DataSheet_1_Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis.docx

ObjectiveTo evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models.
MethodsPubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool.
ResultsA total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction.
ConclusionML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction.
Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752

研究目的：本研究旨在评估机器学习（Machine Learning, ML）预测胃癌（Gastric Cancer, GC）患者淋巴结转移（Lymph Node Metastasis, LNM）的诊断效能，并筛选适用于模型的预测因子。
研究方法：本研究检索了PubMed、EMBASE、Web of Science及Cochrane Library数据库，检索时限自建库至2022年3月16日。采用合并c指数与准确度评估诊断效能，基于机器学习算法类型开展亚组分析。使用随机效应模型进行荟萃分析，采用PROBAST工具完成偏倚风险评估。
研究结果：最终纳入41项研究，共计56182例患者；其中33项研究将研究对象划分为训练集与测试集，其余研究仅设置训练集。机器学习预测胃癌淋巴结转移的训练集c指数为0.837 [95%CI(0.814, 0.859)]，测试集c指数为0.811 [95%CI(0.785, 0.838)]。训练集合并准确度为0.781 [95%CI(0.756, 0.805)]，测试集合并准确度为0.753 [95%CI(0.721, 0.783)]。针对不同机器学习算法与胃癌分期的亚组分析未发现显著差异。在预测因子亚组分析中，训练集内纳入放射组学（Radiomics）特征的模型准确度优于仅纳入临床预测因子的模型（F=3.546, P=0.037）。此外，肿瘤大小、癌浸润深度与组织学分化程度是构建预测模型时最常用的三类特征。
研究结论：机器学习在预测胃癌淋巴结转移方面展现出优异的诊断效能。纳入放射组学特征的机器学习模型对胃癌淋巴结转移风险具备良好的预测准确度。但本研究结果也揭示了模型开发过程中存在部分方法学局限。未来研究应聚焦于优化与完善现有模型，以提升胃癌淋巴结转移预测的准确度。
系统评价注册：本系统评价已在PROSPERO平台注册，注册网址为https://www.crd.york.ac.uk/PROSPERO/，注册标识为CRD42022320752。