Update on Lung Cancer Image Update on Lung Cancer Image ProcessingProcessing

Rick AvilaRick AvilaKarthik KrishnanKarthik Krishnan

Luis IbanezLuis Ibanez

Kitware, Inc.Kitware, Inc.

rick.avila@kitware.comrick.avila@kitware.com

April 19, 2006

KitwareKitware

Therapy AssessmentTherapy Assessment

StartTherapy

TimeTimeTimeTime

AssessmentAssessment• Tumor responseTumor response• ID new lesionsID new lesions

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Tu

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Tu

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Tu

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AssessResponse

4 cm lesion4 cm lesion

CharacteristicsCharacteristics• Late stageLate stage• Thick CTThick CT

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KitwareKitware

RECISTRECIST

TimeTimeTimeTime

Baseline& Treat

AssessResponse

D = -30%D = -30%

D = +20%D = +20%

Sta

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Unaided Interpretation

4cm lesion4cm lesion

8mm 8mm D, 13 pixelsD, 13 pixels

73% 73% VolumeVolume

Erasmus et. al., JCO 2003Erasmus et. al., JCO 2003

Intra-observer errorIntra-observer error

PD: PD: 9.5%9.5% of tumors of tumors

PR: PR: 3%3% of tumors of tumors

Inter-observer errorInter-observer error

PD: PD: 30%30% of tumors of tumors

PR: PR: 14%14% of tumors of tumors

Erasmus et. al., JCO 2003Erasmus et. al., JCO 2003

Intra-observer errorIntra-observer error

PD: PD: 9.5%9.5% of tumors of tumors

PR: PR: 3%3% of tumors of tumors

Inter-observer errorInter-observer error

PD: PD: 30%30% of tumors of tumors

PR: PR: 14%14% of tumors of tumors

KitwareKitware

We can do betterWe can do better

TimeTimeTimeTimeS

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Aided 3D Aided 3D InterpretatioInterpretatio

nn

Improve:Improve:• BiasBias• VarianceVarianceFor Lower:For Lower:• Interval (Interval (t)t)• Study NStudy N

Early Detection & Nodule Sizing ??

4cm lesion4cm lesion

KitwareKitware

First Step: Open Development DatabasesFirst Step: Open Development Databases

All Cases Shown In This Presentation Came From These DatabasesAll Cases Shown In This Presentation Came From These Databases

KitwareKitware

Measurement ChallengesMeasurement Challenges

Patient/Lesion PresentationPatient/Lesion Presentation– SizeSize– ComplexityComplexity– Changes over time (necrosis)Changes over time (necrosis)

ScannersScanners– HardwareHardware– SoftwareSoftware

ProtocolsProtocols– ScanRxScanRx– ContrastContrast– Patient positionPatient position

ObserverObserver– Seed points/ROISeed points/ROI– Data InterpretationData Interpretation 5mm5mm 2.5mm2.5mm

KitwareKitware

Complex BoundariesComplex Boundaries

KitwareKitware

Complex BoundariesComplex Boundaries

KitwareKitware

Volumetric Algorithm ChallengesVolumetric Algorithm Challenges

Boundary Identification ChallengesBoundary Identification Challenges• Vascular network (Ev)Vascular network (Ev)

• Bronchial network (Eb)Bronchial network (Eb)

• Pleura (Ep)Pleura (Ep)

• Sub-voxel edge (Es)Sub-voxel edge (Es)

Errors at 2 time pointsErrors at 2 time pointsEvEv

Error strongly depends Error strongly depends

on on lesion sizelesion size and and

slice thicknessslice thickness

EpEp

Pleur

a

EsEs

No

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N

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II

KitwareKitware

Solid Algorithm: Operating EnvelopeSolid Algorithm: Operating Envelope

Lesion SizeLesion Size

Slic

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ness

Slic

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hick

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10mm10mm 20mm20mm15mm15mm5mm5mm00

1.25 mm1.25 mm

2.5 mm2.5 mm

3.75 mm3.75 mm

5.0 mm5.0 mm

ComplexComplex

BoundariesBoundaries

PartialPartial

VolumeVolume

Noise…Noise…

Curvature…Curvature…

ClinicalClinical

TrialsTrials

SolutionSolution• <= 1.25mm thickness<= 1.25mm thickness

• Algorithm support for Algorithm support for complex intersectionscomplex intersections

• Validate against wide Validate against wide patient and protocol patient and protocol populationpopulation

KitwareKitware

Motivating ExampleMotivating Example

46d46d 69d69d

59mm59mm 48mm48mm 44mm44mm

1D 25%

RECIST would classify response to therapy as Stable DiseaseRECIST would classify response to therapy as Stable Disease

KitwareKitware

3D Analysis3D Analysis

KitwareKitware

3D Analysis3D Analysis

KitwareKitware

Validation ApproachValidation Approach

TimeTimeTimeTime

Baseline& Treat

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Case CollectionCase Collection

• Collect cases w/many Collect cases w/many short interval scansshort interval scans

• Assessment on last Assessment on last scan(s) is clearscan(s) is clear

AnnotationAnnotation

• One or more expert(s) One or more expert(s) classify each case classify each case based on all databased on all data

T1T1 T2T2 T3T3 T4T4 T5T5

MetricMetric

• Measure sens/spec Measure sens/spec between assess pairsbetween assess pairs

• Compare metrics at Compare metrics at last time pointlast time point

• At what time can a At what time can a sens/spec be met?sens/spec be met?

KitwareKitware

Open Database Collection PrioritiesOpen Database Collection Priorities

Add Annotation to Open DatabasesAdd Annotation to Open Databases– Need to assess RECIST as the baseline performanceNeed to assess RECIST as the baseline performance– Need an expert assessment of response for caseNeed an expert assessment of response for case

Add More Cases to Open DatabasesAdd More Cases to Open Databases – Wide range of patient/lesion presentationsWide range of patient/lesion presentations– Wide range of therapy interactionsWide range of therapy interactions

Emphasize Thin SliceEmphasize Thin Slice– Algorithms perform better (e.g. I’’) Algorithms perform better (e.g. I’’)

Collect Data at Smaller Time IntervalsCollect Data at Smaller Time Intervals– Algorithms perform better (e.g. registration)Algorithms perform better (e.g. registration)

Thank YouThank You

KitwareKitware

Edge DetectionEdge Detection

Algorithms that utilize acquisition characteristicsAlgorithms that utilize acquisition characteristics

(e.g. PSF, SNR) can adapt to changes in acquisition(e.g. PSF, SNR) can adapt to changes in acquisition

Step Step FunctionFunction

PSF + NoisePSF + Noise

Object Scanner Image

SmoothLocalize

Recover Edge Using Recover Edge Using

Acquisition CharacteristicsAcquisition Characteristics

Elder et. al. TPAMI 1998

KitwareKitware

Cross-Platform CapabilityCross-Platform Capability

• Goal:Goal:– Software achieves accuracy despite variation in:Software achieves accuracy despite variation in:

• Scanning equipmentScanning equipment• Acquisition protocolsAcquisition protocols

• Solution:Solution:– Establish minimum acquisition standards/protocolsEstablish minimum acquisition standards/protocols– Keep acquisition technique constant per patientKeep acquisition technique constant per patient– Measure scanner characteristics Measure scanner characteristics utilizing a standard utilizing a standard

phantomphantom and publish and publish – Utilize model-based algorithmsUtilize model-based algorithms

KitwareKitware

Unexpected Results Unexpected Results

Many studies report Many studies report greatergreater variance and variance and error when comparing 1D/2D/3D analysiserror when comparing 1D/2D/3D analysis

– Issue 1: More is not always betterIssue 1: More is not always better• All measurements need high precisionAll measurements need high precision• Consider slice thickness Consider slice thickness

– Issue 2: New metrics need optimizationIssue 2: New metrics need optimization• Development data needed to establish best Development data needed to establish best

separation between response classesseparation between response classes