Development of the prognosis prediction model in patients with head and neck cancer using tumor biological characteristics-reflected MRI data and the artificial intelligence-based analysis
| Project/Area Number |
18K07661
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Hokkaido University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
本間 明宏 北海道大学, 医学研究院, 教授 (30312359)
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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 |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2018: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | MRI / 人工知能 / 頭頸部癌 / 頭頚部癌 / 予後予測 / 画像診断 / 頭頚部扁平上皮癌 |
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| Outline of Final Research Achievements |
Firstly, we tried to depict the tumor biological characteristics which related to patient's prognosis using diffusion and perfusion-based MR technique in head and neck cancer. We successfully visualized the tumor growth rate, tumor perfusion and the presence of hypoxic area as tumor functional information. Next, we developed the prognosis prediction model in patients with head and neck cancer using the abovementioned MR-based tumor functional information. Machine learning technique was selected for the development of this diagnostic model. After the optimization of hyperparameters in machine learning model, high diagnostic accuracy to predict patient's treatment outcome could be successfully accomplished.
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| Academic Significance and Societal Importance of the Research Achievements |
本検討は非侵襲的な画像化が難しいとされていた腫瘍の生物学的性状を反映した画像情報を、非造影のMRI技術である動脈スピン標識法および多数のb値を用いた拡散強調像のみで画像化することに成功した。また、それらの腫瘍の機能的情報を含んだ画像情報に機械学習を基本とした解析技術を融合させることによって、頭頸部癌患者の予後予測を高い正診率にて施行することが可能であることを示した。これらの技術によって頭頸部癌患者が有する個々の腫瘍に対して精度の高い治療効果予測、患者に対して予後予測が可能であることが示唆され、頭頸部癌患者のいわゆる個別化医療のための判断材料となりえることが示された。
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Report
(4 results)
Research Products
(12 results)
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[Journal Article] Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study.2019
Author(s)
Fujima N, Shimizu Y, Yoshida D, Kano S, Mizumachi T, Homma A, Yasuda K, Onimaru R, Sakai O, Kudo K, Shirato H
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Journal Title
Cancers
Volume: 11
Issue: 6
Pages: 800-800
DOI
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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