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Proceedings Paper

A DICOM-RT radiation oncology ePR with decision support utilizing a quantified knowledge base from historical data
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Paper Abstract

During the last 2 years we have been working on developing a DICOM-RT (Radiation Therapy) ePR (Electronic Patient Record) with decision support that will allow physicists and radiation oncologists during their decision-making process. This ePR allows offline treatment dose calculations and plan evaluation, while at the same time it compares and quantifies treatment planning algorithms using DICOM-RT objects. The ePR framework permits the addition of visualization, processing, and analysis tools, which combined with the core functionality of reporting, importing and exporting of medical studies, creates a very powerful application that can improve the efficiency while planning cancer treatments. Usually a Radiation Oncology department will have disparate and complex data generated by the RT modalities as well as data scattered in RT Information/Management systems, Record & Verify systems, and Treatment Planning Systems (TPS) which can compromise the efficiency of the clinical workflow since the data crucial for a clinical decision may be time-consuming to retrieve, temporarily missing, or even lost. To address these shortcomings, the ACR-NEMA Standards Committee extended its DICOM (Digital Imaging & Communications in Medicine) standard from Radiology to RT by ratifying seven DICOM RT objects starting in 1997 [1,2]. However, they are not broadly used yet by the RT community in daily clinical operations. In the past, the research focus of an RT department has primarily been developing new protocols and devices to improve treatment process and outcomes of cancer patients with minimal effort dedicated to integration of imaging and information systems. Our attempt is to show a proof-of-concept that a DICOM-RT ePR system can be developed as a foundation to perform medical imaging informatics research in developing decision-support tools and knowledge base for future data mining applications.

Paper Details

Date Published: 11 March 2008
PDF: 6 pages
Proc. SPIE 6919, Medical Imaging 2008: PACS and Imaging Informatics, 691905 (11 March 2008); doi: 10.1117/12.773087
Show Author Affiliations
Jorge R. Documet, Univ. of Southern California (United States)
Brent Liu, Univ. of Southern California (United States)
Anh Le, Univ. of Southern California (United States)
Maria Law, The Hong Kong Polytechnic Univ. (Hong Kong China)

Published in SPIE Proceedings Vol. 6919:
Medical Imaging 2008: PACS and Imaging Informatics
Katherine P. Andriole; Khan M. Siddiqui, Editor(s)

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