Applications of Machine Learning for Radiation Therapy

Hidetaka Arimura, Takahiro Nakamoto

研究成果: ジャーナルへの寄稿評論記事

1 引用 (Scopus)

抄録

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.

元の言語英語
ページ(範囲)35-38
ページ数4
ジャーナルIgaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics
36
発行部数1
DOI
出版物ステータス出版済み - 1 1 2016

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Radiotherapy
Technology
Image-Guided Radiotherapy
Radiation Pneumonitis
Esophageal Stenosis
Tumor Burden
Positron-Emission Tomography
Machine Learning
Radiation
Research
Therapeutics

All Science Journal Classification (ASJC) codes

  • Medicine(all)

これを引用

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