Modern medicine, including the care of the cancer patient, has significantly advanced, with the evidence-based medicine paradigm serving to guide clinical care decisions. Yet we now also recognize the tremendous heterogeneity not only of disease states but of the patient and his or her environment as it influences treatment outcomes and toxicities. These reasons and many others have led to a reevaluation of the generalizability of randomized trials and growing interest in accounting for this heterogeneity under the rubric of precision medicine as it relates to personalizing clinical care predictions, decisions, and therapy for the disease state. For the cancer patient treated with radiation therapy, characterizing the spatial treatment heterogeneity has been a fundamental tenet of routine clinical care facilitated by established database and imaging platforms. Leveraging these platforms to further characterize and collate all clinically relevant sources of heterogeneity that affect the longitudinal health outcomes of the irradiated cancer patient provides an opportunity to generate a critical informatics infrastructure on which precision radiation therapy may be realized. In doing so, data science–driven insight discoveries, personalized clinical decisions, and the potential to accelerate translational efforts may be realized ideally within a network of institutions with locally developed yet coordinated informatics infrastructures. The path toward realizing these goals has many needs and challenges, which we summarize, with many still to be realized and understood. Early efforts by our group have identified the feasibility of this approach using routine clinical data sets and offer promise that this transformation can be successfully realized in radiation oncology.
|Original language||English (US)|
|Number of pages||9|
|Journal||International Journal of Radiation Oncology Biology Physics|
|State||Published - Mar 15 2019|
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Cancer Research