Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration

Gastric cancer (GC) exhibits substantial molecular heterogeneity, necessitating precision oncology approaches. Conventional biomarker assessments, including immunohistochemistry, in situ hybridization, polymerase chain reaction, and next-generation sequencing, are the diagnostic standard but resource-intensive and may be limited by tissue availability, cost, and turnaround time. Artificial intelligence (AI)-enabled computational pathology using whole-slide images (WSIs) has emerged as a scalable approach for inferring molecular and immune phenotypes directly from routine hematoxylin and eosin-stained histopathological images.

This review synthesizes recent advances in AI-based WSI analysis for biomarker assessment in GC, focusing on molecular subtype prediction, actionable genetic alterations, immune-related features, tumor microenvironment characterization, and multimodal integration.

AI models have demonstrated promising performance in predicting microsatellite instability and Epstein-Barr virus status, supporting their potential use as prescreening or triage tools to prioritize confirmatory testing. In addition, these approaches enable the quantitative characterization of the tumor microenvironment by mapping tertiary lymphoid structures and immune architecture, providing prognostic insights. Multimodal integration of histopathology with radiologic, genomic, and clinical data has shown improved predictive performance compared to single-modality approaches, particularly for recurrence and treatment responses.

However, challenges remain, including model interpretability, variability in performance across different datasets, and incomplete data across modalities. Future directions include prospective multicenter validation in clinical workflows, standardization of evaluation frameworks, and implementation of uncertainty estimation to support clinical decision-making. Overall, AI-enabled digital pathology represents a promising approach for advancing precision oncology in GC by improving biomarker assessment and providing insights into tumor biology.