Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications

Clinical Snapshot

Key Findings

• P Value: Not reported in systematic review format

• Effect Size: CNNs achieved sensitivities up to 95.3% for early gastric cancer detection

• Primary Outcome: Diagnostic accuracy of AI models in gastric cancer detection and management

• Nnt Or Sensitivity: Sensitivity up to 95.3% for early detection; AUCs ranging 0.825-0.981 across applications

• Confidence Interval: Not consistently reported across studies

Clinical Application

Limited by need for specialized infrastructure, training, and regulatory approval for clinical implementation Relevant to Australian gastroenterology practice; would require TGA approval for clinical use and integration with existing endoscopy and radiology workflows Patients undergoing gastric cancer screening, diagnosis, or treatment planning in tertiary care settings

Abstract

BACKGROUND: Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of cancer-related mortality worldwide. Although early detection can increase the 5-year survival rate of early gastric cancer (EGC) to over 90%, more than 80% of cases are diagnosed at advanced stages due to subtle clinical symptoms and diagnostic challenges. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown great promise in addressing these limitations. OBJECTIVES: This systematic review aims to evaluate the performance, applications, and limitations of ML and DL models in GC management, with a focus on their use in detection, diagnosis, treatment planning, and prognosis prediction across diverse clinical imaging and data modalities. METHODS: Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus for studies published between 2004 and May 2025. Eligible studies applied ML or DL algorithms for diagnostic or prognostic tasks in GC using data from endoscopy, computed tomography (CT), pathology, or multi-modal sources. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. RESULTS: A total of 59 studies met the inclusion criteria. DL models, particularly convolutional neural networks (CNNs), demonstrated strong performance in EGC detection, with reported sensitivities up to 95.3% and Area Under the Curve (AUCs) as high as 0.981, often exceeding expert endoscopists. CT-based radiomics and DL models achieved AUCs ranging from 0.825 to 0.972 for tumor staging and metastasis prediction. Pathology-based models reported accuracies up to 100% for EGC detection and AUCs up to 0.92 for predicting treatment response. Cross-modality approaches combining radiomics and pathomics achieved AUCs up to 0.951. Key challenges included algorithmic bias, limited dataset diversity, interpretability issues, and barriers to clinical integration. CONCLUSION: ML and DL models have demonstrated substantial potential to improve early detection, diagnostic accuracy, and individualized treatment in GC. To advance clinical adoption, future research should prioritize the development of large, diverse datasets, implement explainable AI frameworks, and conduct prospective clinical trials. These efforts will be essential for integrating AI into precision oncology and addressing the increasing global burden of gastric cancer.

References

1. 1.Alsallal, M., Habeeb, M. S., Vaghela, K., Malathi, H., Vashisht, A., Sahu, P. K., Singh, D., Al-Hussainy, A. F., Aljanaby, I. A., Sameer, H. N., Athab, Z. H., Adil, M., Yaseen, A., & Farhood, B. (2026). Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications. Abdominal Radiology. https://doi.org/10.1186/s13020-025-01059-4