Program Information
Oncospace: A Shared Radiation Oncology Database System Designed for Personalized Medicine, Decision Support, and Research
M Bowers1*, S Robertson2 , J Moore3 , J Wong4 , M Phillips5 , K Hendrickson6 , W Song7 , P Kwok8, T DeWeese9, T McNutt10 , (1) Johns Hopkins University, Baltimore, MD, (2) Johns Hopkins University, Baltimore, MD, (3) Johns Hopkins University, Baltimore, MD, (4) Johns Hopkins University, Baltimore, MD, (5) Univ Washington, Seattle, WA, (6) University of Washington, Seattle, WA, (7) Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, U ofT, Toronto, Ontario, (8) Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, U ofT, Toronto, Ontario, (9) Johns Hopkins University, Baltimore, MD (9) Johns Hopkins University, Baltimore, MD
Presentations
SU-E-P-26 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose:
Advancement in Radiation Oncology (RO) practice develops through evidence based medicine and clinical trial. Knowledge usable for treatment planning, decision support and research is contained in our clinical data, stored in an Oncospace database. This data store and the tools for populating and analyzing it are compatible with standard RO practice and are shared with collaborating institutions. The question is - what protocol for system development and data sharing within an Oncospace Consortium? We focus our example on the technology and data meaning necessary to share across the Consortium.
Methods:
Oncospace consists of a database schema, planning and outcome data import and web based analysis tools.
1) Database: The Consortium implements a federated data store; each member collects and maintains its own data within an Oncospace schema. For privacy, PHI is contained within a single table, accessible to the database owner.
2) Import: Spatial dose data from treatment plans (Pinnacle or DICOM) is imported via Oncolink. Treatment outcomes are imported from an OIS (MOSAIQ).
3) Analysis: JHU has built a number of webpages to answer analysis questions. Oncospace data can also be analyzed via MATLAB or SAS queries.
These materials are available to Consortium members, who contribute enhancements and improvements.
Results:
1) The Oncospace Consortium now consists of RO centers at JHU, UVA, UW and the University of Toronto. These members have successfully installed and populated Oncospace databases with over 1000 patients collectively.
2) Members contributing code and getting updates via SVN repository. Errors are reported and tracked via Redmine. Teleconferences include strategizing design and code reviews.
3) Successfully remotely queried federated databases to combine multiple institutions’ DVH data for dose-toxicity analysis (see below – data combined from JHU and UW Oncospace).
Conclusion:
RO data sharing can and has been effected according to the Oncospace Consortium model: http://oncospace.radonc.jhmi.edu/.
Funding Support, Disclosures, and Conflict of Interest: John Wong - SRA from Elekta Todd McNutt - SRA from Elekta Michael Bowers - funded by Elekta
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