1 / 11

September Monthly Call September 14, 2011 Andrew J. Buckler, MS Principal Investigator

QI-Bench optimizing performance through characterization. TM. September Monthly Call September 14, 2011 Andrew J. Buckler, MS Principal Investigator. Agenda. Study Description file and Data Tagging (Gary) Lab Protocol (Gary, Patrick, and Andy) QC (Alden and Ganesh)

zlata
Download Presentation

September Monthly Call September 14, 2011 Andrew J. Buckler, MS Principal Investigator

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. QI-Bench optimizing performance through characterization TM September Monthly Call September 14, 2011 Andrew J. Buckler, MS Principal Investigator

  2. Agenda • Study Description file and Data Tagging (Gary) • Lab Protocol (Gary, Patrick, and Andy) • QC (Alden and Ganesh) • Analysis status (Andy) • Year Outlook QI-Bench: Optimizing Performance through Characterization

  3. Study Description File and Data Tagging Goto Gary QI-Bench: Optimizing Performance through Characterization

  4. Lab Protocols • Specify • Consider concepts, relations, or properties • Decide if new or existing biomarker • Specify clinical context (either as first for new biomarker or as additional for existing biomarker) • Specify assay method (either as first for new biomarker or as additional for existing biomarker) • Formulate • Infer testable hypothesis contributory for characterizing performance of an assay method for a clinical context for use • Formulate data requirements to execute statistical analysis plans to prove testable hypotheses based on biomarker definition • Develop queries based on data requirements • Run queries to create study-specific Reference Data Sets • Execute • Create ground truth or other reference annotation and markup • Package algorithm or method using Batch Analysis Service API • Initiate a run • Sequence through tasks (potentially with help of Condor grid) • Run interfaced algorithm on a data set • Analyze • Develop analytical method to support testable hypothesis (e.g., in R scripts) • Perform an analysis for a specific test (repeat across tests considered for group analysis) • Annotate Biomarker DB with results for this test • Perform an analysis to characterize performance for the group • Annotate Biomarker DB with results of group statistic • Package • Discuss qualification goal with regulator AND/OR discuss device/software clearance/approval goal with regulator • Prepare qualification full data package AND/OR prepare 510(k) or PMA • Approve submission (according to relevant pathway) QI-Bench: Optimizing Performance through Characterization

  5. Lab Protocols • Formulate • Run queries to create study-specific Reference Data Sets • Execute • Create ground truth or other reference annotation and markup • Package algorithm or method using Batch Analysis Service API • Initiate a run • Sequence through tasks (potentially with help of Condor grid) • Run interfaced algorithm on a data set • Analyze • Develop analytical method to support testable hypothesis (e.g., in R scripts) • Perform an analysis for a specific test (repeat across tests considered for group analysis) QI-Bench: Optimizing Performance through Characterization

  6. Seed Point QC • demonstrator40 • 408 seeds (1A read 40) • pilot3A (was demonstrator15) • 156 seeds (1A read 15) • study1C (was demonstrator1C) • 2364 seeds (1C is set to read 66) • study1187 (was demonstrator1187) • 7122 seeds • Available: RIDER, IDRI, MSKCC “1B”, … QI-Bench: Optimizing Performance through Characterization

  7. Analyze Status: Metrology Definitions • File System Engineering\User Needs\Definitive Metrology Terms for QI 2011-09-13.docx QI-Bench: Optimizing Performance through Characterization

  8. Analyze Status: Stepwise Plan QI-Bench: Optimizing Performance through Characterization

  9. Analyze Status: MVT development • Lesion tracking • Other modalities and measures, e.g., SUV via FDG-PET • Properly functioning multiple regression and N-way ANOVA • Support Clinical Performance assessment (i.e., in addition to current Technical Performance) • Outcome studies • Integrated genomic/proteomic correlation studies • Group studies for biomarker qualification • Serial studies / change analysis • Persistent database • Scale-up to handle thousands of cases (10’s thousands of lesions) • Deploy as Web app QI-Bench: Optimizing Performance through Characterization

  10. Year Outlook QI-Bench: Optimizing Performance through Characterization

  11. Next Steps • QC demonstrator40 and process the lesion reads on the same 40 lesions used in the 1A pivotal as a 7th reader using 1A STATA method and compare results. [QI-Bench<->1A STATA] • Extend the STATA analysis to the 408, publish this result as the first QI-Bench publication (after QC). • New Mac and native-Unix servers • QI-Bench landing page (all user apps plus developer environment) • QC and re-tag pilot3A, study1C, and study1187 • Will send a Doodle to find a new meeting time that works better for West coast participants, still good for Europe. QI-Bench: Optimizing Performance through Characterization

More Related