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NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of Chicago COI: M.L.G. is a stockholder in R2/Hologic, co-founder and equity holder in QuanCtaCve Insights, shareholder in Qview, and receives royalCes from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. It is the University of Chicago Conflict of Interest Policy that invesCgators disclose publicly actual or potenCal significant financial interest that would reasonably appear to be directly and significantly affected by the research acCviCes.
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Page 1: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

NCI Quan)ta)ve Imaging Network (QIN)

Opportuni)es for QI Tools in Breast Oncology

For QIN: Nola Hylton, UCSF

Maryellen Giger, University of Chicago

COI:M.L.G.isastockholderinR2/Hologic,co-founderandequityholderinQuanCtaCveInsights,shareholderinQview,andreceivesroyalCesfromHologic,GEMedicalSystems,MEDIANTechnologies,RiverainMedical,Mitsubishi,andToshiba.ItistheUniversityofChicagoConflictofInterestPolicythatinvesCgatorsdisclosepubliclyactualorpotenCalsignificantfinancialinterestthatwouldreasonablyappeartobedirectlyandsignificantlyaffectedbytheresearchacCviCes.

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The Quan)ta)ve Imaging Network (QIN)

TheQINisanNCIProgramjointiniCaCvetobringquanCtaCveimagingmethodsintoclinicaluClityformeasuringresponsetotreatmentandsupporCngclinicaldecision-making25teamsintheQINfocusonimprovingquanCtaCveresultsfromclinicalimagesforaspecificcancerproblemCross-NetworkWorkingGroupsaddress:1)ImageAnalysisandPerformanceMetrics(MRIandPET/CTSubgroups);2)BioinformaCcs/ITandData-sharing;3)ClinicalTrialsDesignandDevelopment

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3

Networkintent:buildconsensus,sharedataandtools.

The Quan)ta)ve Imaging Network

•  25acCveteams(twofundedthroughtheCanadianGovernment)•  12associatemembersfromUSand7foreigncountries

•  Over46toolsunderdevelopmentandvalidaCon

Page 4: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

Quan)ta)ve Imaging

QuanCtaCveimagingistheextracConofquanCfiable(measurable)featuresfrommedicalimagesfortheassessmentofnormalortheseverity,degreeofchange,orstatusofadisease,injury,orchroniccondiConrelaCvetonormalItisthecombinaConofimaging,analyCcalmethods,andinformaCcs

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PaCentStatus

ImageAcquisiCon

PaCentImages

AlgorithmProcessing

QuanCtaCveResults

ClinicalDecision

QIN: From Informa)on to Knowledge

Page 6: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

The Complexi)es of Quan)ta)ve Tools in Clinical Trials

•  Clinicalchallenge:–  IdenCfytheclinicallymostmeaningfulimagingmarkerforthestudyobjecCve

•  Technicalchallenges:–  ImagestandardizaCon–  ImageacquisiCon– Datatransferand/oranalysis–  SiteversuscentralquanCtaCveanalysis–  ImageanalysistooldistribuConandvalidaCon

Page 7: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

Representa)ve tools developed by QIN teams Tool Modality Purpose

LymphnodesegmentaCon MRI LymphnodesegmentaCon

HologicAegisSER MRI VolumetricbreasttumorsegmentaConQuanCtaCveInsightsQuantX MRI VolumetrictumorsegmentaConandmachinelearning

diagnosCcs

Xcal PET MulCcenterPETSUVcross-calibraCon

AutoPERCIST PET PERCISTresponseanalysisforFDG-PET

LungSegmentaCon CT VolumetriclungnodulesegmentaCon

Radiomicsanalysis CT Lung,headandneckradiomicsanalysis

MassesCmaCon CT MusclemassofcancerpaCents

ePAD Imageanalysis ImageannotaConandquanCtaCveanalysis

Slicer Imageanalysis Imageanalysisandsurgicalplanning

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Why we (the QIN) are here

•  ToidenCfyoncologytrialswherequanCtaCveimagingbiomarkersandQINtoolscansupportoutcomesbyimprovingefficacy,efficiency,orstudypower

• QIN-NCTNPlanningmeeCngrecommendaCons(December2016):

•  QINtoolintegra.onintoclinicaltrialsshouldstartasearlyaspossibleintrialdevelopment

•  Increaseddialogueneededbetweenimagersandoncologists

•  Presenta.onsbyQINmembersat(1)theAlliancePlenarysession,(2)selecteddiseasesitecommiAees,and(3)ImagingcommiAee

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Breast Quan)ta)ve Imaging in Clinical Trials

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•  Func.onaltumorvolume(FTV)predictsrecurrence-freesurvival(RFS)

•  FTVisastrongerpredictorofRFSthanpathologiccompleteresponse(pCR)

•  FTVpredictsRFSasearlyasaKer1cycleofstandardanthracycline-basedchemotherapy

MRIatbaseline,1cycle,betweenACandT,andpre-surgery

Hylton et al., RADIOLOGY 2015

I-SPY 1: ACRIN 6657 & CALGB 150007– Contrast-enhanced MRI for assessing breast cancer response to neoadjuvant chemotherapy

Page 11: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

•  FTVpredic+veperformanceandop+malmeasurement+mepointdifferbybreastcancersubtype

Hylton et al., RADIOLOGY 2015

Early-treatment

Pre-surgery

HR+/HER2- HER2+ HR-/HER2- (TN)

I-SPY 1: ACRIN 6657 & CALGB 150007– Contrast-enhanced MRI for assessing breast cancer response to neoadjuvant chemotherapy

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•  Drugs“graduate”fromI-SPY2whentheyreachaBayesianpredicCveprobabilityofachieving80%successinasubsequentphaseIIIstudy

•  Drugsgraduatewithinsubtypesdefinedbyhormonereceptor(HR)status,HER2statusandMammaprintscore

•  DrugscanbedroppedforfuClity•  I-SPY2isanadap.vely-randomizedphaseIItrialtes.ng

novelagentsforbreastcancer•  IncorporatesMRItumorvolumeinthepa.ent

randomiza.onalgorithm

>2150pa.entsregistered;>1220randomized;>1070withsurgeryasofOct20176druggraduatestodate

I-SPY 2 breast cancer trial

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•  ACRIN6698:sub-studyofI-SPY2tesCngdiffusion-weightedMRI(10sites)•  406I-SPY2paCentsenrolled;272ontreatmentcombinedforanalysis•  DWIaddedtostandardDCE-MRI•  Apparentdiffusioncoefficient(ADC)measuredusingDWI•  Preliminaryresults(presentedatASCO2017):

Ø  ADCandchangeinADCatmid-therapyandpre-surgerypredictpCRØ  Variablepredic+onbysubtype,highestinHR+/HER2-

MulC-focalinvasiveductalcarcinoma.Pre-treatmentDCEMRI1(leo)andDWIb800(right)

DWImeasurestherandommo.onofwaterin.ssueProvidesinforma.onaboutcelldensityandmicrostructure

DCE DWI

ACRIN 6698 - Breast diffusion-weighted MRI (DWI) to predict response to neoadjuvant chemotherapy

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•  Endpoints:•  Primary:radiographicresponseletrozoleonMRI

•  ChangeinMRItumorvolume

•  Secondary:•  Mammographicextentofdisease

•  CandidacyforbreastconservaCon•  Frequencyofre-excisions•  PathCR•  Invasivecanceratexcision

CALGB 40903: Phase II Single-Arm Study of Neoadjuvant letrozole for ER(+) postmenopausal DCIS (PI: Shelley Hwang)

Page 15: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

ACRIN 6688: FLT PET to Measure Early Breast Cancer Response (PI: Lale Kostakoglu)

Best ΔSUVmax cut-off for predicting pCR = -51% (sensitivity 56%;specificity 79%).

Pre-Therapy

7 d Post-

(Kostakoglu, J Nucl Med, 2015) ACRIIN and U Wash QIN U01

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Next Steps: Benefit of using exis)ng and future Clinical Trial data Increase effec)veness & efficiency Incorporate automated, objec)ve computer-extracted biomarkers (radiomics) & develop decision tools using machine learning. Enable efforts to standardize, verify quality, and validate with exis)ng and future Clinical Trial data.

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Incorpora)ng automated computer-extracted characteris)cs (radiomics) into response assessment

(METV on ACRIN 6657 data: only pre-treatment & early treatment imaging exams)

EsCmatedKaplan-Meierrecurrence-freesurvivalesCmatesforMETVNIHQINGrantU01CA195564

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4D DCE MRI images

Computer-Extracted Image Phenotypes

Size

Shape

Morphology

Contrast Enhancement

Texture

Curve

Variance

……

Computerized Tumor Segmentation

Radiologist-indicated Tumor Center

CADpipeline=radiomicspipeline

InputtoClassifier(LDA,SVM)NIHQINGrantU01CA195564

Incorporating machine learning into assessing diagnosis, molecular classification, & response assessment

Computer-extraction of biomarkers (features) followed by training of predictive classifiers

Page 19: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

MulC-insCtuConal,MulC-disciplinaryCollaboraCon

cancerimagingarchive.netBreast Cancer cases

Clinical/Histopathology/GenomicdatadownloadedbyTCGAAssembler&Molecularsubtyping/riskofrecurrence

valuesbyPerouLab

TumorlocaCononMRIdeterminedbyconsensusofthreeoftheTCIAradiologists

MRIsof91cases(GE1.5T)collectedbyTCIA

MRIsof91casesdownloadedtoUChicagoforcomputaConalMRItumor

phenotyping(radiomics)

cancergenome.nih.gov

Page 20: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

FromtheTCIARadiomics--EnhancementTextureofTumorHeterogeneityappearsPredicCveofMolecularSubtype–ClinicalPrognos8cValue

4 55

10 5 10

Kendall test results for trends; p-value=0.0055

LiH,ZhuY,BurnsideES,….PerouCM,JiY,GigerML:QuanCtaCveMRIradiomicsinthepredicConofmolecularclassificaConsofbreastcancersubtypesintheTCGA/TCIADataset.npjBreastCancer(2016)2,16012;doi:10.1038/npjbcancer.2016.12;publishedonline11May2016.

Page 21: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

Good Prognosis Case (left)

Poor Prognosis Case (right)

Cancer Subtype Luminal A Basal-like OncotypeDX Range [0, 100]

14.4 (low risk of breast cancer

recurrence)

100 (high risk of breast cancer

recurrence) MammaPrint

Range [0.848, -0.748] 0.67

(good prognosis) -0.54

(poor prognosis) PAM50 ROR-S (Subtype)

Range [-7.42, 71.76] -2.2

(low risk of breast cancer recurrence)

56.3 (high risk of breast cancer

recurrence) PAM50 ROR-P

(Subtype+Proliferation) Range [-13.21, 72.38]

0.96 (low risk of breast cancer

recurrence)

53.2 (high risk of breast cancer

recurrence) MRI Tumor Size

(Effective Diameter) Range [7.8 54.0]

16.8 mm

21.7 mm

MRI Tumor Irregularity Range [0.40 0.84]

0.438

0.592

MRI Tumor Heterogeneity (Entropy)

Range [6.00 6.59]

6.27

6.51

!

Multi-gene assays of risk of recurrence

Radiomics for “virtual” biopsy

Clinical Therapeutic Response Assessment Value

LiH,ZhuY,BurnsideES,….PerouCM,JiY*,GigerML*:MRIradiomicssignaturesforpredicCngtheriskofbreastcancerrecurrenceasgivenbyresearchversionsofgeneassaysofMammaPrint,OncotypeDX,andPAM50.RadiologyDOI:hwp://dx.doi.org/10.1148/radiol.2016152110,2016.

Page 22: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

IMAGINGGENOMICS–USINGVIRTUALBIOPSIESPATHWAYTRANSCRIPTIONALACTIVITIESASSOCIATEDWITHMRIQUANTITATIVEFEATURES

ZhuY,LiH,…GigerML*,JiY*:DecipheringgenomicunderpinningsofquanCtaCveMRI-basedradiomicphenotypesofinvasivebreastcarcinoma.Nature–ScienCficReports5:17787(2015)

ShapeSize

Heterogeneity

Page 23: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

Opportuni)es for NCTN-QIN Collabora)ons

1.  QINcanprovideexperCsetoguideimagingneedsforNCTNtrials•  QINinvesCgatorsareeagertoparCcipateinNCTNtrials

2.  QINinvesCgatorsseekopportuniCestoaddexploratorybiomarkerstoNCTNtrials,ooenwithoutaddedcost•  QINteamarefundedtodevelopQItools,andrelishthechancetotesttoolsprospecCvelyintrials

•  AddimagingtranslaConalsciencetoNCTNtrials

3.  EnhancedpartnershipforoncologyandimaginginvesCgatorsinNCTNtrials•  Commongoalsofimprovedthequalityandefficiencyofcancerclinicaltrials

Page 24: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

QINprogramofficeRobertNordstrom [email protected],CancerImagingProgram

LoriHenderson [email protected],ClinicalTrialsBranch,CancerImagingProgram

QIN Contact Information

Page 25: NCI Quan)ta)ve Imaging Network (QIN) · NCI Quan)ta)ve Imaging Network (QIN) Opportuni)es for QI Tools in Breast Oncology For QIN: Nola Hylton, UCSF Maryellen Giger, University of

QIN Presenta)ons at Alliance Annual Mee)ng

CommiMee QINRepresenta8veBreast NolaHyltonandMaryellenGigerExperimentalTherapeuCcs PaulKinahanandAmitaDaveGI LarrySchwartzandHugoAertsGU MichaelJacobsandAndryFedorovLymphoma RichWahlandDaveMankoffNeuro-Oncology MichaelKnoppandJayshareeKalpathyRadiaCon-Oncology Hui-KuoShuandYueCaoRespiratory JohnBuayandMichaelMcNiw-GrayIROC Xiao,Rosen,Knopp,andFitzgerald


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