The NCI Office of Cancer Centers Learning Series Bringing Quantitative Imaging to Cancer Center Clinical Trials - PowerPoint PPT Presentation

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The NCI Office of Cancer Centers Learning Series Bringing Quantitative Imaging to Cancer Center Clinical Trials
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The NCI Office of Cancer Centers Learning Series Bringing Quantitative Imaging to Cancer Center Clinical Trials

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  1. The NCI Office of Cancer Centers Learning Series Bringing Quantitative Imaging to Cancer Center Clinical Trials Dial In: 888-469-0693 Passcode: CANCER CENTERSFor Technical Support, call 800-857-8777 and choose option 3.

  2. The NCI Office of Cancer Centers Learning Series Bringing Quantitative Imaging to Cancer Center Clinical Trials Tuesday, October 4, 2011 2:00 to 3:30 pm EDT Moderator Shannon L. Silkensen, PhD Program Director Office of Cancer Centers National Cancer Institute National Institutes of Health Bethesda, MD Featured Presenters Robert J. Nordstrom, PhD Program Director, Imaging Technology Development Branch Cancer Imaging Program, NCIBethesda, MD David Mankoff, MD, PhD Professor & QIN Program GranteeUniversity of Washington Seattle, WA

  3. A Quick Guide to Your Screen • Please submit your question via the Q & A box on the right hand side of your screen. If you do not see the Q&A box, you can expand it by clicking the Q&A on the top navigation panel and dragging it to the right side of your screen.

  4. The Quantitative Imaging Network Building Resources for NCI Cancer Centers A Presentation by Robert J. Nordstrom, PhD National Cancer Institute Cancer Imaging Program

  5. Welcome… A series of web-based seminars on Topics of Interest to NCI Cancer Centers

  6. Today’s Topic Quantitative imaging to predict and measure response to therapy. The Quantitative Imaging Network QIN • Network mission & goals • Organization • Value & resource to Cancer Centers

  7. But first, a word about me Robert J. Nordstrom, Ph.D. • Program Director: Cancer Imaging Program (CIP) of NCI (2006 to present) • Lead program director for Quantitative Imaging Network • Background in industry prior to NIH • Algorithm development • Clinical trials • Systems validation

  8. The CIP Program Team • Larry Clarke, Ph.D. Branch Chief, CIP, Science Officer QIN • Gary Kelloff, M.D. Special Assistant to Associate Director, Science Officer QIN • Program Directors: Leads for QIN Working Groups • Pushpa Tandon, Ph.D. Program Director, CIP • Keyvan Farahani, Ph.D. Program Director, CIP • Huiming Zhang, Ph.D. Program Director, CIP • James Deye, Ph.D. Program Director, RRP • QIN Informatics and Image Archive Support • John Freymann, Bioinformatics Contractor • Justin Kirby, Bioinformatics Contractor

  9. The Cancer Imaging Program: Branches Image Technology Development Image Guided Intervention Clinical Trials Molecular Imaging

  10. Translational Research in CIP ACRIN Quantitative Imaging Network (U01) IND/IDE Submission Prototyping Standardization FDA Submission Translational Research Pipe Line Concept Feasibility Testing Validation Standard of Care Feasibility & Design Clinical Studies Traditional R01 grants DCTD NExT Program Academic/Industrial Partnerships (R01)

  11. Imaging Quantitative Imaging

  12. Events of 1895 changed medical imaging Wilhelm Rontgen discovered x-rays while working in his laboratory in 1895. He later won the first Nobel prize in physics in 1901 for his work.

  13. Imaging technology Anatomic Clinical imaging can play an increased role in patient care without new and costly technology. Organ Cellular Example: Quantitative measure of prediction and response to therapies Molecular ?

  14. What is quantitative imaging? • Quantitative imaging is the acquisition, extraction and characterization of relevant numerically measurable features from medical images for use in research and patient care. • Needed for measurement of therapy response, • Needed for prediction of response, • Needed to link imaging results to other cancer biomarkers such as genomic and/or proteomic data.

  15. A quantitative measure RECIST: Response Evaluation Criteria in Solid Tumors Long axis=17.7 mm Short axis= 9.6 mm Long axis=17.1 mm Short axis= 7.4 mm 3% reduction in linear dimensions Baseline 21 day post therapy: Gefitinib Data from MSKCC

  16. This suggests a number of questions • Is the measured change real? • What are the sources of variance in the process? • What would an additional image show? • Should therapy be continued or altered? • Are we measuring the right parameter? • Hypoxia • Angiogenesis • Tissue elasticity • Metabolic function • Combinations of measurable parameters

  17. Then even more questions • Can sources of variance & error be reduced? • How are quantitative results affected by different imaging platforms or modalities? • How does the latter vary across clinical sites? • What data collection and analysis schemes work “best”? • Can software tools validated on one imaging modality or targeted cancer problem be used on another?

  18. NCI program announcement • Originally PAR-08-225, now PAR-11-150. • Quantitative Imaging Network • Designed to support multi-disciplinary teams to study these and other quantitative imaging questions. • Uses the U01 mechanism: cooperative agreement, not a grant. • Program involvement with administration. • Creates a network of teams working together.

  19. Mission Statement The mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by the development and validation of data acquisition, analysis methods, and tools to tailor treatment in individual patients and to predict or monitor the response to drug or radiation therapy.

  20. To do all this…. • A network of research teams is needed • The cancer problem is large • Different tumor sites and tumor types • Different imaging modalities and methods • Consensus through networking • Validation & standardization methods • Open science approaches • Data sharing & archiving

  21. To do all this…. • Multi-disciplinary teams are needed • Oncologists • Radiologists • Imaging scientists • Bioinformatics specialists • Team – team interactions • Network established working groups in specialty areas

  22. QIN builds on program history • IRAT: Imaging Response Assessment Teams • Cancer Center teams (8) to promote imaging in clinical trials and imaging team science. • RIDER: Reference Imaging Database to Evaluate Response • Lung image data & other organ sites. Metadata included • Web accessible • Phantom data : Longitudinal variance studies

  23. In line with CQIE effort • Standardize data collection protocols • Standardized set of routine QC activities for sites • Qualification of imaging sites • NCI Centers for Quantitative Imaging Excellence • ACRIN doing the qualifications • Pre-qualify sites to be “trial ready” • PET/CT • DCE MRI • Brain & body • Annual requalification Across all manufacturers

  24. Value of QIN to the Cancer Centers • QIN can serve as a common imaging core or technical resource for future Cancer Center trials involving imaging. • QIN can potentially reduce imaging trial costs. • QIN can facilitate implementation of imaging standards. • New quality control and quality assurance studies • Correlation with other biomarker research (e.g. genomics, proteomics) • QIN can assist in integrating imaging research into different programs within and across Cancer Centers. • QIN can position Cancer Center imaging researchers to be more competitive for NIH R01 funding.

  25. QIN Network Organization Steering Committee …. Technical Teams Working Groups

  26. QIN Working Groups • Each working group • Is autonomous from research teams • Has its own mission statement and goals • Focused on specific quantitative imaging challenges • Works to build consensus in area of focus • Communicates results through white papers

  27. QIN Working Groups • Data Collection • Image Analysis & Performance Metrics • Bioinformatics/IT & Data Sharing • Clinical Trial Design & Development • Outreach: External/Industrial Relations

  28. The QIN Map July 2011 September 2011 Univ. of Washington Oregon Health & Science. Brigham & Women’s Columbia Univ. Univ. of Pittsburgh UCSF Univ. of Iowa Mass General Stanford Univ. Johns Hopkins Univ. Vanderbilt Univ. H. Lee Moffitt

  29. Nine teams currently in QIN

  30. Several QIN research hypotheses • Medical imaging has been thwarted by a lack of standardized image analysis tools. • Molecular imaging methods lack accepted, robust and accurate quantitative metrics to access therapy response. • Quality assurance methods for imaging modalities fall short when quantitative imaging is used. • Quantitative MR analysis tools can be used as biomarker guides for targeted therapy and as a surrogate for disease recurrence in prostate. • Quantitative analysis of routine PET and CT images of lung cancer can be prognostic.

  31. Timeline for entry into QIN Stanford U. May 1, 2010 Release date August 2008 Oct 2011 Brigham & Women’s September 2010 H. Lee Moffitt CC March 9,2010 UCSF Sept 26, 2010 2009 2010 2011 Columbia U. Sept 10, 2010 U. Iowa April 1, 2010 Vanderbilt U. May 1, 2010 U. Washington April 15, 2010 U. Pittsburgh Sept 1, 2009 Mass General Hospital May 6, 2011

  32. Pieces to quantitative imaging puzzle Data Analysis & Tool Validation Data Collection & Variance Studies Informatics & Data Sharing

  33. Data Collection Reducing Physical Measurement Uncertainty Data Collection & Variance Studies

  34. Measurement uncertainty Data Collection & Variance Studies Measurement Uncertainty = S [Physical] + [Biological] Physical = Instrument dependent Hardware variance & bias Data collection methods Reconstruction errors Biological = Patient Variability

  35. Impact on Trial Sample Size Data Collection & Variance Studies power = 80% significance = 0.05 40% error 30 % Sample Size 20% Sample size increases as error increases 10% True Effect Size (%) University of Washington data

  36. Measurement uncertainty in trials Data Collection & Variance Studies ± 10% error Responders Region of unknown response Non-responders

  37. Measurement uncertainty in trials Data Collection & Variance Studies ± 2% error Responders Region of unknown response Non-responders

  38. Measurement uncertainty in trials Data Collection & Variance Studies • So, reducing measurement uncertainty will potentially reduce the number of patients needed in a clinical trial, and increase the useful data that can be obtained. This may reduce the cost of trials. It also may permit adaptive trials ( I-SPY-2) The question is: How are measurement uncertainties reduced?

  39. Reducing measurement uncertainty Data Collection & Variance Studies • Standardize data collection protocols • Design phantoms to mimic the clinical measurement, namely to study variance over time line of the clinical study • PET/CT: NIST traceable source • Multiple sites & vendors • Repeat & longitudinal studies • Diffusion Weighted MRI: Pragmatic design • Multiple sites & vendors • Repeat & longitudinal studies • Goal: Understand physical sources of variance

  40. Incorrect calibration Correct calibration Measured values for 10 different PET centers Kinahan JNM 2007 Technical and procedural obstacles Data Collection & Variance Studies Incorrect calibration Correct calibration Single-center calibration errors Lockhart JNM 2011 Multi-center scanner resolution differences University of Washington data

  41. Diffusion weighted MRI variance Data Collection & Variance Studies Within 5% 30+ Sites US and UK 1.5T 3T Vendor 1 1.5T 3T Vendor 2 1.5T 3T Vendor 3

  42. Reducing measurement uncertainty Data Collection & Variance Studies • Standardize data collection protocols • Explore performance or model-based data collection to reduce imaging platform dependence. • Create a common imaging core or technical resource for the Cancer Centers. • Implementation of quality control and variance studies in imaging for clinical trials. • Facilitate sharing of imaging data and tools across Cancer Centers.

  43. Data Analysis &Tool Validation Support clinical decision support systems Data Analysis & Tool Validation

  44. Definition Data Analysis & Tool Validation • A “Tool” is a software procedure for • Searching for images according to specific input criteria, and/or • Isolating a region (or regions) of interest in an image or group of images (segmentation), and/or • Combining different images in a desired way (registration), then • Extracting quantitative (numerical) information from the region or regions of interest, or total body tumor burden, then • Perform analysis to predict or measure therapy response.

  45. The “Model” Program Data Analysis & Tool Validation Similar to assay validation Annotated database with metadata & outcomes An Appropriate Clinical Trial Clinical trial Development Tool Validation Data & Results A QIN Member

  46. QIN links to clinical trials Data Analysis & Tool Validation • Ongoing or planned trials must be identified: Trials not supported by QIN. • Phase I,II, and/or III. • QIN will support additional images beyond trial protocol (IRB approval). • QIN will support correlative studies such as genomics.

  47. Clinical Trials Data Analysis & Tool Validation • Recent meeting of American College of Radiology Imaging Network (ACRIN) • Clinical trials designed specifically to discover biomarkers are difficult to design and execute. • QIN is not doing this. • The clinical trials used by QIN have their own goals and endpoints (therapy results). • QIN takes existing imaging data from ongoing trials or creates required images to develop and validate imaging tools to support clinical decision making.

  48. QIN as a technical resource Data Analysis & Tool Validation Validated Methods Informatics QIN Variance Reduction Future clinical trials Cancer Center clinical trials Clinical Tools Phantoms Quantitative Methods ACRIN and other trial groups

  49. A Quantitative Measure Revisited Data Analysis & Tool Validation Volume=525.4 mm3 Volume=886.2 mm3 41% change in volume Baseline 21 day post therapy: Gefitinib Data from MSKCC

  50. Image-derived heat maps Data Analysis & Tool Validation CT images of lung cancer 219 image features Unsupervised (Automatic) feature clustering Clustering shows three patterns Allows visualization similar to genomic array analysis. Data from H. Lee Moffitt Cancer Center