Development of radiation therapy support system based on radiomics
Project/Area Number |
18K15604
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Research Category |
Grant-in-Aid for Early-Career Scientists
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Allocation Type | Multi-year Fund |
Review Section |
Basic Section 52040:Radiological sciences-related
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Research Institution | Komazawa University |
Principal Investigator |
Magome Taiki 駒澤大学, 医療健康科学部, 准教授 (60725977)
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Project Period (FY) |
2018-04-01 – 2022-03-31
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Project Status |
Completed (Fiscal Year 2021)
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Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2021: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000)
Fiscal Year 2020: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2019: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2018: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
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Keywords | 機械学習 / 深層学習 / 放射線治療 / 個別化治療 / 最適化 / Radiomics / 前立腺癌 / 頭頸部癌 / 線量分布 / 逐次近似再構成 / 事前情報 / ビッグデータ / 医学物理 |
Outline of Final Research Achievements |
The purpose of this study was to develop a radiotherapy support system that combines image features calculated from medical images and artificial intelligence technology to provide various types of support in the field of radiotherapy. In the current radiotherapy, the same prescribed dose is often administered to all patients once the stage of disease is determined. However, there is a possibility to determine the optimal prescribed dose for each patient by integrating and analyzing medical images and other various patient information. In this study, we developed an image generation method to calculate robust image features and a prediction method for patient prognosis based on radiomics and artificial intelligence techniques for various diseases.
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Academic Significance and Societal Importance of the Research Achievements |
現在の放射線治療では、病期等が決まれば全ての患者に同一の処方線量が投与される場合が多い。しかし、医用画像やその他の様々な患者情報を統合して解析することで、患者個別に最適な処方線量等を決定できる可能性がある。本研究の成果は、個別化医療実現のための支援システムとして利用できる可能性がある。また、提案システムを用いることで、実際の臨床試験を行う前に試験の成功率を予測できる可能性がある。
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Report
(5 results)
Research Products
(69 results)
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[Journal Article] Fast Statistical Iterative Reconstruction for Mega-voltage Computed Tomography2020
Author(s)
S. Ozaki, A. Haga, E. Chao, C. Maurer, K. Nawa, T. Ohta, T. Nakamoto, Y. Nozawa, T. Magome, M. Nakano, K. Nakagawa
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Journal Title
The Journal of Medical Investigation
Volume: 67
Issue: 1.2
Pages: 30-39
DOI
NAID
ISSN
1343-1420, 1349-6867
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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[Presentation] Field-of-view expansion of megavoltage CT based on iterative reconstruction algorithm using information of treatment planning kV-CT2018
Author(s)
Yuki Watanabe, Taiki Magome, Akihiro Haga, Kanabu Nawa, Masahiro Nakano, Yukihiro Nomura, Shohei Hanaoka, Keiichi Nakagawa, Darren Zuro, Chunhui Han, Jeffrey Wong, Susanta Hui
Organizer
AAPM 2018
Related Report
Int'l Joint Research
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