2023 Fiscal Year Final Research Report
Study on selection of optimal treatment strategy for head and neck cancer radiotherapy using clinical images and artificial intelligence
| Project/Area Number |
22K15799
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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 | University of Tsukuba |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2024-03-31
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| Keywords | 放射線治療 / 人工知能 / 頭頸部癌 / 子宮頸癌 |
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| Outline of Final Research Achievements |
In cases of head and neck cancer that underwent radiation therapy, image features at the lesion site were extracted using radiomics, and the correlation with outcomes such as survival time, local control rate, and recurrence-free survival rate was analyzed.
In addition, prognosis was analyzed for the treatment results of proton beam therapy for the nasal cavity and paranasal sinuses by dividing patients into three groups. It was found that the surgery + irradiation group and the group that was resectable but underwent irradiation had good overall survival rates, progression-free survival rates, and local control rates.
In addition, post-treatment prognosis was analyzed for cervical cancer patients using MRI images taken before and during treatment. Prognosis was analyzed by dividing patients into three groups according to the tumor shrinkage rate, and it was found that the greater the shrinkage rate, the better the overall survival rate.
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| Free Research Field |
放射線治療
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| Academic Significance and Societal Importance of the Research Achievements |
頭頸部癌や子宮頸癌の画像的特徴から放射線治療後の予後を層別化し予測することで、新たに治療を行う患者や治療途中の患者において、根治に必要な放射線の線量や照射範囲を患者個々人に合わせて最適化することができる。すなわち、予後が不良の群においては照射線量を上げることで根治性を高め、予後が良好な群では線量を下げたり照射範囲を縮小したりことで周囲の正常臓器に対する有害事象の出現リスクを低減させることができ、放射線治療の治療成績や患者のＱＯＬをより向上させることができると考える。
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