Improving Normal Tissue Complication Probability Models: The Need to Adopt a 'Data-Pooling' Culture
- Department of Human Oncology, University of Wisconsin School of Medicine, Madison, WI (United States)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY (United States)
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI (United States)
- Department of Radiation Oncology, University of Rochester Cancer Center, Rochester, NY (United States)
- Center for Comprehensive Informatics, Emory University, Atlanta, GA (United States)
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC (United States)
Clinical studies of the dependence of normal tissue response on dose-volume factors are often confusingly inconsistent, as the QUANTEC reviews demonstrate. A key opportunity to accelerate progress is to begin storing high-quality datasets in repositories. Using available technology, multiple repositories could be conveniently queried, without divulging protected health information, to identify relevant sources of data for further analysis. After obtaining institutional approvals, data could then be pooled, greatly enhancing the capability to construct predictive models that are more widely applicable and better powered to accurately identify key predictive factors (whether dosimetric, image-based, clinical, socioeconomic, or biological). Data pooling has already been carried out effectively in a few normal tissue complication probability studies and should become a common strategy.
- OSTI ID:
- 21372145
- Journal Information:
- International Journal of Radiation Oncology, Biology and Physics, Vol. 76, Issue 3; Other Information: DOI: 10.1016/j.ijrobp.2009.06.094; PII: S0360-3016(09)03301-X; Copyright (c) 2010 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; ISSN 0360-3016
- Country of Publication:
- United States
- Language:
- English
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