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May 2012 Update May 10, 2012 Andrew J. Buckler, MS Principal Investigator, QI-Bench PowerPoint Presentation
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May 2012 Update May 10, 2012 Andrew J. Buckler, MS Principal Investigator, QI-Bench

May 2012 Update May 10, 2012 Andrew J. Buckler, MS Principal Investigator, QI-Bench

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May 2012 Update May 10, 2012 Andrew J. Buckler, MS Principal Investigator, QI-Bench

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  1. May 2012 Update May 10, 2012 Andrew J. Buckler, MS Principal Investigator, QI-Bench With Funding Support provided by National Institute of Standards and Technology

  2. Resources are needed to address widening gap in imaging capability as practiced vs. capability of modern medicine 2 2 2 2 2 2

  3. 18F-NaF 18F-FDG bone formation glucose metabolism 18F-FACBC 18F-FLT amino acid metabolism proliferation Biologic Target angiogenesis hypoxia 18F-FMISO DCE-MRI PET receptor status apotosis 18F-FES 18F-XXX Example: Beyond Anatomy to Palette of Functional Measures 3 3 3 3 3 3 3

  4. QI-Bench is a resource that may be used by single sponsors, defined-entity consortia, or true open science programs 4 4 4 4 4 4

  5. Example: COPD Either: To assist individual suppliers in optimizing their offerings To assist groups like COPDgene consortia To enable open development such as by QIBA or flexible mix of these. 5 5 5 5 5 5 5

  6. QI-Bench is composed of building blocks Provenance architecture of Iterate Data warehouse 6 6 6 6 6 6

  7. Provenance architecture of Iterate Example: DCE-MRI using Patient, Synthetic, and Phantom Data Curate, maintain and serve reference data sets Execute batch runs over multi-parameter synthetic data Characterize performance Data warehouse 7 7 7 7 7 7 7

  8. Primary GE Healthcare Icon Medical Imaging Columbia University INTIO, Inc. Vital Images, Inc. Median Technologies Vital Images, Inc. FraunhoferMevis Siemens Moffitt Cancer Center Toshiba Test bed: CT volumetry method challenge (“3A”) Some of the Participants Secondary Investigation n Investigation 1 Investigation Investigation Pilot Pilot Pilot Pilot • Defined set of data • Defined challenge • Defined test set policy Pivotal Pivotal Pivotal Pivotal Train Train Train Train Test Test Test Test 8 8 8 8 8 8

  9. Primary Some of the Participants Transformation Secondary Modality Environment Therapy Decision Environment Broader capability: Systematic qualification of CT volumetry PROFILE Authoring and Testing human expert view machine view Transformation Transformation • Median Technologies • Vital Images, Inc. • FraunhoferMevis • Siemens • Moffitt Cancer Center • Toshiba • GE Healthcare • Icon Medical Imaging • Columbia University • INTIO, Inc. • Vital Images, Inc. Investigation Investigation Investigation 1 Investigation n Pilot Pilot Pilot Pilot Inter-analysis technique (algorithm) variability (3A) Correlation with clinical endpoints and outcomes (3B) Extend to other Lesion characteristics Explore figures- of-merit and QC procedures • Defined set of data • Defined challenge • Defined test set policy Pivotal Pivotal Pivotal Pivotal Train Train Train Train Test Test Test Test Minimum detectable biological change (1B) Inter-scanner model, and -site variability (1C) Intra- and inter-reader variability (1A) 5 readers, 3 reads each Performance-based branch / compliance procedure Therapy- Patient Patient Feedback Machine Human Observer 9 9 9 9 9 9 9

  10. Update: Iterate progress • Added small workflows to Iteratedemonstration server. • Stored and retrieved data on server via workflows. • Distinction between workflow execution on server and local workstation. 10

  11. Update: Iterate coming attractions • Upgraded base software installation. • New site with workflows for local workstation. • Server workflows that produce outputs. 11

  12. Analyze performs the statistical analyses… 1 2 3 12

  13. …and adds the results to the knowledgebase (using W3C “best practices” for “relation strength”). URI=45324 1 2 URI=9956 URI=98234 3 13

  14. Provenance architecture of Iterate • Demonstration of statistical analysis using R within Iterate • Obtain data • Produce summary plots and statistics • Perform analyses 14

  15. Conceptual Example • Example data based on QIBA 3A layout • Phantom study • 5 lesions, each read by 3 readers • The study design enables us to answer several questions • Is there a difference among readers on volume? • If so, which are different? • Is there a difference among readers on bias? • If so, which are different? • Appropriate figures flow from the study design and enable us to visually understand the data 15 15 15 15 15

  16. Data

  17. 17 17 17 17 17

  18. 18 18 18 18 18

  19. 19 19 19 19 19

  20. 20 20 20 20 20

  21. summary1 21 21 21 21 21

  22. summary2 22 22 22 22 22

  23. tukey1 23 23 23 23 23

  24. tukey2 24 24 24 24 24

  25. 25 25 25 25 25

  26. 26 26 26 26 26

  27. Up and running now for you to use 27 27 27 27 27 27

  28. 28

  29. Value proposition of QI-Bench • Efficiently collect and exploit evidence establishing standards for optimized quantitative imaging: • Users want confidence in the read-outs • Pharma wants to use them as endpoints • Device/SW companies want to market products that produce them without huge costs • Public wants to trust the decisions that they contribute to • By providing a verification framework to develop precompetitive specifications and support test harnesses to curate and utilize reference data • Doing so as an accessible and open resource facilitates collaboration among diverse stakeholders 29

  30. Summary:QI-Bench Contributions • We make it practical to increase the magnitude of data for increased statistical significance. • We provide practical means to grapple with massive data sets. • We address the problem of efficient use of resources to assess limits of generalizability. • We make formal specification accessible to diverse groups of experts that are not skilled or interested in knowledge engineering. • We map both medical as well as technical domain expertise into representations well suited to emerging capabilities of the semantic web. • We enable a mechanism to assess compliance with standards or requirements within specific contexts for use. • We take a “toolbox” approach to statistical analysis. • We provide the capability in a manner which is accessible to varying levels of collaborative models, from individual companies or institutions to larger consortia or public-private partnerships to fully open public access. 30

  31. QI-BenchStructure / Acknowledgements • Prime: BBMSC (Andrew Buckler, Gary Wernsing, Mike Sperling, Matt Ouellette) • Co-Investigators • Kitware (Rick Avila, Patrick Reynolds, JulienJomier, Mike Grauer) • Stanford (David Paik) • Financial support as well as technical content: NIST (Mary Brady, Alden Dima, John Lu) • Collaborators / Colleagues / Idea Contributors • Georgetown (Baris Suzek) • FDA (Nick Petrick, Marios Gavrielides) • UMD (Eliot Siegel, Joe Chen, Ganesh Saiprasad, Yelena Yesha) • Northwestern (Pat Mongkolwat) • UCLA (Grace Kim) • VUmc (Otto Hoekstra) • Industry • Pharma: Novartis (Stefan Baumann), Merck (Richard Baumgartner) • Device/Software: Definiens, Median, Intio, GE, Siemens, Mevis, Claron Technologies, … • Coordinating Programs • RSNA QIBA (e.g., Dan Sullivan, Binsheng Zhao) • Under consideration: CTMM TraIT (Andre Dekker, JeroenBelien) 31