Li Yilin, Xie Fengjiao, Xiong Qin, Lei Honglin, Feng Peimin
Department of Gastroenterology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Front Oncol. 2022 Aug 18;12:946038. doi: 10.3389/fonc.2022.946038. eCollection 2022.
To 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.
PubMed, 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.
A 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.
ML 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.
https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752.
评估机器学习（ML）在预测胃癌（GC）患者淋巴结转移（LNM）方面的诊断性能，并确定适用于模型的预测因素。
检索了从创刊至2022年3月16日的PubMed、EMBASE、Web of Science和Cochrane图书馆。采用合并c指数和准确性来评估诊断准确性。基于ML类型进行亚组分析。使用随机效应模型进行荟萃分析。使用PROBAST工具进行偏倚风险评估。
共纳入41项研究（56182例患者），其中33项研究将参与者分为训练集和测试集，其余研究仅有训练集。ML在训练集和测试集中预测LNM的c指数分别为0.837 [95%CI（0.814，0.859）]和0.811 [95%CI（0.785 - 0.838）]。训练集的合并准确性为0.781 [（95%CI（0.756 - 0.805）]，测试集为0.753 [95%CI（0.721 - 0.783）]。不同ML算法和GC分期的亚组分析显示无显著差异。相比之下，在预测因素的亚组分析中，在训练集中，包含放射组学的模型比仅具有临床预测因素的模型具有更高的准确性（F = 3.546，p = 0.037）。此外，肿瘤大小、肿瘤浸润深度和组织学分化是用于预测的模型中最常用的三个特征。
ML在预测GC的LNM方面显示出优异的诊断性能。其中一个涵盖放射组学及其ML算法的模型对GC中LNM的风险显示出良好的准确性。然而，结果揭示了开发过程中的一些方法学局限性。未来的研究应专注于完善和改进现有模型，以提高LNM预测的准确性。
