Practical data collection and extraction for big data applications in radiotherapy

Todd R. McNutt, Michael Bowers, Zhi Cheng, Peijin Han, Xuan Hui, Joseph Moore, Scott Robertson, Charles Mayo, Ranh Voong, Harry Quon

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The capture of high-quality treatment data and outcomes is necessary in order to learn from our clinical experiences with big data analytics. In radiotherapy, there are several practical challenges to overcome. Practical aspects of data collection are discussed pointing to a need for a culture change in clinical practice to one that captures structured patient-related data in routine care in a prospective manner. Radiation dosimetry and the contoured anatomy must also be captured routinely to represent the best estimate of delivered radiation. The quality and integrity present in the data are critical which poses opportunities to introduce electronic validity checking to improve them. Similarly, data completeness and methods and technology to improve the efficiency and sufficiency of data capture can be introduced. In the manuscript, the types of clinical data are discussed including patient reports, images, biospecimens, treatments, and symptom management. With a data-driven culture, the realization of a learning health system is possible unlocking the potential of big data and its influence on clinical decision-making and hypothesis generation.

Original languageEnglish (US)
Pages (from-to)e863-e869
JournalMedical Physics
Issue number10
StatePublished - Oct 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 American Association of Physicists in Medicine


  • big data
  • decision support
  • learning health system
  • machine learning


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