2024 Fiscal Year Final Research Report
Prediction Model for Tumor Immune Activation and Radiotherapy Effectiveness Using Machine Learning
| Project/Area Number |
22K07671
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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 | Sapporo Medical University |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Keywords | 放射線治療 / 腫瘍免疫 |
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| Outline of Final Research Achievements |
Immunohistochemical staining for CD8/FoxP3-positive cells was completed, and cases of preoperative radiation therapy for cervical cancer with available treatment outcome data were used to compare quantitative evaluation using specialized software with conventional visual assessment. The study demonstrated no significant differences between the two methods and explored the potential superiority of software-based assessment.Similarly, using cases of oropharyngeal cancer with completed CD8 immunohistochemical staining and available treatment outcome data, we evaluated the validity of software-based assessment. In both cervical cancer and oropharyngeal cancer, quantitative evaluation using the specialized software showed no significant differences compared to conventional visual assessment. Additionally, the software demonstrated the potential for faster analysis.
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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 |
本研究は、放射線治療を受けた子宮頸癌および中咽頭癌の症例において、CD8/FoxP3陽性細胞の免疫組織染色を専用ソフトで定量解析し、従来の目視判定と同等の妥当性を持つこと、かつ短時間での解析が可能であることを示した。これにより、放射線治療後の免疫環境の客観的評価が可能となり、病理診断の効率化や標準化に加え、免疫反応に基づく個別化放射線治療の推進にも貢献する学術的・社会的意義がある。
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