Data Inventory for Cancer Patients Receiving Radiotherapy for Outcome Analysis and Modeling
Jason Vickress, Rob Barnett and Slav Yartsev*
London Regional Cancer Program, London Health Sciences Centre, London, ON, N6A 4L6, Canada
- Corresponding Author:
- Slav Yartsev
London Regional Cancer Program
London Health Sciences Centre
London, ON, N6A 4L6, Canada
Tel.: +1-519-685-8600, ext-53171
E-mail: [email protected]
Received date: September 27, 2013; Accepted date: November 27, 2013; Published date: December 04, 2013
Citation: Vickress J, Barnett R, Yartsev S (2013) Data Inventory for Cancer Patients Receiving Radiotherapy for Outcome Analysis and Modeling. Int J Biomed Data Min 3:105. doi: 10.4172/2090-4924.1000105
Copyright: © 2013 Vickress J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Objective: To describe a database created for storing and analysis of patient specific data related to pre-treatment condition, treatment planning, and outcomes with a long term future objective to predict the optimal radiation therapy for new patients. Method: Construction of the centralized database for the collection of sufficient information for outcome analysis and modeling will be comprised of a SQL database and a commercial DICOM-RT PACS (MIM) server. Development of dedicated software for automatic transfer of DICOM-RT files from different sources to MIM PACS through unique import procedures. Planning dose objectives and constraints from Tomoplan (Accuray), Pinnacle (Philips) and Eclipse (Varian) treatment planning systems and daily position correction information from treatment units are transferred to the SQL database. Results and conclusion: A centralized database for all patient specific data, treatment planning and outcome information allows for determining correlations between treatment parameters and patient outcomes. The proximity between tumor and organs at risk is demonstrated as useful in determining optimal planning parameters in addition to the planning data of previously treated patients. The proposed database can perform automated analysis regarding quality assurance, dose accumulation for multiple treatments on different machines and assist physicians in choosing the optimal treatment modality.