Using Big Data Analytics to Advance Precision Radiation Oncology

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

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Big clinical data analytics as a primary component of precision medicine is discussed, identifying where these emerging tools fit in the spectrum of genomics and radiomics research. A learning health system (LHS) is conceptualized that uses 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, however, is different, and an overview of the implications is discussed. With advancements in technologies and culture to improve the efficiency, accuracy, and breadth of measurements of the patient condition, the concept of an LHS may be realized in precision radiation therapy.

Original languageEnglish (US)
Pages (from-to)285-291
Number of pages7
JournalInternational Journal of Radiation Oncology Biology Physics
Issue number2
StatePublished - Jun 1 2018

ASJC Scopus subject areas

  • Radiation
  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research


Dive into the research topics of 'Using Big Data Analytics to Advance Precision Radiation Oncology'. Together they form a unique fingerprint.

Cite this