Department of Dermatology, Chongqing Dangdai Plastic Surgery Hospital, Chongqing, China.
Department of Dermatology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Immunol. 2024 May 21;15:1281940. doi: 10.3389/fimmu.2024.1281940. eCollection 2024.
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.
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.
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.
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.
免疫疗法的出现改变了黑色素瘤的治疗方式，延长了许多患者的生存期。然而，仍有少数患者对免疫疗法无反应，并且仍然缺乏有效的工具来早期识别此类患者人群。研究人员已经开发出用于预测黑色素瘤免疫治疗反应的机器学习算法，但它们的预测准确性一直不一致。因此，进行了本次系统评价和荟萃分析，以全面评估机器学习在黑色素瘤免疫治疗反应预测中的准确性。
从建库至 2022 年 7 月 30 日，在 PubMed、Web of Sciences、Cochrane Library 和 Embase 中检索相关研究。使用预测模型风险偏倚评估工具（PROBAST）评估纳入研究的偏倚风险和适用性。Meta 分析在 R4.2.0 上进行。
共纳入 36 项研究，包括 30 项队列研究和 6 项病例对照研究。这些研究主要发表于 2019 年至 2022 年期间，共涉及 75 个模型。本研究的结局指标为无进展生存期（PFS）、总生存期（OS）和治疗反应。在训练集中，PFS 的合并 C 指数为 0.728（95%CI：0.629-0.828），在训练集和验证集中，治疗反应的 C 指数分别为 0.760（95%CI：0.728-0.792）和 0.819（95%CI：0.757-0.880），在训练集和验证集中，OS 的 C 指数分别为 0.746（95%CI：0.721-0.771）和 0.700（95%CI：0.677-0.724）。
机器学习在黑色素瘤免疫治疗反应和预后方面具有相当高的预测准确性，特别是在前者方面。然而，由于缺乏外部验证以及某些类型模型的稀缺性，还需要进一步的研究。
