Foundation model-based gastric cancer staging from complete, uncurated EGD image sequences

Background: Histopathologic assessment remains the gold standard for gastric cancer staging but creates significant treatment bottlenecks. While AI-based prediction of pathologic outcomes from endoscopic images shows promise, existing approaches rely on manually selected images, introducing selection bias and missing the case-level context essential for clinical decisions. We present GastroFM, a vision transformer-based foundation model that learns from complete esophagogastroduodenoscopy (EGD) image sequences, enabling case-level predictions aligned with clinical practice. Methods: GastroFM is pretrained using a modified DINOv3 framework on approximately 500,000 images from 13,515 pathology-confirmed EGD cases at Samsung Medical Center (2019-2023). We then fine-tuned the pretrained model for gastric cancer staging using Attention-based Multiple Instance Learning (AbMIL), which aggregates uncurated, variable-length image sequences (1 to 105 images per case) into case-level predictions. We evaluated the model on gastric cancer staging at multiple granularities using a 75%/10%/15% train-validation-test split (8,121 cases with gastric cancer): (1) early gastric cancer (EGC) vs. advanced gastric cancer (AGC), (2) four-class T-stage (T1-T4), and (3) lymph node metastasis (N0 vs. ≥N1). Results: On the test set, GastroFM achieved: AUC 0.93 (accuracy 0.91) for AGC classification, substantially exceeding expert endoscopic assessment (76.8% accuracy in our cohort); AUC 0.84 (overall accuracy 0.67) for 4-class T-stage classification; and AUC 0.85 (accuracy 0.87) for lymph node metastasis prediction. Conclusion: Unlike conventional approaches that rely on manually selected images, GastroFM analyzes complete, uncurated image sequences, providing case-level predictions aligned with clinical practice workflows. Further comparative studies with existing foundation models and large-scale external validation are necessary to fully establish its superiority and clinical utility in diverse settings.