Using Big Data Analytics to Advance Precision Radiation Oncology

Publication date: Available online 2 March 2018
Source:International Journal of Radiation Oncology*Biology*Physics
Author(s): Todd R. McNutt, Stanley H. Benedict, Daniel A. Low, Kevin Moore, Ilya Shpitser, Wei Jiang, Pranav Lakshminarayanan, Zhi Cheng, Peijin Han, Xuan Hui, Minoru Nakatsugawa, Junghoon Lee, Joseph A. Moore, Scott P. Robertson, Veeraj Shah, Russ Taylor, Harry Quon, John Wong, Theodore DeWeese
Big Clinical Data Analytics as a primary component of precision medicine is discussed, ] identifying where these emerging tools fit in the spectrum of genomic and radiomic research. A learning health system (LHS) is conceptualized that utilizes clinically acquired data with machine learning to advance the initiatives of precision medicine. The LHS is comprehensive and can be used for clinical decision support, discovery, and hypothesis derivation. These developing uses can positively impact the ultimate management and therapeutic course for patients. The conceptual model for each use of clinical data, is however different, and an overview of the implications is discussed. With advancement in technologies and culture to improve the efficiency, accuracy and breadth of measurements of the patient condition, the concept of a LHS may be realized in precision radiotherapy.



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