AUTHOR=Feng Mary , Valdes Gilmer , Dixit Nayha , Solberg Timothy D. 

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

JOURNAL=Frontiers in Oncology

VOLUME=8

YEAR=2018

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2018.00110

DOI=10.3389/fonc.2018.00110

ISSN=2234-943X

ABSTRACT=<p>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.</p>