| Project/Area Number |
18K08617
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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 55020:Digestive surgery-related
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| Research Institution | Osaka University |
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Principal Investigator |
SAKAI Daisuke 大阪大学, 医学部附属病院, 特任講師(常勤) (10621071)
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| Co-Investigator(Kenkyū-buntansha) |
石井 秀始 大阪大学, 医学系研究科, 特任教授(常勤) (10280736)
今野 雅允 大阪大学, 医学系研究科, 寄附講座講師 (80618207)
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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 |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 化学療法 / 消化器がん / 人工知能 |
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| Outline of Final Research Achievements |
If a sequence of cancer cells removed by surgery can be performed and the combination of effective anticancer drugs can be determined based on the data, the effect of postoperative chemotherapeutic treatment will increase dramatically. As a result, it is thought that this will lead to improved prognosis. However, at present, it takes a considerable amount of time and effort for humans to analyze sequence data obtained from cancer cells and determine effective anticancer agents, and it is difficult to derive the optimal combination of anticancer agents for individual patients. It's impossible. Therefore, in this study, deep learning was performed using artificial intelligence (AI) from sequence data of about 200 types of cancer cells and susceptibility data of each cell to 265 types of anticancer drugs, and the optimum resistance was obtained from the sequence data. We aimed to build a treatment support system that derives a combination of drugs.
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| Academic Significance and Societal Importance of the Research Achievements |
実装した畳み込みニューラルネットワーク(Convolutional Neural Network :CNN)を用いて最適な抗がん剤の組み合わせの決定を行うことにより、同臨床検体を用いてマウス移植モデル(patient-derived xenograft: PDXモデル)を作成し、人工知能により導き出された効果的抗がん剤の組み合わせで精密医療を目指した基盤を構築することができた。
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