Radiomics based on multiphasic contrast-enhanced CT combined with clinical features for differentiating hypovascular pancreatic neuroendocrine tumors and pancreatic ductal adenocarcinoma

To evaluate the value of a combined model based on multiphase contrast-enhanced CT radiomics and clinical features for differentiating hypovascular pancreatic neuroendocrine tumors (hypo-PNETs) from pancreatic ductal adenocarcinoma (PDAC). A total of 297 patients with pathologically confirmed pancreatic tumors, including 99 hypo-PNETs and 198 PDACs, were retrospectively enrolled. Radiomics features were extracted from non-contrast, arterial-phase, venous-phase, and combined three-phase CT images. After feature selection, radiomics models were established using multiple machine-learning classifiers. Independent clinical predictors were identified by logistic regression and integrated with the optimal radiomics signature to construct a combined model. Model performance was assessed using receiver operating characteristic analysis, calibration curves, decision curve analysis, net reclassification improvement, and integrated discrimination improvement. Pancreatic duct dilatation, tumor composition, age, and maximum tumor diameter were identified as independent predictors. Among the radiomics models, the combined three-phase SVM model achieved the best performance, with AUCs of 0.814 and 0.812 in the training and test sets, respectively. The combined model yielded the highest AUCs (0.886 in the training set and 0.849 in the test set); however, because several between-model comparisons in the test set did not reach statistical significance, its advantage should be interpreted as a potential incremental benefit rather than definitive superiority. A combined model integrating multiphasic CT radiomics and clinical features showed promising performance for differentiating hypo-PNETs from PDAC. This model may provide complementary support for preoperative diagnosis, although its incremental value over the radiomics-only model requires further validation.