Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review.

ABSTRACT:

Background

Accurate diagnosis and prognosis stratification of gastric cancer (GC) are crucial for effective treatment. However, traditional histopathological image analysis relies on the subjective judgment of pathologists, which is time-consuming and prone to errors. The emergence of deep learning (DL) models provides new ways to automate and improve the analysis of GC pathology images. This systematic review aims to evaluate the current application, challenges, and future directions of DL in GC pathology image analysis.

Methods

The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines and searched four databases: PubMed, Scopus, Web of Science, and IEEE Xplore (as of June 19, 2025).

Res

SUBMITTER: Xia S 

PROVIDER: S-EPMC12317529 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature