Machine Learning and Radiogenomics: Lessons Learned and Future Directions

Machine Learning and Radiogenomics: Lessons Learned and Future Directions

Due to the rapid increase in the availability of patient data, there is significant interest in precision medicine that could facilitate the development of a personalized treatment plan for each patient on an individual basis. Radiation oncology is particularly suited for predictive machine learning...

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Bibliographic Details
Main Authors: John Kang, Tiziana Rancati, Sangkyu Lee, Jung Hun Oh, Sarah L. Kerns, Jacob G. Scott, Russell Schwartz, Seyoung Kim, Barry S. Rosenstein
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-06-01
Series:Frontiers in Oncology
Subjects:
statistical genetics and genomics
radiation oncology
computational genomics
precision oncology
machine learning in radiation oncology
big data
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2018.00228/full

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https://www.frontiersin.org/article/10.3389/fonc.2018.00228/full

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