Objective To determine feasibility of texture analysis of CT images for the discrimination of nonhypervascular pancreatic neuroendocrine tumor (PNET) from pancreatic ductal adenocarcinoma (PDAC). Methods CT images of 15 pathologically proved as PNETs and 30 PDACs in West China Hospital of Sichuan University from January 2009 to January 2017 were retrospectively analyzed. Results Thirty best texture parameters were automatically selected by the combination of Fisher coefficient (Fisher)+classification error probability combined with average correlation coefficients (PA)+mutual information (MI). The 30 texture parameters of arterial phase (AP) CT images were distributed in co-occurrence matrix (18 parameters), run-length matrix (10 parameters), and autoregressive model (2 parameters). The distribution of parameters in portal venous phase (PVP) were co-occurrence matrix (15 parameters), run-length matrix (10 parameters), histogram (1 parameter), absolute gradient (1 parameter), and autoregressive model (3 parameters). In AP and PVP, the parameter with the highest diagnostic performance were both Teta2, and the area under curve (AUC) value was 0.829 and 0.740 (P<0.001,P=0.009), respectively. By the B11 of MaZda, the misclassification rate of raw data analysis (RDA)/K nearest neighbor classification (KNN), principal component analysis (PCA)/KNN, linear discriminant analysis (LDA)/KNN, and nonlinear discriminant analysis (NDA)/artificial neural network (ANN) was 28.89% (13/45), 28.89% (13/45), 0 (0/45), and 4.44% (2/45), respectively. In PVP, the misclassification rate of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN was 35.56% (16/45), 33.33% (15/45), 4.44% (2/45), and 11.11% (5/45), respectively. Conclusions CT texture analysis is feasible in the discrimination of nonhypervascular PNET and PDAC. Teta2 is the parameter with the highest diagnostic performance, and in AP, LDA/KNN modality has the lowest misclassification rate.