition System CalibrationDetail: Site staff shall conform to the QA program defined by the device manufacturer.

Activity: Patient PreparationDetail: Staff shall prepare the patient according to the local standard of care. Precursor: Decide if we need/can be prescriptive about any of the details in efforts to "standardize human behavior" or local procedures. Some protocols call for patient to be "comfortably positioned", in "comfortable clothes" at "comfortable temperature" and with an empty bladder; presumably to minimize patient motion and stress (which might affect the imaging results) and any unnecessary patient discomfort. Detail: The following details shall be recorded in the <???> System, manually by the Staff if necessary. Contrast administration: (Agent, dose, route) The standards for this are currently evolving. To be comparable (e.g. to subtract to get change values), measurements must be made under consistent contrast administration. Consistency includes contrast type, route of administration, rate of administration, interval between start of contrast and start of scan, use of power injector? Requiring no contrast (like ACRIN 6678 did) would be less of an issue than requiring contrast which has potential patient health issues. Creatinine Clearance: (renal function). Patient Positioning: Target: Supine/Arms Up/HeadFirst Acceptable: any but not inconsistent with prior scan Consistency is required to avoid unnecessary variance in attenuation and gravity induced shape. Target is provided as a default to drive some consistency when details of prior scans are not available. Breath Hold: Target: Single Breath Hold at Full Inspiration Acceptable: suspended respiration with high % of end inspiration = single breath hold at full inspiration, right? Breath hold reduces motion which degrades the image. Full inspiration inflates lungs which is necessary to separate structures and make lesion more conspicuous. Bladder State: Target: empty bladder Acceptable: any The target here is purely for patient comfort. Precursor: How should the details be recorded about the preparation of each actual patient? DICOM provides a way to encode most of these details in the image headers, but we may need to require the operator to enter them.

Activity: Image AcquisitionDetail: The acquisition system shall support saving and easily calling up saved acquisition protocols. Precursor: Do we need standard naming? Could use UPICT or ACRIN proper name. Sites might prefer “site recognizable” aliases (but need to still know it is the prescribed protocol) Detail: The acquisition system shall produce images with the following characteristics: Precursor: Determine which characteristics of the resulting images matter? Slice width - Ideal: <= 1 mm Target: 1-2.5mm Acceptable: 5mm

direct component of voxel size; determines resolution along patient (z) axis Slice interval - Ideal: contiguous or 20% overlap Target: contiguous or 20% overlap Acceptable: contiguous

gaps may "truncate" the spatial extent of the tumor Isotropic Voxels - Ideal: yes Target: yes Acceptable:

isotropic voxels reduce the volume measurement error effect of tumor orientation (which is difficult to control) requiring isotropic voxels means requiring that the same value be selected for both slice width and voxel size. would it ever be acceptable to allow a slight difference in the values, e.g. 1mm slice width, .8mm voxel size? Anatomical Coverage E.g. Above lung apices to symphysis pubis Field of View:Voxel Size - Ideal: Rib-to-rib: 0.55mm - .75mm Target: Outer Thorax: 0.7mm - .8mm. Acceptable: Complete Thorax: 0.8 to 1.0mm smaller voxels reduce partial volume effects and (likely) provide higher precision (i.e. higher spatial resolution) but larger voxels increase field of view and thus encompass more anatomy

Scan Plane - 0 azimuth Motion Artifact - Ideal: no artifact Target: no artifact Acceptable: "minimal??“ motion artifacts may produce false targets and distort the size of existing targets Noise Level - Ideal: "minimal?" Target: "low" Acceptable: "predictable?“ greater levels of noise may degrade segmentation by humans or algorithms

<NOT sure how to deal with this one either; the noise changes with square root to slice thickness, so thinner slices will not be low noise; also recon algorithm affects noise just as significantly. can I pair these up? How about if I do pair slice thickness and noise? see below> Slice width and Noise - Ideal: <= 1 mm, std. deviation in 20 cm water phantom < 40 HU Target: 1-2.5mm, std. deviation in 20 cm water phantom < 40 HU Acceptable: 5mm, std. deviation in 20 cm water phantom < 40 HU slice width determines voxel size and resolution in longitudinal (z) direction of patient; also has a significant impact on noise - thinner slices have much higher noise than thicker slices for a given mAs (or effective mAs) setting. Here noise is recommended to be constant across slice thickness; this would be accomplished by increasing mAs for thinner slices and reducing for thicker slices. Spatial Resolution: Ideal: 7-8 lp/cm Target: 6-8 lp/cm Acceptable: 6-8 lp/cm Resolution is the number of resolvable line-pairs per cm in a scan of an ACR resolution phantom (or equivalent) Higher spatial resolution is necessary to distinguish borders of tumors Spatial resolution is determined by scanner geometry (not under user control) and reconstruction algorithm (which is under user control.

Detail: The acquisition system shall support configuration of the following acquisition parameters: Precursor: Determine which acquisition parameters matter KVP - Ideal: 120 Target: 110-130 Acceptable: 110-140

kVP determines contrast between tissues and also influences noise and radiation dose; should be consistent for all scans of patient effective mAs (medium patient) - Ideal: 80 to 120 Target: 60 to 200 Acceptable: 40 to 350 effective mAs (large patient) - Ideal: Target: Acceptable: effective mAs = (mA*time/pitch) higher mAs lowers noise but increases dose Rotation Speed - Ideal: Target: Acceptable: faster rotation reduces the breath hold requirements and reduces the likelihood of motion artifacts Collimation width - Ideal: 20 to 40 mm Target: 10 to 80 mm Acceptable: 5 to 160 mm wider collimation widths can increase coverage and shorten acquisition, but can introduce cone beam artifacts which may degreade image quality # of channels - Ideal: 64 or greater Target: 16 or greater Acceptable: 1 or greater Mod

do we need to specify? Some protocols call for "Helical Mode", but if the detector is wide enough to span the anatomy there is no need to do helical Some protocols call for "High Speed Mode". Do we need to state this if we already state pitch?

Table speed do we need to specify? E.g. 7.5mm/sec? Not needed if no helical. If it is helical, what is the purpose of specifying? Is it to imply the intended length of scan time vis-a-vis breath hold? Recon. Kernel Characteristics: - Ideal: slightly enhnacing Target: standard to enhancing Acceptable: soft to overenhancing <the relationship between kernel characteristics and our goals/claims is likely complex. What can we say or at least identify as needing investigation> Recon. Kernel Name – informational Scanner Model - informational

indicates the model has been used successfully with the described parameters Precursor: What value ranges for each parameter constitute an acceptable “baseline”? Late Stage (IIIb and IV) Lung Cancer in World Wide Clinical Trials (pharma base case): Outer ring of quality must be RI = 5 mm; next ring RI = 3 mm. Inner ring specified by Professor Mulshine and colleagues for top-shelf clinical trials of neoadjuvant therapy in earlier stage disease at RI < 1.5 mm. Note some earlier stage NSCLC trials done with radiofrequency ablation (RAPTURE, R. Lencioni, PI) included some stage I cancers, with all lesions < 3.5 cm} Some parameters need ranges to "normalize" results across different patient sizes Consider existing protocols: ACRIN 6678 Quality Control Parameters for CT Scan Tumor Volumetric Measurements specified: (Slice width, Slice interval, Voxel Size, Absence of Motion Artifact) & (KVP, mAs, Rotation Speed, Collimation width, # of channels, Scanner Model, Recon. Algorithm, Non-use of Intravenous Contrast) Note: many parameters are specified as a range, some depending on the size of the patient NLST (National Lung Screening Trial) Acquisition Parameters specified: (Slice width, Slice interval, # of Images) & (KVP, mA, mAs, Effective mAs, Rotation Speed, Collimation width, # of channels, Detector "width", "MODE", Pitch, Table increment, Table speed, Scan time, Scanner Model, Recon. Algorithm, Dose) Note: some of these parameters are redundant (i.e. can be calculated from other parameters), many parameters are specified as a range, some depending on the size of the patient The ACRIN protocol may be prefereable since NLST was for a screening study, not for measuring progressive disease <Insert link to UPICT protocol specifications by Professor McNitt-Gray and colleagues>?

RAPTURE Trial Phase II trial NCT00690703 (now closed) at ClinicalTrials.Gov Perhaps Dr. Lencioni could suggest methods which would have helped him assess the results in this trial?

Precursor: What uniform language should be used for documenting image acquisition protocols in the profile. DICOM is working on a new object for storing protocols electronically (prescribed or performed) Perhaps Manufacturers should provide CDs with acquisition parameters as they did for the MRI study of brain volumes to the Alzheimer's Disease Neuroimaging Trial sponsored by the Foundation for NIH UPICT is working on common terms for the protocol parameters and possibly a standard presentation CT Acquisition Protocol Groundwork

Activity: Image Reconstruction<Is there any reason not to fold the Image Reconstruction activity into the Image Acquisition activity and just treat them as a pair? Is there any need/value for them to be separate?> E.g. what kernel to use? Kernel will be important. Even more so in liver than lung, and in spinal mets assessments. Detail: The acquisition system shall be able to perform reconstruction with the following parameters: Reconstruction interval: 5mm without any gaps Kernel: <???> Further discussion may be necessary. Some sites complain that when compared to slices with an 8mm interval and a 5mm gap (a common clinical standard), the use of a 5mm interval with no gap slows down throughput and increases reading time, radiation exposure and storage requirements. It seems likely that 5mm interval with no gap is necessary to achieve the claims, so the related costs are unavoidable and manageable. This may also tie in to specifying the characteristics of the resulting images rather than the parameters of reconstruction for certain makes/models. Specify what to achieve, rather than how to achieve it. Transaction: Transfer ImagesDetail: The acquisition system shall support DICOM CT Storage as SCU. Detail: The measurement system shall support DICOM CT Storage as SCP and DICOM Q/R as SCU

Activity: MeasurementDetail: The measurement system shall support the following measurements: Precursor: What measurements are useful for evaluating lung tumors Bitvol <because it is the typical “detailed” volume measurement> RECIST <because it is the current gold standard and we need it to compare> Modified RECIST (J. Natl. Cancer Inst. 2008;100:698-711) <to support wider cancer etiology than HCC> <consider just adding a bunch of tools if they are easy to implement> Precursor: What types of cases/issues must the measurement system demonstrate being able to handle? E.g. attachment points, Precursor: What accuracy is initially sufficient to be useful? Precision of measurement is the primary objective. Accuracy is less important to the base case for pharma. Accuracy becomes increasingly important to the inner rings of quality, reaching its maximum in screening studies of asymptomatic people with risk factors for lung cancer. Precursor: What repeatability is initially sufficient to be useful? Precursor: What accuracy/repeatability can be easily achieved? <Insert link to relevant Groundwork> <Insert link to very preliminary image analysis in very-best-case-scenario of extremely well demarcated, simple lung tumors which suggest test-retest variability is less than 1% when RI is 5 mm> Precursor: What is the theoretical limit for accuracy/repeatability with typical equipment <Insert link to relevant Groundwork> <Insert links to image analysis of well demarcated tumors in the MSKCC coffee break images as the most optimistic boundary, and analysis of complex masses invading solid tissues as the most realistic boundary. First link will be to image analysis by RadPharm, Inc. Other links will be provided by software developers as the data become available. Detail: The measurer shall be able to diagnose Progressive Disease at one half the change in volume associated with RECIST line-lengths. Precursor: What do we need to specify about the measurer? Human oversight will be required. In the first stage, a trained technologist or image analysis specialist will select tumors for automatic boundary demarcation. In the next stage, the image analysis specialist will be able to manually correct portions of the boundary where either the algorithm failed or the mass becomes too complex to reliably follow over the course of treatment. In the final stage, a trained radiologist will accept or revise the mark ups. Precursor: What is the limit on accuracy/repeatability due to the measurer (reader)? The limit of inter-rater reliability will be such that thresholds for diagnosing Progressive Disease will be within one time-point assessment in a series of time-points for patients enrolled in longitudinal trials. The need for adjudication between discrepant time-point assessments will be less for volumetric image analysis than for ordinary RECIST 1.1 assessments. See 1A Reader Variability Study <Should we add an Activity: Measurer Training to train/confirm the skill of each measurer>

Transaction: Transfer MeasurementsDetail: The Measuring System shall support storage of the measurements in <???> format. Detail: The Measuring System shall support storage of the segmentation in <???> format. Precursor: Need to choose Segmentation and Markup Formats ... Consider CDISC as a way to require measurement systems to provide data in a format that is easily consumed by Clinical Trials systems/databases. Note that CDISC has done image work on CT Oncology (related to RECIST). Might not be interested in CDISC change categories, but the measurements they specify is useful (have included volume). <Insert link to CDISC imaging work> IHE has worked with CDISC on some general IT profiles and so there may be some IHE transactions we could borrow.

Profile Details

Parameter ComplianceLevel *Collimation Width

Acceptable 5 to 160mmTarget 10 to 80mmIdeal 20 to 40mm

Slice Interval Acceptable Contiguous or up to 20% overlap

Slice Width Acceptable <= 5.0mmTarget 1.0 to 2.5mmIdeal <= 1.0mm

Pixel Size See 7.1.1Isotropic Voxels

Acceptable Slice width <= 5 x Pixel SizeTarget Slice width = Pixel Size

Scan Plane Acceptable Same for each scan of subject

Target 0 azimuthRotation Speed

Acceptable Manufacturer’s default

Result: Document indicating what must be done to meet the Claims, based on the Groundwork


QIBA Process – “Industrializing Biomarker Use”QIBA Process – “Industrializing Biomarker Use”

AcademicUse

ClinicalTrialUse

ClinicalPractice

Use

• Transformational – addresses gap; impacts public health• Translational – concept proved; ready to advance• Feasible – good chance to succeed in near term • Practical – leverages existing resources and technology• Collaborative – engages HW/SW/Agent stakeholders

• Identify significant sources of variance• Estimate achievable repeatability and accuracy • Validate underlying assumptions and mechanisms• Determine details critical to specify in the Profile

• Document the agreed parameters and procedures• Converge practice; reduce gratuitous variation• Initiate regulatory engagement

• Specify details necessary to be robust in general use• Drive out any impeding variance and complexity• Make details stable, clear, implementable, testable

• Test compliance with QIBA Profile specifications• Publish validated products/sites


QIBA: Active in Several Aspects

QIBA PROFILE

I. CLINICAL CONTEXT II. CLAIMS III. PROFILE DETAILS IV. COMPLIANCE SECTION V. ACKNOWLEDGEMENTS

UPICT Profiles (Target Concept)


Acceptable

Target

Ideal

Determining Which Parameters (Slice Thickness, Recon Algorithm, etc.) Affect Measurement Variability

QIBA: Active in Several Aspects

QIBA PROFILE


Analyzing/Creating Data to Inform Profiles

QIBA Experiments and GroundworkQIBA Experiments and Groundwork


Analyzing:Effects of Measurement Methods

• 1D, 2D, 3D•Effects of Slice thickness

• Phantoms•Apply to Patient Images

• e.g. Coffee Break Experiment• Standardization across scanners

Analyzing:Effects of Measurement Methods

• 1D, 2D, 3D•Effects of Slice thickness

• Phantoms•Apply to Patient Images

• e.g. Coffee Break Experiment• Standardization across scanners

Profiles are actionable for both Marketing and R&D

QIBA PROFILE


PRODUCT CREATION PROCESS (PCP)

Customer Requirements

Specification (CRS)

Customer Requirements

Specification (CRS)

System Requirements

Specification (SRS)

System Requirements

Specification (SRS)

Verification Plan and Protocol

Verification Plan and Protocol

Participation and visibility


Provide a regulatory pathway that works in the business model

Quantitative Imaging Test Approval[National regulatory agencies, e.g., FDA CDRH]

Evidentiary Studies for Coverage Decisions

[Payer organizations, e.g., CMS]

Quantitative Imaging Test Qualification

[National regulatory agencies, e.g., FDA CDER]

Feedback path to provide evidence to extend initial intended use for new, stronger, clinical claim

Initial intended use now extended to stronger association with mechanism-of-action or surrogacy

Intended use (usually initially having no claim of surrogacy but which could be extended if further

clinical data could be collected)

Reimbursable based on accumulated evidence of necessary and reasonable use

Quantitative Imaging Test Discovery, Development, and Validation[Private & Academic Sectors]

Use in Routine Clinical Care

Use in Clinical Research

Path when use is established in clinical trials first (though feedback path would allow its use in clinic later)

Path when clinical use is pursued first (though can proceed to qualification later)


“Qualification Data” is structured to be responsive to regulatory needs

Qualification Data (filed to support regulatory filings for both suppliers and users)I. Technical Characteristics. Quantification of test-retest intra-and inter-rater reliability.II. Establish standards for using 3D volumetric imaging in a retrospective clinical trial.

A. Level of performance adequate for using 3D volumetric analysis in a clinical trial Stage IIA1. The effect size required to classify a change in the volume of a small pulmonary nodule as malignant, i.e., the difference in volume between within subject measurements at Time

1 versus Time 2.Stage IIA2: To quantify the effect size that is required to cross thresholds for a treatment-induced responses in categorical assessments, such as "Partial Response" and "Disease Progression". B Appropriate imaging acquisition standards for use of 3D volumetric analysisC What type of evaluations are necessary to validate the use of 3D volumetric imaging

III. Diagnostic Accuracy. Begin with a single expert per software package who will work under ideal conditions with high resolution images. Use RIDER data sets to derive Kappa statistics, receiver-operator-characteristic (ROC) curves, likelihood ratios, etc.

A. Quantification of sensitivity and specificity in distinguishing categorical response variables, including Partial Response (PR), Stable Disease (SD), and Progressive Disease (PD). <specific procedure, evaluation method…>

Data collection requiredMarkup requirements

Approach to using dataB. Correlation between 3D image analysis and "latent gold standard", i.e., RECIST <specific procedure, evaluation method…>

IV. Progress to multiple image analysts.<specific procedure, evaluation method…>

V. Progress to "real world" image resolution.<specific procedure, evaluation method…>

VI. Efficacy & Effectiveness. Formal estimate of the value from 3D volumetric image analysis versus latent standard (RECIST) in terms of A. Increased analytical power per subject, B Length of time each subject needs to stay on trial, and C Cycle time required to make critical GO or NO GO decisions about drugs.The specific aim will be to compare time-dependent outcome measures based on RECIST to outcome measures based on volumetric analyses, such as time to response and progression free

survival for (1) individual subjects, and (2) the sample as a whole before the trial concludes a drug is either effective or futile.


• Qualifications• Pharma data request• Corporate visits• Explore honest broker concept• Performance evaluation manuscript(s)

Current Program-level Projects


• Requests tendered, BQRTs formed.

• In partnership with FNIH/ Biomarkers Consortium.

• Expect to meet with BQRTs late this year or early 2011.

• Requests tendered, BQRTs formed.

• In partnership with FNIH/ Biomarkers Consortium.

• Expect to meet with BQRTs late this year or early 2011.

• Idea originated as partnership with PCF meeting in May.

• Objective is to assemble potentially large number of cases for retrospective and meta-analysis to support regulatory filings.

• Preparation and logistics of the request being developed with PhRMA Imaging Group and OIA committee.

• Idea originated as partnership with PCF meeting in May.

• Objective is to assemble potentially large number of cases for retrospective and meta-analysis to support regulatory filings.

• Preparation and logistics of the request being developed with PhRMA Imaging Group and OIA committee.

• Objective is to engage multiple levels of management at “imaging innovators” on how QIBA enables win-win scenarios on the development and optimization of equipment for quantitative performance.

• Consistent with goals of MITA Biomarkers Taskforce.

• Objective is to engage multiple levels of management at “imaging innovators” on how QIBA enables win-win scenarios on the development and optimization of equipment for quantitative performance.

• Consistent with goals of MITA Biomarkers Taskforce.

In partnership with:

Specific aims: 1. Define use model and requirements for

discovery and testing workflows.2. Design and build services based on Aim

1.3. Apply services for data proficiency

testing.

Could provide means for integrated phantom and digital archive compliance testing contributory to regulatory filings by sponsors.

In partnership with:

Specific aims: 1. Define use model and requirements for

discovery and testing workflows.2. Design and build services based on Aim

1.3. Apply services for data proficiency

testing.

Could provide means for integrated phantom and digital archive compliance testing contributory to regulatory filings by sponsors.

Test Bed ApplicationMake drug development more efective:• Faster (Window trials—quantitative

endpoint);Cheaper (Adaptive Bayesian Design, two to three weeks of drug exposure);Better (Phantom calibration, standardize method, open source reference tools, defined molecular targets, tailored delivery systems)

• Tighter (variance), lighter (dose), standardized (protocol/profile)

Make care more personalized to patient:• Clinically proven detection and

longitudinal quantification• Quantitiative CT measures incorporated

into adaptive therapy / monitoring


Use a better example, but ~ like this


lesion Reader 1 contour(includes sliver)

Reader 2 contour(excludes sliver)

Even with Exquisite images, still uncertainty about what is and isn’t part of a lesion.This leads to uncertainty in measurements, even with experts.

CT Committee:Summary of Activity #1 of 2Activity Goal Status

Profiles (two, one for late and one for early)

Standardized and optimized VIA

Drafts in place, first proffered to UPICT, refining for next versions

Experimental groundwork:

1A Phantom scans and analysis

Bottoms-up characterization where ground truth is known

Analysis done, reporting at RSNA, releasing data sets publicly in categories related to Profile levels

VOLCANO and BIOCHANGE “Challenge” based approach to characterizing performance, using change measures rather than accuracy measures

First phases done, second phases underway

CT Committee:Summary of Activity #2 of 2Activity Goal Status

1B clinical data study for minimum detectable change

Coffee break studies for formal consideration of limits of detectability

Acquisitions done, reading underway

1C multi-site and multi-vendor phantom study

Extend 1A type results to “multi” context to extend bottoms-up characterization

Two protocols developed, one that is prescriptive and the other “performance” based, sites enrolled and data collection starting

Briefing Document VIA qualified for use in cancer therapy response assessment

Request Letter sent to FDA, document draft in circulation, need action plan to finish

Profile: CT Lung Nodule Volume Measurement for Primary/Regional Nodes and Metastatic Sites Section 6: Universal Parameters (independent of vendor, platform, and version)6.1 Devices In clinical settings, the v-CT committee expects that the protocol will be implemented on scanners that conform to the

expectations of the Medical Device Directive Quality System and the Essential Requirements of the Medical Device Directive. These instruments should have been designed and tested for safety in accordance with IEC 601-1, as well as for ElectroMagnetic Compatibility (EMC) in accordance with the European Union’s EMC Directive, 89/336/EEC. Labeling for these requirements, as well as ISO 9001 and Class II Laser Product,should appear at appropriate locations on the product and in its literature. The scanners should be CSA compliant.

6.2 # of channels Ideal: 64 or greater Target: 16 or greater Acceptable: 1 or greater 6.3 Detail: Protocol retrieval The acquisition system shall support saving and easily calling up saved acquisition protocols. • The running title of this image acquisition and processing protocol will be "QIBA vCT chest". 6.4 Detail: Anatomical Coverage Scout/topogram/planning view should be acquired to insure the field of view will cover the entire lung, from above the thoracic

inlet to a level just below the diaphragm. The acquisition system shall produce images with the following characteristics: 6.5 Detail: Slice Width Ideal: <= 1 mm Target: 1-2.5 mm Acceptable: <= 5 mm • Direct component of voxel size; determines resolution along patient (z) axis 6.6 Detail: Slice interval Ideal: contiguous or 20% overlap Target: contiguous or 20% overlap Acceptable: contiguous

















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Volumetric CT Example

Technical Description of Volumetric Image Analysis using CT (defining Activities and Biomarker Read-outs)

AcquireSubtract volumes

Patient Prep

Reconstruct and Post-

processDirectly process

images to analyze change

Obtain images per timepoint (2)

Imaging Agent (if any)

images

Assess change per target lesion

-OR- Interpret

Assess change in tumor burden (per patient)

Volumechange per

targetlesion (Δvt)

Change intumor burden

by volume(ΔT.B.)

Lesionvolume at

timepoint (vt)

Calculate volume

Calculate volume

volume changes

volumes

...

August 2010 Buckler Biomedical LLC 34October 13, 2010 34Informatics Services for Quantitative Imaging
















June 2010 Buckler Biomedical LLC 35

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FDG-PET Example

Technical Description of FDG-PET(defining Activities and Biomarker Read-outs)

Acquire CT

Subtract volumes

Patient Prep




Obtain images per timepoint (2)

Contrast Agent (if any)

CTimages

Assess change per target lesion

-OR- Interpret

Change intumor burden

by volume(ΔT.B.)

Calculate volume

Calculate volume

(vt)

volume changes

volumes

...

Acquire PET

Subtract SULs

Patient Prep




FDG chemist

ry

PETimages

-OR-

Assess change in metabolic volume

Uptakechange per

target(ΔSULt)

Lesionuptake at

timepoint (SULt)

Calculate volume

Calculate corrected

SULt

SULs

Total Lesion

Glycolysis (TLGt)

Lesion GlycolysisChange (ΔTLGt)

Interpret

Change in Metabolic Volume (ΔM.V.)

TLGchanges


Public Data Resources

QIBADefine Profiles and

Conduct Groundwork

Characterization / Qualification Data

Quantitative Imaging Profiles

New Drug Application

s

Clearances / Approvals

MOTIVATION AND PURPOSE

1.meta analysis of data about how treatment induced changes in marker read-outs correlate with health outcomes. The data would variously include results from the published literature, retrospectively re-analyzed data from previous clinical trials, and prospectively analyzed data from trials based on our QIBA protocols and profiles.

2.Such analysis would directly feed efforts to complete the “full data package” for qualification as well as provide contributory evidence for de novo 510(k)s and PMAs. With such proven biomarker tests, it could drive utilization for practicing radiologists in their use of these quantitative techniques on a more proven base than currently exists.

“TRACTABLE PLAN” a.k.a. “3B”

1.Work with pharma representatives on best way to frame the request

2.Work with Open Image Archives committee on development of use cases and desired/required attributes of archive

3.Work with NIST as “trusted broker” to handle scrubbing of institution source, sorting into bulls-eye levels, sequestering of test set, and submission of training set to public archive

4.Design the meta-analysis using results of 1A/B/C and 3A to inform statistical power analysis and for initial thresholds

5.Conduct a pilot of the meta-analysis to establish capability of the class of tests that represent the marker using the training set

6.Conduct the pivotal meta-analysis on the test set

7.Leave behind the training set for direct access

8.Define services whereby the test set is indirectly accessible via the trusted broker

August 2010 Buckler Biomedical LLC 37October 13, 2010 37Informatics Services for Quantitative Imaging

Agenda: Working Discussions1:45 PM Use Case Analysis Andrew Buckler,

MS

2:30 PM Systems architecture and prototyping approach Patrick Reynolds, MS

(including rationalizing ITL starting points, Julien Jomier, MSNBIA/AVT, and MIDAS/Batchmake)

3:30 PM Break

3:45 PM Intergroup Relationships: Andrew Buckler, MS

(NIST, QIBA, FDA, NCI, industry, academia)

4:00 PM NIST reporting requirements and preferences Mary Brady, MS

4:15 PM Work plans, milestones, and next steps All

5:00 PM Adjourn


Use Case Analysis and Requirements Flowdown


What information do these specifications contain?

Conceptual Model

Document (Specification):

What are you going to do? What important role does it fill

Platform

Independent Model


What does the interface and info model look like (in general)?

Platform

Specific Model


What exactly does the model look like, what does the code look like, what technology will you use (ie, what is the Platform, caGrid, Web services, Mobile platform, etc.). What Datatypes will you use?

System Use Cases

Enterprise Use Case

Basic Story Board

Enterprise Use Case: Describe it as the User thinks of it, not the Technologist

Link to Basic Storyboard

Assumptions and Dependencies

Definitions

For each Basic Workflow:Pre-conditions

E.g., what information needs to exist

Steps, e.g., •Identify available information.•Extract and integrate the relevant information from previous work/s or datasets.•Identify associated work/s or datasets.•Develop a working knowledge of the existing works.

Alternate WorkflowsPost-conditions, e.g.,

E.g., researcher's knowledge expanded by discovered information.

Alternate Post Conditions, e.g., E.g., no related information identified.


Researcher Biorepository Coordinator

Use Case Model(BAM)

Activity Diagram

class C3PR Analysis Model

BRIDG Release 1.0 -- Static Classes::Organization

+ organizationIdentifier: II+ name: string+ description: string+ postalAddress: string+ telecomAddressType: string+ telecomAddressUsage: string+ telecomAddressValue: string+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::HealthCareSite

+ healthcareSiteIdentifier: II+ postalAddress: string

Documentation

BRIDG Release 1.0 -- Static Classes::StudyProtocol

+ disease: CD+ phase: CD+ intent: CD+ populationDescription: string+ subjectType: CD+ blindedIndicator: boolean+ blindingSchema: CD+ multiInstitutionIndicator: boolean+ randomizedIndicator: boolean+ confidentiality: CD+ monitor: CD::Documentation+ identifier: II+ title: string+ detailedDescription: string+ summaryDescription: string+ synopsis: string+ documentationType: CD+ subtype: SET CD+ revision: string+ language: CD+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::StudySite

+ role: CD+ accrualStatus: CD+ targetAccrualNumber: int+ irbApprovalDate: dateTime+ startDate: dateTime+ endDate: dateTime+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::StudySubject

+ studySubjectIdentifier: II+ subgroup: CD+ informedConsentSignatureText: string+ studySubjectState: string+ status: string+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::Person

+ name: string+ initials: string+ postalAddress: string+ telecomAddressType: string+ telecomAddressUsage: string+ telecomAddressValue: string+ birthDateTime: dateTime+ deathDateTime: dateTime+ educationLevel: CD+ ethnicGroup: SET CD+ gender: CD+ householdIncome: CD+ maritalStatus: CD+ race: SET CD

BRIDG Release 1.0 -- Static Classes::Participant

+ participantIdentifier: II+ confidentialityIndicator: boolean+ paymentMethod: CD

BRIDG Release 1.0 -- Static Classes::StudyInvestigator

+ role: CD+ startDate: dateTime+ endDate: dateTime+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime+ signatureText: string

BRIDG Release 1.0 -- Static Classes::FundingSource

+ fundingIdentifier: II

BRIDG Release 1.0 -- Static Classes::StudySponsor

+ sponsorType: CD

BRIDG Release 1.0 -- Static Classes::PlannedStudy

+ targetAccrualNumber: int+ plannedSubjectParticipationDuration: string+ plannedSubjectInterventionDuration: string::StudyProtocol+ disease: CD+ phase: CD+ intent: CD+ populationDescription: string+ subjectType: CD+ blindedIndicator: boolean+ blindingSchema: CD+ multiInstitutionIndicator: boolean+ randomizedIndicator: boolean+ confidentiality: CD+ monitor: CD::Documentation+ identifier: II+ title: string+ detailedDescription: string+ summaryDescription: string+ synopsis: string+ documentationType: CD+ subtype: SET CD+ revision: string+ language: CD+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::Epoch

+ epochName: CD

TreatmentEpochNonTreatmentEpoch

- accrualCieling: int- accrualIndicator: boolean- enrollmentIndicator: boolean- reservationIndicator: boolean

EligibilityCriterion

- notApplicableIndicator: boolean- questionText: CD- displayOrder: int

InclusionEligibilityCriterion

ExclusionEligibilityCriterion

StratificationFactor

- questionText: CD- displayOrder: int

StratificationFactorValidAnswer

- validAnswer: CD

CoordinatingCenter

StudyCoordinatingCenter

ScheduledEpoch

ScheduledTreatmentEpoch

BRIDG Release 1.0 -- Static Classes::Arm

+ armName: CD::PlannedCalendarTS+ plannedCalendarDateTime: dateTime::CalendarTS+ descriptiveName: string+ timeOffset: int+ timeOffsetUnit: string+ rangeOfRepetitions: string+ timeOffsetReferencePoint: string+ additionalDurationDescription: string

Calendar

BRIDG Release 1.0 -- Static Classes::PlannedCalendar

+ effectiveDateTime: dateTime+ referenceTimePoint: string

CalendarTS

BRIDG Release 1.0 -- Static Classes::PlannedCalendarTS

+ plannedCalendarDateTime: dateTime

CalendarCell

BRIDG Release 1.0 -- Static Classes::

PlannedCalendarCell

::CalendarCell+ note: string

ScheduledNonTreatmentEpoch

Calendar


ScheduledCalendar

CalendarTS

BRIDG Release 1.0 -- Static Classes::ScheduledCalendarTS

+ scheduledCalendarDateTime: dateTime::CalendarTS+ descriptiveName: string+ timeOffset: int+ timeOffsetUnit: string+ rangeOfRepetitions: string+ timeOffsetReferencePoint: string+ additionalDurationDescription: string

CalendarCell


ScheduledCalendarCell


ScheduledArmSegment

- armSegmentName: string

EligibilityCriterionAnswer

- answer: string

StratificationFactorAnswer

- answer: string

StudyDisease

- primaryDiseaseIndicator: boolean- name: CD

StudyDiseaseSite

- primaryDiseaseSiteIndicator: boolean- name: CD

DiseaseHistory

- otherDiseaseName: CD- otherDiseaseSite: CD

Identifier

- value: string- type: string

OrganizationAssignedIdentifier SystemAssignedIdentifier

StudySubject

- informedConsentSignedDate: dateTime

BRIDG Release 1.0 -- Static Classes::ArmSegment

+ armSegmentName: CD

1

plays / is playedby 0..*

identifiedby/identifies


1

1..*

0..1


0..*

assigns/assignedby

1

has/isfor

1

0..*

treated by/treats

1

1

0..*

1

0..*

1

plays / is playedby

0..*

1

plays/ is playedby

0..*

1

defines a / for a1

1..*

is sponsored by /sponsors

1

1

operates as /represented by

0..*

1..*1

0..*

participates as /is fulfi l led by

1

0..1

is coordinated by / coordinates

1

0..*

functions as / isrepresented by

1

1..*

is conducted by /participates in

1

1

is used to createa / is a subject-specificdescription of

0..*

1..*

1

1

identifies eventsfor / has

1

1..*1

1..*

is the timing for /occurs accordingto

1

0..*

executes/ isexecuted at

1

1

operates as / represented by

0..*

1

plays/ is playedby

0..*

1..* 1

1..*is the timing for /occurs accordingto

1

1 1..*

0..*is the treatmentlocation for / isassigned to

1

class C3PR Analysis Model

BRIDG Release 1.0 -- Static Classes::Organization

+ organizationIdentifier: II+ name: string+ description: string+ postalAddress: string+ telecomAddressType: string+ telecomAddressUsage: string+ telecomAddressValue: string+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::HealthCareSite

+ healthcareSiteIdentifier: II+ postalAddress: string

Documentation

BRIDG Release 1.0 -- Static Classes::StudyProtocol

+ disease: CD+ phase: CD+ intent: CD+ populationDescription: string+ subjectType: CD+ blindedIndicator: boolean+ blindingSchema: CD+ multiInstitutionIndicator: boolean+ randomizedIndicator: boolean+ confidentiality: CD+ monitor: CD::Documentation+ identifier: II+ title: string+ detailedDescription: string+ summaryDescription: string+ synopsis: string+ documentationType: CD+ subtype: SET CD+ revision: string+ language: CD+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::StudySite

+ role: CD+ accrualStatus: CD+ targetAccrualNumber: int+ irbApprovalDate: dateTime+ startDate: dateTime+ endDate: dateTime+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::StudySubject

+ studySubjectIdentifier: II+ subgroup: CD+ informedConsentSignatureText: string+ studySubjectState: string+ status: string+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::Person

+ name: string+ initials: string+ postalAddress: string+ telecomAddressType: string+ telecomAddressUsage: string+ telecomAddressValue: string+ birthDateTime: dateTime+ deathDateTime: dateTime+ educationLevel: CD+ ethnicGroup: SET CD+ gender: CD+ householdIncome: CD+ maritalStatus: CD+ race: SET CD

BRIDG Release 1.0 -- Static Classes::Participant

+ participantIdentifier: II+ confidentialityIndicator: boolean+ paymentMethod: CD

BRIDG Release 1.0 -- Static Classes::StudyInvestigator

+ role: CD+ startDate: dateTime+ endDate: dateTime+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime+ signatureText: string

BRIDG Release 1.0 -- Static Classes::FundingSource

+ fundingIdentifier: II

BRIDG Release 1.0 -- Static Classes::StudySponsor

+ sponsorType: CD

BRIDG Release 1.0 -- Static Classes::PlannedStudy

+ targetAccrualNumber: int+ plannedSubjectParticipationDuration: string+ plannedSubjectInterventionDuration: string::StudyProtocol+ disease: CD+ phase: CD+ intent: CD+ populationDescription: string+ subjectType: CD+ blindedIndicator: boolean+ blindingSchema: CD+ multiInstitutionIndicator: boolean+ randomizedIndicator: boolean+ confidentiality: CD+ monitor: CD::Documentation+ identifier: II+ title: string+ detailedDescription: string+ summaryDescription: string+ synopsis: string+ documentationType: CD+ subtype: SET CD+ revision: string+ language: CD+ status: CD+ statusStartDate: dateTime+ statusEndDate: dateTime

BRIDG Release 1.0 -- Static Classes::Epoch

+ epochName: CD

TreatmentEpochNonTreatmentEpoch

- accrualCieling: int- accrualIndicator: boolean- enrollmentIndicator: boolean- reservationIndicator: boolean

EligibilityCriterion

- notApplicableIndicator: boolean- questionText: CD- displayOrder: int

InclusionEligibilityCriterion

ExclusionEligibilityCriterion

StratificationFactor

- questionText: CD- displayOrder: int

StratificationFactorValidAnswer

- validAnswer: CD

CoordinatingCenter

StudyCoordinatingCenter

ScheduledEpoch

ScheduledTreatmentEpoch

BRIDG Release 1.0 -- Static Classes::Arm

+ armName: CD::PlannedCalendarTS+ plannedCalendarDateTime: dateTime::CalendarTS+ descriptiveName: string+ timeOffset: int+ timeOffsetUnit: string+ rangeOfRepetitions: string+ timeOffsetReferencePoint: string+ additionalDurationDescription: string

Calendar

BRIDG Release 1.0 -- Static Classes::PlannedCalendar

+ effectiveDateTime: dateTime+ referenceTimePoint: string

CalendarTS

BRIDG Release 1.0 -- Static Classes::PlannedCalendarTS

+ plannedCalendarDateTime: dateTime

CalendarCell


PlannedCalendarCell


ScheduledNonTreatmentEpoch

Calendar


ScheduledCalendar

CalendarTS

BRIDG Release 1.0 -- Static Classes::ScheduledCalendarTS

+ scheduledCalendarDateTime: dateTime::CalendarTS+ descriptiveName: string+ timeOffset: int+ timeOffsetUnit: string+ rangeOfRepetitions: string+ timeOffsetReferencePoint: string+ additionalDurationDescription: string

CalendarCell


ScheduledCalendarCell


ScheduledArmSegment

- armSegmentName: string

EligibilityCriterionAnswer

- answer: string

StratificationFactorAnswer

- answer: string

StudyDisease

- primaryDiseaseIndicator: boolean- name: CD

StudyDiseaseSite

- primaryDiseaseSiteIndicator: boolean- name: CD

DiseaseHistory

- otherDiseaseName: CD- otherDiseaseSite: CD

Identifier

- value: string- type: string

OrganizationAssignedIdentifier SystemAssignedIdentifier

StudySubject

- informedConsentSignedDate: dateTime

BRIDG Release 1.0 -- Static Classes::ArmSegment

+ armSegmentName: CD

1

plays / is playedby 0..*



1

1..*

0..1


0..*

assigns/assignedby

1

has/isfor

1

0..*

treated by/treats

1

1

0..*

1

0..*

1

plays / is playedby

0..*

1

plays/ is playedby

0..*

1

defines a / for a1

1..*

is sponsored by /sponsors

1

1

operates as /represented by

0..*

1..*1

0..*

participates as /is fulfi l led by

1

0..1

is coordinated by / coordinates

1

0..*

functions as / isrepresented by

1

1..*

is conducted by /participates in

1

1

is used to createa / is a subject-specificdescription of

0..*

1..*

1

1

identifies eventsfor / has

1

1..*1

1..*

is the timing for /occurs accordingto

1

0..*

executes/ isexecuted at

1

1

operates as / represented by

0..*

1

plays/ is playedby

0..*

1..* 1

1..*is the timing for /occurs accordingto

1

1 1..*

0..*is the treatmentlocation for / isassigned to

1

class Lab Model

Study

+ id: Integer+ identifier: String+ name: String+ assigningAuthority: String

PerformingLaboratory

Person

+ id: Integer+ dateOfBirth: Date+ initials: String

CentralLaboratory

StudySite

+ id: Integer

Investigator

+ identifier: String+ name: String

Participant

SubjectAssignment

+ id: Integer+ type: String+ studySubjectIdentifier: String

Activ ity

+ id: Integer+ identifier: String+ actualStartDateTime: Date+ actualEndDateTime: Date+ plannedTimeElapsed: String+ plannedTimeElapsedDescription: String+ reason: String+ plannedIndicator: Boolean+ typeModifier: String

SpecimenCollection

+ id: Integer+ subjectAgeAtCollection: Integer+ subjectAgeAtCollectionUnits: String+ fastingStatus: String+ method: String

Specimen

+ id: Integer+ identifier: String+ accessionNumber: String+ condition: String+ commentsFromLaboratory: String+ commentsFromInvestigator: String

LabResult

+ id: Integer+ numericResult: Integer+ numericPrecision: Integer+ textResult: String+ referenceRangeLow: Integer+ referenceRangeHigh: Integer+ referenceRangeComments: String+ referenceTextList: String+ reportedResultStatus: String+ testPerformedDateTime: Date+ referenceFlag: String

ConceptDescriptorDataType

+ id: Integer+ code: String+ codeSystem: String+ codeSystemName: String+ codeSystemVersion: String+ displayName: String

Name: Lab ModelAuthor: ScenPro, Inc.Version: 1.0Created: 8/8/2006 2:03:19 PMUpdated: 5/3/2007 6:23:44 PM

LabTest

+ id: Integer+ status: String+ additionalTestDescription: String+ comments: String+ studyDefinedIndicator: Boolean+ studyScheduledIndicator: Boolean

Organization

+ id: Integer+ identifier: String+ name: String

BRIDG/RIM Entity

BRIDG/ RIM Role

BRIDG/ RIM Participation

BRIDG/ RIM Activity/Act

HealthCareSite

+subjectAssignmentCollection

0..*

+studySite

1

+investigatorCollection0..*

+studySiteCollection0..*

+participant 1

+subjectAssignment 0..1

+activityCollection

0..*

+subjectAssignment

1

+centralLab 0..1

+specimenCollection

0..*

+specimenCollection 1..1

+specimenCollection 1..*

+studySiteCollection

0..*

+study 1

+labTest 0..*

labTestId

+labTestId

1

+labTest 1 +labTestCollection 0..*

+race 1

race

+personCollection

0..*

+gender 1

gender

+personCollection

0..*

+materialType

1 materialType

+specimenCollection

0..*

+units

1

units

+labResultCollection 0..*

+labTest

1

+labResult

0..1

+labTestCollection 0..*

+specimen 1+healthCareSite 1

+studySiteCollection 0..*

+performingLaboratory 0..1

+labResultCollection 0..*

DAM-Based Class Diagrams

Work Flow

Data Flow

Actors

Storyboards in use case define the work flow and data flow that identify the pre- and post-conditions

Data elements exchanged in a data flow are fully specified in class diagrams

Activities in activity diagrams could inform the work flow and functions of the applicationsApplications

Static Elements

Basic Story Board: Describe it as abstracted actors, operations, and dataDefinitionsBrief Description

E.g., this use case includes activities for the identification and review of raw or processed data, methodologies, experimental systems, and published next questions.

ActorsExternal InterfacesData Elements Storyboard

E.g., the researcher will identify and review available information relevant to the topic to integrate and synthesize into domain knowledge.


System Use Cases: Map to Specific ImplementationsMap functions to architectural design elements and phase into development stages:

•Identify currently extant components that are effected

•Propose new components that are needed

•Establish specifications that are critical-to-quality in the implementation

•Propose project phasing scenarios for funding proposals

Informatics Services for Quantitative ImagingOctober 13, 2010 42

Brainstorm / Discuss ContentRelated activities to draw from / support:

1. Open Image Archives ad hoc committee2. TCGA Radiology and other correlative in-silico studies3. QIN4. RVL5. QIBA6. Legacy understanding of requirements from current version of tools7. JANUS/SDTM

Workflows:1. Suite:

1. Compliance tests / proficiency testing of supplier implementations 2. Create and manage reference data sets using clinical and synthetic data3. FDA qualification of imaging biomarkers as clinical endpoints in clinical trials4. Comparative evaluation vs. gold standards or otherwise accepted markers5. FDA approval or clearance of imaging tests with strong clinical claims for market6. Profiling

2. Experimental groundwork3. Algorithm Development


Intergroup Relationships

• (NIST, QIBA, FDA, NCI, industry, academia)


Documents

Measurement Science and Engineering FUNDING PROVIDED BY NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY Informatics Services for Statistically Valid and