Artificial intelligence-based reclassification of gastric adenocarcinoma enables prognostic stratification via diffuse-type patch proportion

Background: Gastric adenocarcinoma (GAC) remains a major global health burden with marked heterogeneity, complicating diagnosis and prognostic assessment. The Laurén classification, though widely used, suffers from interobserver variability, particularly in defining the mixed subtype. Artificial intelligence (AI)-driven image analysis may improve standardization and prognostic assessment in GAC. Methods: We retrospectively analyzed 404 patients with resected GAC (2015–2022) from Fundeni Clinical Institute. Whole-slide images (WSIs) were annotated by pathologists with expertise and processed into patches for training a two-stage deep learning pipeline based on YOLO26m-cls. The first model (GAC-I) distinguished malignant from non-malignant tissue, while the second (GAC-ST) classified malignant patches as intestinal or diffuse. We developed the diffuse prognostic score (DPS), defined as the proportion of diffuse patches relative to total patches, and correlated it with overall survival (OS). Results: GAC-I and GAC-ST achieved high diagnostic performances, with accuracies of 0.9437 ± 0.0317 (F1 score: 0.9456 ± 0.0243) and 0.8080 ± 0.0833 (F1 score: 0.7528 ± 0.1094). DPS ≥ 0.5 was significantly associated with lower median OS (16.1 months) compared to DPS < 0.5 (42.067 months), association confirmed by multivariate Cox-regression analysis (HR 3.88, p < 0.001) and matched case-control analysis. Groups were balanced across all variables except tumor differentiation, which was more frequently high-grade in DPS ≥ 0.5. After adjustment, DPS ≥ 0.5 remained an independent predictor of mortality (HR 2.684, p = 0.027). Conclusion: We developed and validated a robust AI-based framework for automated GAC classification and prognostic stratification using H&E WSIs. DPS is an independent, reproducible marker of OS, supporting its potential integration into clinical pathology workflows to guide personalized treatment.