Abstract Objectives To develop and validate a deep learning radiopathomics model (DLRPM) integrating radiological and pathological imaging d
Abstract Objectives To develop and validate a deep learning radiopathomics model (DLRPM) integrating radiological and pathological imaging data to predict biliary cyclooxygenase-2 (COX-2) expression in children with pancreaticobiliary maljunction (PBM), and to compare its performance with single-modality radiomics, deep learning radiomics (DLR), and pathomics models. Methods This retrospective study included 219 PBM patients, divided into a training set (n = 104; median age, 2.8 years, 75.0% females) and internal test set (n = 71; median age, 2.2 years, 83.1% females) from center I, and an external test set (n = 44; median age, 3.4 years, 65.9% females) from center II. Biliary COX-2 expression was detected using immunohistochemistry. Radiomics, DLR, and pathomics features were extracted from portal venous-phase CT images and H&E-stained histopathological slides, respectively, to build individual single-modality models. These were then integrated to develop the DLRPM, combining three predictive signatures. Model performance was evaluated using AUC, net reclassification index (NRI, for assessing improvement in correct classification) and integrated discrimination improvement (IDI). Results The DLRPM demonstrated the highest performance, with AUCs of 0.851 (95% CI, 0.759–0.942) in internal test set and 0.841 (95% CI, 0.721–0.960) in external test set. In comparison, AUCs for the radiomics, DLR, and pathomics models were 0.532–0.602, 0.658–0.660, and 0.787–0.805, respectively. The DLRPM significantly outperformed three single-modality models, as demonstrated by the NRI and IDI tests (all p
