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. 2018 Apr 17:8:110.
doi: 10.3389/fonc.2018.00110. eCollection 2018.

Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

Mary Feng  1 , Gilmer Valdes  1 , Nayha Dixit  1 , Timothy D Solberg  1
Affiliations

Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

Mary Feng et al. Front Oncol. .

Abstract

Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.

Keywords: big data; machine learning; predictive models; process improvement; radiation oncology.

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Figures

Figure 1
Figure 1
Radiotherapy workflow, from consult to follow-up.
See this image and copyright information in PMC

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