Integrating Radiomics and Computational Pathology to Predict Early Recurrence of Pancreatic Ductal Adenocarcinoma and Uncover Its Biological Basis in Tumor Microenvironment

BACKGROUND: Accurate prediction of early recurrence (ER) after radical resection remains a critical challenge in pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop and validate an integrated radiomic-pathology (Rad-Path) model for ER prediction and to elucidate its underlying biological mechanisms. METHODS: A retrospective cohort of 225 PDAC patients who underwent R0 resection was included. Preoperative CT images and whole-slide images (WSI) were collected for the extraction of radiomic features and computational pathology features. Selected features were used to develop 11 distinct machine learning models. The SHapley Additive exPlanations (SHAP) algorithm was employed to evaluate feature importance. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) were performed on prospectively collected specimens. RESULTS: The final Rad-Path model achieved AUCs of 0.851 and 0.814 in the internal and external validation cohorts, respectively. The predicted ER group was specifically linked to the enrichment of fibroblasts and pancreatic stellate cells, as well as dysregulation in extracellular matrix (ECM)-related pathways. This finding was validated histopathologically, as predicted ER patients predominantly displayed a "reactive-dominant" phenotype marked by abundant activated fibroblasts and ECM deposition. CONCLUSION: Our study offers a high-performance predictive model for ER in PDAC and establishes ECM remodeling as a key biological mechanism underlying the predictions.