June Monthly Call June 8, 2011 Andrew J. Buckler, MS Principal Investigator

1. QI-Bench optimizing performance through characterization TM June Monthly Call June 8, 2011 Andrew J. Buckler, MS Principal Investigator

2. Today • Design progress • Mapping of user requirements to semantic workflow and apps • Information modeling progress • Demonstrator progress • FDA data collection now a reference data set • Plans and progress re: execute and analysis • “Give back” seeds (as first in what we see as a series of results we produce for the community) • Test bed update QI-Bench: Optimizing Performance through Characterization

3. DESIGN PRogress QI-Bench: Optimizing Performance through Characterization

4. Enterprise Use Case workflows Core Activities for Biomarker Development Create and Manage Semantic Infrastructure and Linked Data Archives Commercial Sponsor Prepares Device/Test for Market Consortium Establishes Clinical Utility/Efficacy of Putative Biomarker Create and Manage Physical and Digital Reference Objects Collaborative Activities to Standardize and/or Optimize the Biomarker QI-Bench: Optimizing Performance through Characterization

5. Commercial Sponsor Prepares Device/Test for Market Create and Manage Physical and Digital Reference Objects Consortium Establishes Clinical Utility/Efficacy of Putative Biomarker Create and Manage Semantic Infrastructure and Linked Data Archives Core Activities for Biomarker Development QISL Quantitative Imaging Specification Language Reference Data Set Manager 3. Batch analysis scripts QIBO Reference Data Sets, Annotations, and Analysis Results Source of clinical study results Collaborative Activities to Standardize and/or Optimize the Biomarker UPICT Protocols, QIBA Profiles, entered with Ruby on Rails web service Batch Analysis Service Clinical Body of Evidence (formatted to enable SDTM and/or other standardized registrations 4. Output QIBO- UPICT Protocols, QIBA Profiles, literature papers and other sources (red edges represent biostatistical generalizability) BatchMake Scripts QI-Bench: Optimizing Performance through Characterization

6. QISL Quantitative Imaging Specification Language Reference Data Set Manager 3. Batch analysis scripts QIBO Reference Data Sets, Annotations, and Analysis Results Source of clinical study results UPICT Protocols, QIBA Profiles, entered with Ruby on Rails web service Batch Analysis Service Clinical Body of Evidence (formatted to enable SDTM and/or other standardized registrations 4. Output QIBO- UPICT Protocols, QIBA Profiles, literature papers and other sources (red edges represent biostatistical generalizability) BatchMake Scripts QI-Bench: Optimizing Performance through Characterization

7. goto EA QIBO, RadLex/ Snomed/ NCIt built using Ruby on Rails. caB2B, NBIA, PODS data elements, DICOM query tools. MIDAS, BatchMake, Condor Grid. Built using CakePHP. MVT portion of AVT, re-useable library of R scripts. STDM standard of CDISC into repositories like FDA’s Janus. QI-Bench: Optimizing Performance through Characterization

8. QI-Bench: Optimizing Performance through Characterization

9. DEmonstratorPRogress QI-Bench: Optimizing Performance through Characterization

10. Test bed update QI-Bench: Optimizing Performance through Characterization

11. Next Steps • Finish “Specify,” “Execute,” and “Analyze” demonstrators on 1A data – with extension to 1193 • Explore integration of NIST work, caB2B, and NBIA connector demonstrator for basis of “Formulate” • Explore use of Columbia system for reader interface in “Execute” • Continue to engage with CDISC on imaging domains for “Package” • Create design documentation, web service shells, and development environments for apps • Stage requirements into development iterations • Continue engagement with 3A, 3B, and PET test-bed project teams QI-Bench: Optimizing Performance through Characterization

12. QI-Bench optimizing performance through characterization TM QI-Bench: Optimizing Performance through Characterization