Applications of Machine Learning for Radiation Therapy

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  • ARIMURA Hidetaka
    Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University
  • NAKAMOTO Takahiro
    Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University

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  • 放射線治療における機械学習の応用
  • ホウシャセン チリョウ ニ オケル キカイ ガクシュウ ノ オウヨウ

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<p>Radiation therapy has been highly advanced as image guided radiation therapy (IGRT) by making advantage of image engineering technologies. Recently, novel frameworks based on image engineering technologies as well as machine learning technologies have been studied for sophisticating the radiation therapy. In this review paper, the author introduces several researches of applications of machine learning for radiation therapy. For examples, a method to determine the threshold values for standardized uptake value (SUV) for estimation of gross tumor volume (GTV) in positron emission tomography (PET) images, an approach to estimate the multileaf collimator (MLC) position errors between treatment plans and radiation delivery time, and prediction frameworks for esophageal stenosis and radiation pneumonitis risk after radiation therapy are described. Finally, the author introduces seven issues that one should consider when applying machine learning models to radiation therapy.</p>

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