Images as Biomarkers potential future advances in the field as viewed by ISPY-2. Nola Hylton, PhD University of California, San Francisco

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1 Images as Biomarkers potential future advances in the field as viewed by ISPY-2 Nola Hylton, PhD University of California, San Francisco

2 POTENTIAL/PROMISE: Images as Biomarkers Imaging Biomarker: a quantitative measurement of a tissue property made from in-vivo image data that reflects a biologic or physiologic process Provides a non-invasive assessment of the whole organ/tumor Can be repeated over time to observe change Can provide a readout of disease status or effect of treatment Example: breast MRI showing a small, invasive ductal carcinoma in dense breast tissue Standard clinical contrast-enhanced MRI Diffusionweighted MRI Derived ADC map x10-3 mm²/s Partridge SC, et al. JMRI 2010

3 CHALLENGES/BARRIERS: Images as Biomarkers Vast number of candidate imaging biomarkers (many imaging modalities PET, MRI, US, optical; many quantification methods) Technical requirements for clinical imaging and biomarker imaging can be conflicting (anatomic clarity versus quantitative accuracy; biomarker images do not always make the prettiest picture) Imaging biomarkers need to be optimized for their application (ie., to improve diagnostic specificity; to predict risk or survival) Comparative optimization of imaging biomarkers requires prospective, standardized collection of imaging datasets and associated outcomes

4 Different drivers for optimizing clinical imaging and biomarker imaging CLINICAL IMAGING: Anatomic clarity and speed Image enhancements and filters used to improve contrast, image quality and lesion conspicuity Scan time reduction strategies utilized to improve efficiency Adjustments made for patient-based optimization of parameters BIOMARKER IMAGING: Accuracy and repeatability Image acquisition designed to maximize quantitative accuracy Controllable errors are minimized (often at expense of resolution, scan time) Inter- and intra-patient variability are minimized (fixed protocols; no patientspecific adjustments; controlled introduction of software/hardware upgrades)

5 Multiple stages of image quantification MR Image Acquisition Image Processing Biomarker Quantification Tumor Volume k trans Map Peak Value Red fraction (high permeability)... Test against outcomes DCE-MRI

6 Optimizing the imaging biomarker for what purpose? Detection best determination of presence/absence of disease Diagnosis highest specificity Staging best agreement with histopathologic extent; or disease aggressiveness Response Assessment most sensitive to change over time Risk Marker for example, best prediction of recurrence

7 Optimizing the imaging biomarker Example from breast MRI Effect of initial enhancement threshold on functional tumor volume (FTV) measurement by DCE- MRI: 70% PE threshold volume = 57 cc Best agreement with histopathology 100% PE threshold volume = 43 cc Strongest association with time-to-recurrence

8 Cox proportional hazards model for predicting RFS: Effect of FTV thresholds p value Hazard Ratio p Value PE > PE > PE > PE > PE > PE > PE > PE > PE > PE > p value PE > PE > PE > SER > 1.8 SER > 1.2 SER > 0.6 SER > 0.0 SER > PE Threshold SER > Hazard Ratio SER > 0.6 SER > PE Threshold SER > 1.8 SER > 1.2 SER > 0.0 SER > 1.0

9 I-SPY has been a process of continuous standardization and outcomes-based optimization I- SPY 1 Standard AC/T ACRIN 6657 Original ACRIN 6657 Extension Greater standardization, protocol adherence, quality control; tracking More advanced MRI techniques introduced over time (DCE -> MRS -> DWI) I-SPY 2 T + novel agent ACRIN 6698 (May 2012) 2010 Image data loss rate decreased from 12% in ISPY- 1 to 1% in ISPY-2

10 Real-time image analysis and reporting using the Hologic Aegis workstation *IDE-approved software for functional tumor volume (FTV) measurement FTV used in I-SPY 2 adaptive randomization design WORKFLOW: Image data transferred to ACRIN via TRIAD, then to UCSF FTV = 31.5 cc QC performed by ACRIN and UCSF DCE-MR images analyzed at site using Hologic/Sentinelle Aegis workstation* Report generated, approved and signed by site radiologist, centrally approved by Imaging PI Tumor volume measurement transmitted via I-SPY TRANSCEND to Statistical Center at MDACC within 72 hours of exam * developed under NCI Academic-Industrial Partnership (AIP) Grant Real-time in vivo MRI biomarkers for breast cancer pre-operative treatment trials (R01 CA132870)

11 I-SPY2 image quality and protocol compliance (2010-present) Reasons for non-compliance 8, 4% 6, 3% I-SPY2 image protocol compliance 23, 10% 4, 2% Timing deviation Auto timing 66, 29% 22, 10% 23, 1% Boxing not consistent Threshold not 70% 18, 8% 147, 8% 24, 11% 3, 1% Non-compliant, analyzable DCEs Non-compliant, nonanalyzeable DCEs Timing Deviation DCE acquisition 50, 22% PE threshold not consistent Late exam submission Factors preventing analysis Compliant, analyzeable , 91% 2 MR parameters outside protocol 5 Other 5 1% of exams non-analyzable for FTV Image Artifact Present 5 3 Scan Quality Insufficient Machine Failure UCSF Core Lab: Margarita Watkins, Sachiko Suzuki, Krysta Banfield, Roxana Dhada

12 Where we are.. Quantitative Imaging (QI) metrics have enormous potential to be employed as biomarkers Several QI biomarkers are gaining acceptance (PET-SUV, DCE- MRI) Many efforts are underway to unify and disseminate standards for quantitative imaging (QIBA, QIN, ACRIN) Familiarity and acceptance of QI standards are increasing in the clinical environment and among equipment manufacturers

13 What s Needed Collaboration among imaging scientists and clinical trials investigators to promote and reinforce QI standards in the clinical environment This has been done successfully in ISPY-2 The value of the imaging biomarker relies on the quality of the image acquisition Partnerships with equipment manufacturers to address the mixed needs of clinical diagnostic and biomarker imaging Embedded processes for testing, optimizing and comparing imaging biomarkers in prospective, controlled clinical trials Imaging scientists need meaningful settings for developing and testing imaging biomarkers

14 6657/I-SPY 1 Trial Team UCSF Imaging Core Lab Members David Newitt, Sheye Aliu, Margarita Watkins, Sachiko Suzuki, Krysta Banfield, Roxana Dhada, Evelyn Proctor, Jessica Gibbs, Ella Jones, Lisa Wilmes ACRIN 6657 Trial Team N. Hylton, B. Joe, M. Watkins, S. Suzuki, D. Newitt, E. Proctor, UCSF; J. Blume, H. Marques, B. Herman, C. Gatsonis, B. Dunning, ACRIN DMC; M. Rosen, M. Schnall, U Penn; E. Pisano, UNC, E. Morris, MSKCC; W. Bernreuter, UAB; S. Polin, Georgetown; C. Lehman, S. Partridge, U Wash; P. Weatherall, UTSW; G. Newstead, U Chicago; P. Bolan, U Minnesota; B. LeStage, N. Sauers, ACRIN Advocates I-SPY Trial Network L. Esserman, J. Gray, L Vantveer, UCSF; A. DeMichelle, U Penn; D. Berry, F. Symmans, MDACC, L Carey, C. Perou, UNC, L. Montgomery, C. Hudis, MSKCC; H. Krontiras, UAB; M. Liu, Georgetown; J. Gralow, U Wash; D. Tripathy, UTSW; F Olopade, U Chicago; D. Yee, U Minnesota; S. Madhavan, K. Buetow, E. Petricoin, J. Perlmutter, NCICB