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

ABSTRACT:

Objective

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.

Methods

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.

Results

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 fo

SUBMITTER: Li Y 

PROVIDER: S-EPMC9433672 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature