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TU‐D‐AUD A‐01: Informatics Needs in Radiation Oncology Research: Challenges and Potential Solutions
Author(s) -
Deasy J
Publication year - 2008
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2962570
Subject(s) - workflow , data management , computer science , informatics , context (archaeology) , data science , health informatics tools , radiation oncology , medical physics , vendor , translational research , health informatics , data collection , medicine , data mining , radiation therapy , pathology , radiology , database , paleontology , statistics , mathematics , marketing , public health , biology , electrical engineering , business , engineering
Radiation oncology research faces an explosion of data collection, analysis, and management issues. Clinical trials research requires interfacing multiple vendor systems, storage of large‐scale, multi‐type data in systems that provide convenient, and secure access for analyses, quality assurance/improvement, and monitoring. HIPPA concerns are making multi‐institutional datasharing and clinical trial cooperation more challenging. Relevant datatypes now span the spectrum of multiple types of imaging, traditional radiotherapy data objects, image‐guidance data, physician‐reported, or patient‐reported outcomes, and new biological datasets and tissue/fluid samples along with their various bioprofiles and ‘‐omics.’ Effective management schemes must preserve the context, storyline, and linkage amongst related data. Effective utilization and learning based on all the available data will require new informatics tools and a more open approach to realizing the extent of the challenge and how ineffctively it has been addressed to date. In this presentation I will discuss these challenges that vendors, physicians, physicists, and informaticists face in helping to build out effective tools to support radiation oncology research in the next ten years. I will particularly focus on the emerging informatics needs of clinical trial groups (such as RTOG), as well as academic research centers, who want to fully utilize and learn from modern image‐guided, adaptive, biologically‐stratified, treatment paradigms. We will discuss approaches to problems associated with collecting outcomes data, utilizing complex datasets within the clinical workflow, and effectively learning from clinical, imaging, and biological data.