Objective To access the diagnostic performance of CT texture analysis to differentiate atypical pancreatic solid pseudopapillary tumor (SPT) from pancreatic ductal adenocarcinoma (PDAC). Methods CT images of 26 patients with pathologically proved atypical SPT and 52 patients with PDAC were analyzed. 3D regions of interest (ROIs) on arterial phase (AP) and portal venous phase (PVP) images were drawn by ITK-Snap software. A.K. software (GE company, USA) was used to extract texture features for the discrimination of atypical SPT and PDAC. After removing redundancy (by a correlation analysis through R software), texture features were selected by single-factor and multi-factor logistic regression, and logistic regression model was finally established. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance of single texture feature and logistic model. Results A total of 792 texture features ［396 of AP, 396 of PVP］ from AP and PVP CT images were obtained by A.K. software. Of these, 61 texture features (35 of AP, 26 of PVP) were selected by R software (result of correlation analysis showed that correlation coefficient >0.7). Two texture features, including MinIntensity and Correlation_AllDirection_offset1_SD, were selected to establish logistic model. The sensitivity and specificity of these 2 texture features were 71.15% and 76.92%, 63.46% and 76.92% respectively, the area under curve ( AUC) were 0.740 and 0.754 respectively. The model’s sensitivity and specificity were 73.08% and 80.77% respectively, the AUC value was 0.796. There was no significance among the model, MinIntensity, and Correlation_AllDirection_offset1_SD (P>0.05). Conclusions CT texture analysis of 3D ROI is a quantitative method for differential diagnosis of atypical SPT from PDAC.