書誌事項
- タイトル別名
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- Applications of Machine Learning for Radiation Therapy
- 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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説明
<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>
収録刊行物
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- 医学物理
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医学物理 36 (1), 35-38, 2016
公益社団法人 日本医学物理学会
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詳細情報 詳細情報について
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- CRID
- 1390282680485608704
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- NII論文ID
- 130005172377
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- NII書誌ID
- AA11580542
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- ISSN
- 21869634
- 13455354
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- NDL書誌ID
- 027549892
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- PubMed
- 28428495
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDLサーチ
- PubMed
- CiNii Articles
- KAKEN
- OpenAIRE
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- 抄録ライセンスフラグ
- 使用不可