Department 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.
Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
Expert Rev Anticancer Ther. 2024 Jan-Feb;24(1-2):21-28. doi: 10.1080/14737140.2024.2311684. Epub 2024 Feb 12.
Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making.
Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023).
An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.
免疫疗法是癌症治疗的重大突破之一，已成为一种强大的临床策略，但并非所有患者对免疫检查点阻断和其他免疫疗法策略都有反应。应用机器学习 (ML) 技术预测癌症免疫疗法的疗效有助于临床决策。
将 ML（包括深度学习 (DL)）应用于放射组学、病理学、肿瘤微环境 (TME) 和免疫相关基因分析，以预测免疫疗法的疗效。本综述中的研究从 PubMed 和 ClinicalTrials.gov 中检索（2023 年 1 月）。
越来越多的研究表明，ML 已应用于肿瘤学研究的各个方面，有可能提供更有效的个体化免疫治疗策略，并增强治疗决策。随着 ML 技术的进步，未来可能会出现更有效的免疫疗法疗效预测方法。
