Objective:To investigate the value of radiomics based on multiparametric magnetic resonance imaging (MRI) and multiple machine learning algorithms in predicting the expression of Bcl-2 and c-Myc in patients with encephalic primary central nervous system lymphoma (PCNSL).MethodsThe clinical data of patients with intracranial PCNSL in Maoming People's Hospital and Xinyi People's Hospital from January 2021 to January 2024 were reviewed and analyzed. Based on the expression of Bcl-2 and c-Myc proteins detected by immunohistochemical staining, patients were divided into the double-expression lymphoma (DEL) group and the non-double-expression group (nDEL group). Tumors were manually segmented on MRI images to extract radiomic features. Repeated least absolute shrinkage and selection operator (repeated-LASSO) was applied to select features, followed by the construction of classification models using 15 machine learning algorithms with parameter tuning, custom parameter combinations, LASSO, and 10-fold cross-validation.ResultsThere were no statistically significant differences in age, gender, presence of hemorrhage or necrosis, tumor location, peritumoral edema, maximum diameter, number of tumors, and presence of meningeal or ependymal invasion between training set, internal validation set, and external validation set. A preliminary set of 2 895 stable radiomic features was obtained based on an intraclass correlation coefficient (ICC>0.75). Repeated-LASSO selected 16 features. The eXtreme Gradient Boosting (XGboost) model and gradient boosting machine (GBM) models, showed the best performance, with the highest area under curve (AUC) of 0.91 in the validation set.ConclusionMultiparametric MRI combined with multiple machine learning algorithms shows great potential for detecting DEL in PCNSL.