Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

作者全名：Li, Juan; Dan, Kena; Ai, Jun

作者地址：[Li, Juan] Chongqing Dangdai Plast Surg Hosp, Dept Dermatol, Chongqing, Peoples R China; [Dan, Kena] Chongqing Med Univ, Affiliated Hosp 3, Dept Dermatol, Chongqing, Peoples R China; [Ai, Jun] Chongqing Huamei Plast Surg Hosp, Dept Dermatol, Chongqing, Peoples R China

通信作者：Ai, J (通讯作者)，Chongqing Huamei Plast Surg Hosp, Dept Dermatol, Chongqing, Peoples R China.

来源：FRONTIERS IN IMMUNOLOGY

ESI学科分类：IMMUNOLOGY

WOS号：WOS:001237751000001

JCR分区：Q1

影响因子：5.7

年份：2024

卷号：15

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：machine learning; prediction; melanoma; immune checkpoint inhibitor; meta-analysis

摘要：Background The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy. Methods Relevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0. Results A total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0.792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721-0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and validation sets, respectively. Conclusion Machine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

基金机构：　

基金资助正文：