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September 2012 Update September 13, 2012 Andrew J. Buckler, MS Principal Investigator, QI-Bench

September 2012 Update September 13, 2012 Andrew J. Buckler, MS Principal Investigator, QI-Bench. With Funding Support provided by National Institute of Standards and Technology. Agenda for Today.

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September 2012 Update September 13, 2012 Andrew J. Buckler, MS Principal Investigator, QI-Bench

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  1. September 2012 Update September 13, 2012 Andrew J. Buckler, MS Principal Investigator, QI-Bench With Funding Support provided by National Institute of Standards and Technology

  2. Agenda for Today • Update on statisticalanalysislibrarymodules, includingconceptualdevelopmentofaggregateuncertainty (Jovanna) • Overviewoffunctionality in Reference Data Set Manager staged for thedevelopmentiteration (Patrick) 2 2 2 2 2 2 2

  3. Unifying Goal of 2nd Development Iteration • Perform end-to-end characterization of vCT including meta-analysis of literature, incorporation of QIBA results, and "scaled up" using automated detection and reference volumetry method. • Integrated characterization across QIBA, FDA, LIDC/RIDER, Give-a-scan, Open Science sets (e.g., biopsy cases), through analysis modules and rolling up to an i_ file in zip archive. • Specifically have people like Jovanna, Ganesh, and Adele to use it (as opposed to only Gary, Mike/Patrick, and Kjell) 3 3 3 3 3 3 3 3

  4. Analyze: Update on Library Modules 4 4

  5. Analyze: Validation • Go to Jovanna’s desktop 5 5

  6. Analyze: Aggregate Uncertainty • Objective: comprehensively characterize the performance of an imaging biomarker. • Two orthogonal considerations: • Breadth of data used: use as much data as you can, regardless of where it comes from! • Nature of study designs that result in determination of uncertainty components • Approach: • Utilize common analytical pipeline to place literature and heterogeneous study results onto a common plane (this motivates the file conventions that drive the library design) • Roll-up separate components into an aggregate: current WIP for discussion 6 6

  7. Analyze: Aggregate Uncertainty • Go back to Jovanna’s desktop 7 7

  8. Execute: Basic Plan • Generalize processing framework from the previous development year • Support user-in-the-loop processing workflows for certain data-processing tasks • Refine input and output formats to adhere to newer standards (AIM 4.0, DICOM Segmentation Objects, etc.) 8 8

  9. Execute: Implementation • Support for Radiological Worklists and DICOM Query and Retrieve directly from Execute • Batchmake scripts initiate worklist item delegation and reader stations can retrieve those datasets from Midas as they would a PACS • Generalize processing API harness to allow arbitrary algorithm runs on arbitrary datasets. • Optimize the web API and scripting interface to allow more seamless interaction with other QI-Bench applications 9 9

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  11. 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 11

  12. 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. 12

  13. QI-BenchStructure / Acknowledgements • Prime: BBMSC (Andrew Buckler, Gary Wernsing, Mike Sperling, Matt Ouellette, Kjell Johnson, Jovanna Danagoulian) • 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) 13

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