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The objective of this study was to investigate whether computed tomography texture analysis can be used to differentiate papillary renal cell carcinoma (PRCC) subtypes. Sixty-two PRCC tumors were retrospectively evaluated, with 30 type 1 tumors and 32 type 2 tumors. Texture parameters quantified from three-phase contrast-enhanced CT images were compared with least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for each parameter. The selected texture parameters of each phase were used to generate support vector machine (SVM) classifiers. Decision curve analysis (DCA) of the classification was performed. The two texture parameters with the top two AUC values were – 333–7 Correlation (AUC = 0.772) and 45–7 Entropy (AUC = 0.753) in the corticomedullary phase, 333–4 Correlation (AUC = 0.832) and 45–7 Entropy (AUC = 0.841) in the nephrographic phase, and 135–7 Entropy (AUC = 0.858) and – 333–1 InformationMeasureCorr2 (AUC = 0.849) in the excretory phase. Entropy and Correlation have a high correlation with the two types of PRCC and are increased in type 2 PRCC. A model incorporating the texture parameters with the top two AUC values in each phase produced an AUC of 0.922 with an accuracy of 84% (sensitivity = 89% and specificity = 80%). The nephrographic-phase model and the model combining the texture parameters of the three phases can differentiate the two types with the largest net benefit. Computed tomography texture analysis can be used to distinguish type 2 PRCC from type 1 with high accuracy, which may be clinically important.