P-MES: Explainable pathology-based distant metastasis risk stratification in locally advanced nasopharyngeal carcinoma.

Background: Despite substantial therapeutic progress, distant metastasis (DM) persists as the leading cause of treatment failure and a critical driver of poor survival in nasopharyngeal carcinoma (NPC). Existing prognostic tools remain limited in accuracy and clinical utility, underscoring the need for more robust and actionable biomarkers. The convergence of artificial intelligence (AI) and digital pathology offers a promising avenue by enabling the extraction of prognostic signatures directly from routinely available histopathology. In this study, we developed and validated a deep learning (DL) model based on whole slide images (WSIs) to predict post-treatment DM risk in patients with locally advanced NPC. Methods: We developed the Pathology-based distant metastasis risk stratification model (P-MES) using WSIs from 147 NPC patients. WSIs were encoded into patch-level embeddings using the pretrained UNI feature extractor, followed by multiple instance learning (MIL) aggregation to generate slide-level representations for predicting DM. To enhance interpretability, attention-based heatmaps were generated, and complementary morphologic features extracted by CellProfiler were used to elucidate histopathologic patterns associated with metastatic risk. Results: P-MES achieved excellent predictive performance in the training, validation, and internal test sets (AUCs: 0.990, 0.887, and 0.949), demonstrating substantial discrimination, calibration, and clinical net benefit. CellProfiler analysis further identified ten morphology-derived features associated with DM. Conclusion: P-MES is an automated, pathology-based framework for predicting DM in NPC, providing pathological insights and supporting personalized clinical management.