Quantitative Imaging Biomarker Allianceqibawiki.rsna.org/images/4/46/Why_QIBA_MR_specifics.pdf ·...

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PRINCIPAL LOGISTICAL AND

FINANCIAL SUPPORT

PROVIDED BY RSNA

WHY QIBA: MR SPECIFICS

Corporation Visit

Autumn 2010

Andrew J. Buckler, MS

Program Director, QIBA

Our Team

Autumn 2010 Why QIBA: MR Specifics 2

Moffitt Cancer CenterNCINIBIBNIHNISTNordicNeuroLab, Inc.NovartisOhio State UniversityPerceptive Informatics, Inc.PharmtracePhilips HealthcarePrism ClinicalQuiron Hospital, Valencia, SpainRadboud University Medical Center, NijmegenRadPharmRocheSiemens MedicalState University of New YorkTempleTeraRecon, Inc.The Institute of Cancer ResearchUniveristy of PennsylvaniaUniversity of Alabama at BirminghamUniversity of California, DavisUniversity of California, San DiegoUniversity of ChicagoUniversity of MichiganUniversity of PennsylvaniaUniversity of Southern CaliforniaUniversity of Texas Health Sciences Center, San AntonioUniversity of Texas M.D. Anderson Cancer CenterVanderbilt UniversityVirtualScopics, Inc.

ACR / ACRINAMAG Pharmaceuticals, IncAstraZenecaAvotec, IncBeth Israel Deaconess Medical CenterBioClinica, Inc.Biomedical SystemsBrigham and Women's HospitalBuckler Biomedical LLCCHOPColumbia UniversityDuke UniversityFDAGE HealthcareHologic, InciCAD, IncImagepaceIndiana UniversityInstitute for Medical Image Computing Johns Hopkins UniversityLehigh Valley Diagnostic ImagingMACMallinckrodt Institute of RadiologyMassachusetts General HospitalMedical College of WisconsinMedical NumericsMerckMerge HealthcareMITA (NEMA)

See speaker notes for full list of individual

names

Presenter

Presentation Notes

Mehdi Adineh, PhD ACR Edward Ashton, PhD VirtualScopics, Inc. Prashant Bansal, MS Beth Israel Deaconess Medical Center Daniel P. Barboriak, MD Duke University Jelle Barentsz, MD, PhD Radboud University Medical Center, Nijmegen Marcelino Bernardo, PhD NIH Michael Boss NIST Orest B. Boyko, MD, PhD University of Southern California Andrew J. Buckler, MS Buckler Biomedical LLC Michael H. Buonocore, MD, PhD University of California, Davis Yue Cao, PhD University of Michigan Paul Carnochan, MD The Institute of Cancer Research Chris Carr RSNA H. Cecil Charles, PhD Duke University Sandra Chica, MD Novartis Charles Clark, PhD NIST Laurence P. Clarke, PhD NCI NIH Cancer Imaging Program Geoffrey D. Clarke, PhD University of Texas Health Sciences Center at San Antonio David A. Clunie, MBBS RadPharm Patricia E. Cole, PhD, MD Imagepace Donald Cooper RT (R) (MR) Biomedical Systems Oana Craciunescu, PhD Duke University Jon DeVries, MBA Merge Healthcare Steve Drew RSNA Richard Eaton, JD Medical Imaging & Technology Alliance Steve Einstein Jeffrey L. Evelhoch, PhD Merck Melanie Freed FDA John Freymann NCI Peter Gall, PhD Siemens Robert J. Gillies, PhD Moffitt Cancer Center Ron B. Goldfarb, PhD NIST Igor D. Grachev, MD, PhD GE Healthcare Leo J. Grady, PhD Siemens Alexander Guimaraes, MD, PhD Harvard-Massachusetts General Hospital Sandeep N. Gupta, PhD GE Healthcare Donald P. Harrington, MD, MA State University of New York Luna Hilaire, PhD GE Healthcare Marko Ivancevic, PhD Philips Healthcare Edward F. Jackson, PhD MD Anderson Cancer Center R. Gilbert Jost, MD Mallinckrodt Institute of Radiology Gregory Karczmar, PhD University of Chicago Michael Kelley, MD NIST-Boulder Michael Knopp, MD, PhD Ohio State University Despina Kontos, PhD University of Pennsylvania Robert Krieg, PhD Siemens Hendrik Laue, PhD Institute for Medical Image Computing Jiachao Liang, PhD Hologic, Inc Guoying Liu, PhD NIBIB Marianne Maffoni TeraRecon, Inc. Luis Marti-Bonmati, MD, PhD Quiron Hospital, Valencia, Spain Michael A. Miller, PhD Indiana University Colin G. Miller, PhD BioClinica Desiree E. Morgan, MD University of Alabama at Birmingham Naira Muradyan, PhD iCAD, Inc Gillian Newstead, MD University of Chicago Aytekin Oto, MD University of Chicago William R. Ott, MD NIST Chahin Pachai, PhD BioClinica Charles Parisot GE Healthcare Andrea Perrone, MD BioClinica Janie Petti, RT(R)(CV)(MR) Biomedical Systems Jeffrey W. Prescott Ohio State University David E. Purdy, PhD Siemens Daniel S. Reich, MD, PhD Johns Hopkins University Brian D. Reynolds, PhD Duke University Wolf S. Richter Pharmtrace Mark Rosen, MD, PhD Univeristy of Pennsylvania Bruce Rosen, MD, PhD Harvard-Massachusetts General Hospital Stephen Russek, PhD NIST Roberto Sanz-Requena, PhD Quiron Hospital, Valencia, Spain Annette Schmid, PhD Perceptive Informatics, Inc. Karl Schmidt, PhD AMAG Pharmaceuticals, Inc Mitchell Schnall, MD, PhD University of Pennsylvania Katherine Scott, PhD Perceptive Informatics, Inc. Anand K. Singh, MD Harvard-Massachusetts General Hospital Walter M. Stadler, MD University of Chicago Daniel C. Sullivan, MD Duke University John C. Waterton, PhD AstraZeneca Gerald L. Wolf, PhD, MD AMAG Pharmaceuticals, Inc Akira Yamada University of Chicago Thomas Yankeelov, PhD Vanderbilt University Brenda Ye, MD FDA Gudrun Zahlmann, PhD Roche Harris Ahmad, MD BioClinica, Inc. Travis Allen Avotec, Inc Daniel P. Barboriak, MD Duke University Bradley Buchbinder, MD MAC Andrew J. Buckler, MS Buckler Biomedical LLC Paul E. Bullwinkel, PhD Avotec, Inc Geoffrey D. Clarke, PhD University of Texas Health Services Center at San Antonio Edgar (Ted) DeYoe, PhD Medical College of Wisconsin Cathy Elsinger, PhD NordicNeuroLab, Inc. Frank Engel-Murke, PhD Siemens Scott Faro, MD Temple DeVang Gor, MD Lehigh Valley Diagnostic Imaging Sandeep N. Gupta, PhD GE Healthcare Arne Hengerer, PhD Siemens Joy Hirsch, PhD Columbia University Edward F. Jackson, PhD MD Anderson Cancer Center Kathryn M. McMillan, PhD GE Healthcare Heiko Meyer, PhD Siemens David Mikulis, MD MAC Feroze Mohamed, PhD Temple Srinivasan Mukundan, Jr, MD, PhD Harvard-Brigham and Women's Hospital Rajaraman Narayanaswamy GE Healthcare Jeffrey Petrella, MD Duke University Jay J. Pillai, MD Johns Hopkins University James L. Reuss, PhD Prism Clinical Timothy Roberts, PhD CHOP Daniel C. Sullivan, MD Duke University Rebecca Theilmann, PhD University of California, San Diego Douglas M. Tucker, PhD, MBA Medical Numerics James T. Voyvodic, PhD, MBA Duke University Domenico Zaca, PhD Johns Hopkins University - Medical School Gudrun Zahlmann, PhD Roche

Quantification Builds on the Proud History of Innovation in MR


• Technical advances help us move from “qualitative image information” to “quantitative image biomarker measurements”

• Quantitative imaging biomarker data can be used to 1) provide improved differential diagnosis and staging, and 2) optimize both the delivery and assessment of personalized therapies

• Examples: • Early response assessment• Adaptive therapy• Optimized delivery of

combination therapies

T1+G

dK

trans

DW

IBaseline Day 21 XRT

Adaptive Radiation Therapy- tumor and normal tissue response -

Quantitative MR Applications Measure Disease more Precisely


• Clinical research, Clinical trials, and Drug discovery

• Assessing individual response to therapy

• Guidance for real time, e.g., MR-guided thermal therapy, or adaptive therapy, e.g. MR-guided adaptive radiotherapy

Already in use in single-and multi-center Phase I/II clinical trials

Increasing use clinically

48 hours4 weeks8 weeks

A

Percent Change in Ktrans

From Baseline (%)

Colon(30 mg/kg, NA)*

Colon‡

(30 mg/kg, NA)

Colon‡

(30 mg/kg, NA)

Ovarian(30 mg/kg, SD)

Renal†

(30 mg/kg, PD)

Renal†

(30 mg/kg, PD)

Pseudomyxoma(3 mg/kg, SD)

Sarcoma(1 mg/kg, SD)

Colon(30 mg/kg, SD)

Large Intestine(30 mg/kg, SD)

Sarcoma(30 mg/kg, SD)

Pancreatic(30 mg/kg, PD)

Large Intestine(30 mg/kg, SD)

Ovarian§

(30 mg/kg, PD)

Ovarian§

(30 mg/kg, PD)

-70% -60% -50% -40%-30%-20%-10% 0% 10% 20%

Liu, et al., J Clin Oncol 23:5464, 2005. Herbst et al., J Clin Oncol 27:2557, 2009

Quantification Increases the Utility and Value of Imaging


Biomarkers often follow Therapy into the clinic

as diagnostics for better therapy monitoring by:(A) Making clinical trials more effective:

• Faster (Window trials—quantitative endpoint); Cheaper (two to three weeks of drug exposure); Better (Phantom calibration, standardize method, open source reference tools, defined molecular targets, tailored delivery systems) ; Tighter(variance); Standardized (Protocols, Profiles)

(B) Making care more personalized to patient:

• Clinically proven detection and longitudinal quantification for follow-up

• Quantitative imaging biomarker measures incorporated into adaptive therapy

• Moves imaging from diagnostics and staging to therapy monitoring

Presenter

Presentation Notes

On animated arrows: Increased analytical power per subject and faster regime switching Increased efficacy and basis of comparison over time

Technical as well as Business Obstacles Impede Realization of the Opportunity


Even when individual companies do these steps, community need for standards required to address multi-vendor reproducibility are not accounted for.

Efforts by individual manufacturers to qualify quantitative imaging applications:

• Are more costly, and

• Run over longer time periods…

…than the business model of device and software manufacturers generally support.

Developm’t

Assay validation

cost

time

Endpoint qualification

These issues are exacerbated by lack of clarity in regulatory and reimbursement policy which increase the risk while decreasing the incentive

• Technical factors– Vendor-specific pulse sequence implementations– Field inhomogeneity– Surface coil intensity variation– Off-resonance & dielectric effects– Image artifacts and noise– Signal non-linearity with respect to agent

concentration– Lack of standardization (phantoms for contrast

response assessment, etc.)– Quantitative imaging not business model

(“upgrade dilemma”)• Physical factors

– Scan acquisition parameters– Image reconstruction parameters– Choice of contrast agents– ROI subjectivity– No standardized data analysis models or test

data• Biologic factors

– Patient gross motion (voluntary & involuntary)– Respiratory motion– Cardiac motion/cardiac output

Example drill down: IAUC/Ktrans using DCE-MRI

• DCE-MRI is not routine standard of care, but increasingly used clinically

• Current radiological practice is not quantitative• Manufacturers have different implementations of

pulse sequences that result in wide range of contrast response characteristics

• Manufacturers have nothing to compare to • Economic challenge to manufacturers in supporting

clinical trial applications vs clinical routine

• DCE-MRI is used in early phase clinical studies• There is increasing interest in clinical use as well• The diversity in technical solutions will remain due

to the lack of economic benefits to the vendors. The task is to come up with solutions to harmonize image biomarker results across vendors.

• Image quality is a major issue for all quantitative imaging

• Manufacturers are focusing on technology not biological validation. We have to deal with it for almost all exploratory types of activities.

• DCE – MRI: quantitative analysis of dynamic T1 contrast enhanced images

• Use cases: – Clinical trial related

• UC1: pharmacodynamic investigations (e.g., Ktrans) in early phase clinical trials• UC2: biological effect assessment as predictive biomarker • UC3: heterogeneity of disease/response

– Clinical routine use (future)• UC4: diagnostic decision making • UC5: therapeutic progress assessment in a clinical environment• UC6: therapy guidance / adaptive therapy

Autumn 2010 7Why QIBA: MR Specifics

Example drill down: PreSurgical Mapping using BOLD fMRI

• BOLD fMRI is not standard of care in clinical practice but employed increasingly

• CPT codes introduced (2007) – positive growth in reimbursement and adoption

• Methodology evolved via neuroscientists, neuropsychology – relatively new to radiology practice

• Current radiological practice is not quantitative

• fMRI not yet used in clinical trials

• Manufacturers provide technical solutions for implementation - variability in defining parameters (# volumes and TR)

• Required peripheral equipment not routinely provided by MR vendor – requiring integration of 3rd party technical solutions (stimulus presentation)

• Variability in analysis protocols – QC measures available from manufacturers (MR and SW)

• Economic challenge to manufacturers – volume is not there

• BOLD fMRI: quantitative analysis of EPI image sequences used in conjunction with functional imaging stimulus paradigms

• Use cases: – Clinical routine

• UC1: diagnostic assessment in surgical and/or treatment planning (e.g. tumor, epilepsy)

• UC2: risk assessment in decision making • UC3: therapeutic progress assessment in a clinical environment (e.g. stroke

recovery, TBI)– Clinical trials (future)

• UC4: biological effect assessment as predictive biomarker, therapeutic progress

Autumn 2010 8Why QIBA: MR Specifics

QIBA Addresses the Obstacles, Enabling Profitable New Products

Widely Available, High Performance, Quantitative Imaging

Result:

Imaging Science, Metrology, and Biostatistics

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elJune 2010 Buckler Biomedical LLC 9Autumn 2010 9Why QIBA: MR Specifics

QIBA Profile Content


Claims:“Detect tumor response with twice the sensitivity of RECIST in the Lung”

nodules > 1cm …

Actors TableCT Acquisition SystemMeasurement SoftwareRadiologist…

Activity DefinitionsCalibration / QA Patient PreparationImage AcquisitionReconstructionPost-Processing Analysis / Measurement Reading / Interpretation…

User Perspective

Will it do what I need?

What/who do I need

to 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 products

are affected?

What do I have to implement;

(features, capabilities,

performance targets)

How will I be tested?

Details:

QIBA “Industrializes” QI


Select a Biomarker

AcademicResearch

ClinicalTrial Use

ClinicalPractice

DraftQIBA Profile

Coordinate Groundwork

Draft Protocol

ValidateEquipment

& Sites

• 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

• Apply selection criteria:−Transformational, Translational, Feasible, Practical

QIBA PROFILE

I. CLINICAL CONTEXT

II. CLAIMS

III. DETAILS

IV. COMPLIANCE

V. ACKNOWLEDGEMENTS

QIBA GROUNDWORK for ANALYZING/CREATING DATA

to INFORM PROFILES

Reports and Data Sets Analyzing:

• Technical characteristics and sources of errors

• Stand-alone performance on phantoms and synthetic data

• Clinical performance in terms of intra- and inter-reader variability

• Clinical efficacy• Standardization across

scanners


PRODUCT CREATION PROCESS of DEVICE and SOFTWARE

MANUFACTURERS

Customer Requirements Specification

System Requirements Specification

Verification Plan and Protocol

Participation and visibility for all stakeholders

QIBA Leverages Resources and Bridges Perspectives Across Communities

QIBA PROFILE

I. CLINICAL CONTEXT

II. CLAIMS

III. DETAILS

IV. COMPLIANCE

V. ACKNOWLEDGEMENTS

QIBA GROUNDWORK for ANALYZING/CREATING DATA

to INFORM PROFILES

Reports and Data Sets Analyzing:

• Technical characteristics and sources of errors

• Stand-alone performance on phantoms and synthetic data

• Clinical performance in terms of intra- and inter-reader variability

• Clinical efficacy• Standardization across

scanners


PRODUCT CREATION PROCESS of DEVICE and SOFTWARE

MANUFACTURERS

Customer Requirements Specification

System Requirements Specification

Verification Plan and Protocol

Participation and visibility for all stakeholders

Our Offer – and our Request – is to Increase your Engagement with Us

Participate in DCE-MRI and

fMRIgroundwork

Use Profiles to create QIBA-

compliant product

Assign resources to Profiling for

cancer and neuroologyapplications

To be specific, for DCE-MRI and BOLD fMRI, we are requesting:• Assist with collaborative groundwork activities:

– Participate in experimental studies for characterizing performance.

– Review requests and provide feedback on standardizing acquisition system characteristics.

• Apply engineering resources to help refine QIBA profiles:– Assist with the engineering analysis being performed to arrive at

requirement levels and functional specifications.

– Assist with the writing of QIBA profile claims.

• Prepare for future product development and marketing:– Review QIBA profiles and current product performance claims.

– Perform QIBA studies and internally validate QIBA compliance.

– Obtain approval to claim QIBA compliance.


June 2010 Buckler Biomedical LLC 15

We can’t do it alone, you can’t do it alone. We need to do it together.


• Utilization of imaging grows as it is used for monitoring response and adapting therapy.

• Technical as well as business obstacles impede commercialization.

• QIBA addresses these obstacles, accounting for individual stakeholder value propositions.

• The commercialization model is similar to IHE, including relationship to product creation process.

• Collaborative resources in precompetitive model address the science and provide critical mass as well as cost sharing for regulatory data collection.

• We invite you to join us in making the critical step of defining Profiles.

• New products compliant with the outputs of this process will fuel a virtuous cycle of innovation in this next generation of imaging, rewarding all participants.

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