Abstract： Objective:To develop and validate a machine learning model based on radiomics features extracted from contrast enhancement-T1 weighted imaging (CE-T1WI) of MR for differentiating between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM).Methods:The clinical and imaging data of 120 patients with pathologically confirmed PCNSL or GBM admitted to the Neurosurgery Center, Beijing Tiantan Hospital, Capital Medical University from January 2020 to December 2021 were collected retrospectively. The patients were randomly divided into the training group ( n=84) and test group ( n=36). The tumor boundary was delineated on the preoperative CE-T1WI MR images with the 3D-Slicer software and the radiomics features were extracted using the Pyradiomics package written in Python. The t-test and LASSO regression were used to screen the best radiomics features to distinguish PCNSL and GBM in the training group and the selected features were used to construct the diagnostic model by random forest classifier. The accuracy of the model′s prediction was evaluated by drawing the receiver operating characteristic (ROC) curve and measured using the area under the ROC curve (AUC). Results:A total of 1 218 imaging features were extracted by Pyradiomics package of Python software, and 3 radiomics features were screened by t-test and LASSO regression. The area under the receiver operating characteristic curve (area under the curve, AUC) of the diagnostic model constructed by random forest classifier was 0.874, and the sensitivity and specificity were 0.878 and 0.684 respectively. The AUC results of 5-fold cross-validation were 0.870, 0.881, 0.871, 0.855 and 0.898(average value: 0.870). Conclusion:The machine learning model constructed based on radiomics features has a high accuracy in distinguishing PCNSL and GBM.