Development of estimation system of dosimetric QA for volumetric modulated arc therapy
| Project/Area Number |
18K15545
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Kyoto University |
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Principal Investigator |
Ono Tomohiro 京都大学, 医学研究科, 特定助教 (90782657)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | VMAT / 機械学習 / 最適化 / QA予測 / dosiomics / QA結果予測 / 回帰木 / 重回帰分析 / ニューラルネットワーク / 医学物理学 / 強度変調放射線治療 / QA |
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| Outline of Final Research Achievements |
QA result prediction was developed for VMAT using machine learning and the system was structed. In accordance with the research protocol, following three research were performed; “1. Analysis of features of plan information”, “2. Development of QA result prediction model via machine learning” and “3. Prospective evaluation of prediction model accuracy and structing the system”.
The model was constructed using more than 1000 actual clinical cases in our hospital, and it was found that the prediction accuracy could be improved by combining the information on plan and dose information.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究成果により、高精度放射線治療の品質管理の新たな手法として学術論文誌に報告を行い、高い評価を得た。患者プランのQA結果の予測を可能とするため、新たなQA手法としての確立に貢献するものと考える。また本手法を用いることで高精度放射線治療プランの立案段階で予めQA結果の予測が可能となるため、照射プラン形成へのフィードバックへの応用も期待される。本手法により治療計画の効率化、さらには患者へのより良い照射プランの提供が可能となると考えられ、学術的・社会的にも意義がある。
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Report
(4 results)
Research Products
(7 results)
