癌症免疫疗法的有效性和机器学习
Yuting Fang1,2, Xiaozhong Chen1, Caineng Cao1
1Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China.
Expert review of anticancer therapy
|January 30, 2024
概括
机器学习 (ML) 预测癌症免疫治疗的有效性,帮助临床决策. 这种方法分析了放射学,病理学和瘤微环境数据,以进行个性化治疗.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 免疫治疗是一种免疫疗法.
背景情况:
- 免疫疗法是癌症的关键治疗方法,但患者的反应各不相同.
- 预测免疫疗法疗效对于临床决策至关重要.
研究的目的:
- 审查机器学习 (ML) 在预测癌症免疫疗法的有效性方面的应用.
- 突出ML在个性化癌症治疗策略中的潜力.
主要方法:
- 在PubMed和ClinicalTrials.gov的文献搜索 (截至2023年1月).
- 在放射学,病理学,瘤微环境 (TME) 和与免疫相关的基因分析中分析ML应用.
- 包括深度学习 (DL) 技术.
主要成果:
- 在瘤学研究中,ML越来越多地用于预测治疗结果.
- 机器学习模型分析各种数据类型,包括成像和基因组信息.
- 研究表明,ML有可能增强个性化免疫疗法策略.
结论:
- 机器学习为预测癌症免疫治疗反应提供了有希望的工具.
- 机器学习技术的进一步进步可能会导致更有效的预测方法.
- 在接受免疫治疗的癌症患者中,ML集成可以改善临床决策.
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