Machine Learning and Radiogenomics: Lessons Learned and Future Directions

@article{Kang2018MachineLA,
  title={Machine Learning and Radiogenomics: Lessons Learned and Future Directions},
  author={John Kang and Tiziana Rancati and Sangkyun Lee and Jung Hun Oh and Sarah L. Kerns and Jacob G. Scott and Russell Schwartz and Seyoung Kim and Barry S. Rosenstein},
  journal={Frontiers in Oncology},
  year={2018},
  volume={8}
}
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 (ML) models due to the enormous amount of diagnostic data used as input and therapeutic data generated as output. An emerging field in precision radiation oncology that can take advantage of ML… 

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