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Comprehensive Tool for Medical Image Analysis and Biomarker Evaluation

Utilize the QI-Bench tool to analyze CT volumetry data for measuring therapeutic efficacy in tumor growth. Specify, execute, and formulate feedback using reference datasets. Run MVT for variability analysis. Conduct intra- and inter-reader comparisons, outlier analysis, and plot visualization for insightful results.

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Comprehensive Tool for Medical Image Analysis and Biomarker Evaluation

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Presentation Transcript

  1. Analyze User Instructions, adapted from MVT Bob Schwanke Siemens With Funding Support provided by National Institute of Standards and Technology

  2. QI-Bench Overview RDF Triple Store obtained_by CT Volumetry CT used_for measure_of Therapeutic Efficacy Tumor growth Specify Execute Formulate Feedback Reference Data Sets Analyze QIBO Feedback Y=β0..n+β1(QIB)+β2T+ eij 2 2 2

  3. BiomarkerDB.SummaryStatistic+ = Analyze ({ReferenceDataSet .CollectedValue}); 3

  4. Load Cases and Run MVT (measurement variability tool)

  5. AIM output for selected case (from Batch Analysis Service)

  6. Choose Analysis Type (we’ll be adding new Types)

  7. Ground truth can be direct or indirect

  8. Choose Biomarker Measurements to Analyze (we’ll be adding Measurements) Deselect RECIST and WHO measurements

  9. Some Methods exist; we will create new ones (e.g., factor analysis) R Script for summary statistic

  10. Columns for calculation according to the Methods Column per measurement

  11. Output of Analysis Type run (this example intra-reader) Intra- and Inter-reader comparisons

  12. Output of Analysis Type run (this example inter-reader) Inter-reader measurements

  13. Additional outputs based on Methods … calculated

  14. Outlier Analysis with Drill-down is Supported (access to raw imagery) Outliers list

  15. Resulting plots – we’ll be adding plot types, e.g., box, radar, and K-M Configure plots as needed

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