Pathomics Signature for Prognosis and CA19-9 Interception in Pancreatic Ductal Adenocarcinoma: A Real-Life,...

Histopathological hematoxylin and eosin (H&E) slides contain valuable prognostic information for pancreatic ductal adenocarcinoma (PDAC), yet systematic feature extraction remains challenging. This multi-center study developed and validated an automated prognostic model using deep learning on digitized whole-slide images from 873 PDAC patients with surgical resection across three academic centers. The CrossFormer architecture achieved superior performance in external validation (area under the curve [AUC] = 0.774), significantly outperforming ResNet-18 (AUC = 0.716), ResNet-50 (AUC = 0.737), and DenseNet-121 (AUC = 0.729). Gradient-weighted Class Activation Mapping identified key prognostic features including desmoplastic stroma, high nuclear-to-cytoplasmic ratio, tumor necrosis, and immune cell infiltration. The pathomics signature effectively stratified patients into low-risk and high-risk groups with significant survival differences (p < 0.001). Critically, carbohydrate antigen 19-9 (CA19-9) retained prognostic value only in low-risk patients (hazard ratio [HR] = 2.70, p < 0.001) but not in high-risk patients (HR = 0.998, p = 0.990). High-risk patients derived substantial benefit from adjuvant chemotherapy (HR = 0.56, p = 0.038), whereas low-risk patients showed no significant benefit (HR = 0.83, p = 0.562). These findings provide actionable clinical insights: treatment intensification for high-risk patients and CA19-9-guided monitoring for low-risk patients. This validated, interpretable model transforms routine H&E slides into quantitative prognostic tools, enabling personalized treatment strategies without additional testing costs.