On the Fuzziness of Machine Learning, Neural Networks, and Artificial Intelligence in Radiation Oncology

Radiation oncology has been hailed as a potential vanguard for guiding Big Data applications into cancer research, quality assessment, and clinical care. This was emphasized by the joint American Society for Radiation Oncology/National Cancer Institute/American Association of Physicists in Medicine workshop held in Bethesda, Maryland in 2015 (1). Radiation therapy provides a unique combination of clinical patient demographics, physical use of radiation, application of image guidance ("radiomics"), and biological markers ("radiogenomics") generated over a treatment period that can span a few days to several weeks.

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