Images as Biomarkers – potential future advances in the field as viewed by ISPY-2

Nola Hylton, PhDUniversity of California, San Francisco

Images as Biomarkers• POTENTIAL/PROMISE:

− 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

Standard clinical contrast-enhanced

MRI

Diffusion-weighted MRI Derived ADC map

x10-3 mm²/s

- 3.5

- 2.0

- 0.0

- 1.0

- 3.0

Partridge SC, et al. JMRI 2010

Example:

breast MRI showing a small, invasive ductal carcinoma in dense breast tissue

Images as Biomarkers• CHALLENGES/BARRIERS:

− 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

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 patient-specific adjustments; controlled introduction of software/hardware upgrades)

Multiple stages of image quantification

Image Acquisition Image Processing Biomarker Quantification

MR

DCE-MRI

ktrans Map

Tumor Volume

Peak Value

“Red” fraction (high permeability)...

Test against

outcomes

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

70% PE thresholdvolume = 57 cc

Best agreement with histopathology

100% PE thresholdvolume = 43 cc

Strongest association with time-to-recurrence

Optimizing the imaging biomarkerExample from breast MRI

Effect of initial enhancement threshold on functional tumor volume (FTV) measurement by DCE-MRI:

Cox proportional hazards model for predicting RFS: Effect of FTV thresholds

SER > 0.0

SER > 0.6SER > 1.2

SER > 1.8

00.010.020.030.040.05

PE >

50.

00PE

> 6

0.00

PE >

70.

00PE

> 8

0.00

PE >

90.

00PE

> 1

00.0

PE >

110

.0

PE >

120

.0

PE >

130

.0

PE >

140

.0

PE >

160

.0

PE >

180

.0

PE >

200

.0

p value

0.0000

0.0001

0.0010

0.0100

0.1000

40 90 140 190

p Va

lue

PE Threshold

p value

SER > 0.0

SER > 1.0

SER > 0.0

SER > 0.6

SER > 1.2SER > 1.8

02468

10

Hazard Ratio

0

2

4

6

8

10

40 90 140 190PE Threshold

Hazard Ratio

SER > 0.0

SER > 1.0

I-SPY has been a process of continuous standardization and outcomes-based optimization

ACRIN 6698

(May 2012)I-SPY 2

T + novel agent

ACRIN 6657

Extension

ACRIN 6657

Original

I- SPY 1Standard AC/T

2002

2007

2010

• Greater standardization, protocol adherence, quality control; tracking

• More advanced MRI techniques introduced over time (DCE -> MRS -> DWI)

• Image data loss rate decreased from 12% in ISPY-1 to 1% in ISPY-2

* developed under NCI Academic-Industrial Partnership (AIP) Grant “Real-time in vivo MRI biomarkers for breast cancer pre-operative treatment trials” (R01 CA132870)

WORKFLOW:

• Image data transferred to ACRIN via TRIAD, then to UCSF• 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

*IDE-approved software for functional tumor volume (FTV) measurement

FTV used in I-SPY 2 adaptive randomization design

‘Real-time’ image analysis and reporting using the Hologic Aegis workstation

FTV = 31.5 cc

I-SPY2 image quality and protocol compliance (2010-present)

1% of exams non-analyzable for FTV

147, 8%23, 1%

1761, 91%

I-SPY2 image protocol compliance

Non-compliant,analyzable DCEs

Non-compliant,nonanalyzeable DCEs

Compliant,analyzeable

5

5

3

5

12

Factors preventing analysisMR parametersoutside protocol

Other

Image ArtifactPresent

Scan QualityInsufficient

Machine Failure

8, 4%

66, 29%

50, 22%24, 11%3, 1%

18, 8%

22, 10%

4, 2%6, 3%

23, 10%

Reasons for non-complianceBoxing notconsistent

Timing deviation -Auto timing

Timing Deviation -DCE acquisition

Threshold not 70%

PE threshold notconsistent

Late examsubmission

UCSF Core Lab: Margarita Watkins, Sachiko Suzuki, Krysta Banfield, Roxana Dhada

• 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

Where we are..

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

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