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

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). Results: The initial search identified 520 articles, and 22 studies that met the inclusion criteria were finally included. The results show that DL models have performed excellently in GC detection, histological classification, and prognosis prediction. Some models even reached an accuracy of over 95% in GC detection. Convolutional neural networks (CNN) are the most commonly used DL models. However, current studies still have limitations, such as limited dataset size, lack of external validation, and insufficient data diversity. The applicability to different types and stages of GC is also unclear. Conclusions: Future research must build larger, more diverse, and more representative datasets. These should cover a wider range of GC types and stages, and undergo rigorous clinical validation. This will help fully realize the potential of DL in GC pathology image analysis and ultimately improve clinical practice.