Feature value analysis of delivered dose distribution contributing to individualized radiotherapy
Project/Area Number |
18K07667
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 52040:Radiological sciences-related
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Research Institution | The University of Tokyo |
Principal Investigator |
Imae Toshikazu 東京大学, 医学部附属病院, 副診療放射線技師長 (80420222)
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Co-Investigator(Kenkyū-buntansha) |
芳賀 昭弘 徳島大学, 大学院医歯薬学研究部(医学域), 教授 (30448021)
山下 英臣 東京大学, 医学部附属病院, 准教授 (70447407)
高橋 渉 東京大学, 医学部附属病院, 助教 (50755668)
鈴木 雄一 東京大学, 医学部附属病院, 副診療放射線技師長 (70420221)
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Project Period (FY) |
2018-04-01 – 2023-03-31
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Project Status |
Completed (Fiscal Year 2022)
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Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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Keywords | 放射線治療 / 個別化 / 投与線量分布 / 特徴量 / 形態変化 |
Outline of Final Research Achievements |
This study aimed to reconstruct the dose distribution that reflects the patient's shape and the device movement during treatment, and to clarify the feature value that causes the error between the dose distribution and the treatment plan. We created the delivered dose distribution using non-rigid registration and deep learning approaches. In addition, we extracted feature values for the therapeutic and side effects of stereotactic body radiation therapy using radiomics analysis and clarified that specific feature values indicate clinical usefulness.
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Academic Significance and Societal Importance of the Research Achievements |
本研究では,非剛体レジストレーションを用いた投与線量分布の作成や深層学習を用いた画質改善法の提案,体幹部定位放射線治療における治療効果と副作用,また,レディオミクス解析を用いて抽出した特徴量の臨床的有用性を示した.治療効果や副作用に直結する投与線量分布の作成や投与線量分布と治療計画の誤差要因の明確化,また,治療後の患者に対する詳細な解析結果は,今後の放射線治療の個別化や最適化に貢献する.
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Report
(6 results)
Research Products
(16 results)
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[Journal Article] Improvement in Image Quality of CBCT during Treatment by Cycle Generative Adversarial Network2020
Author(s)
今江 禄一, 鍛冶 静雄, 木田 智士, 松田 佳奈子, 竹中 重治, 青木 淳, 仲本 宗泰, 尾崎 翔, 名和 要武, 山下 英臣, 中川 恵一, 阿部 修.
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Journal Title
Japanese Journal of Radiological Technology
Volume: 76
Issue: 11
Pages: 1173-1184
DOI
NAID
ISSN
0369-4305, 1881-4883
Related Report
Peer Reviewed / Open Access
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[Journal Article] Salvage stereotactic body radiotherapy for post?operative oligo?recurrence of non?small cell lung cancer: A single?institution analysis of 59?patients2020
Author(s)
Aoki S, Yamashita H, Takahashi W, Nawa K, Ota T, Imae T, Ozaki S, Nozawa Y, Nakajima J, Sato M, Anraku M, Nitadori J, Karasaki T, Abe O, Nakagawa K.
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Journal Title
Oncology Letters
Volume: 19(4)
Pages: 2695-2704
DOI
Related Report
Peer Reviewed / Open Access
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[Presentation] Pseudo-CBCT Image Prediction of Head and Neck Cancer Patient Using Principal Component Vector Fields of Early Treatment Fractions.2019
Author(s)
M Nakano, T Imae, T Nakamoto, A Haga, K Nawa, Y Nomura, R Chhatkuli, K Demachi, W Takahashi, K Yamamoto, K Nakagawa, M Hashimoto, Y Yoshioka, M Oguchi,
Organizer
American Association of Physicists in Medicine 61st Annual Meeting and Exhibition
Related Report
Int'l Joint Research
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