Objectives Using a radiomics method to predict brain invasion by meningioma. Methods 1595 quantitative imaging features were extracted. LASSO was performed to select features. SVM was used to fit the predictive model. Furthermore, a nomogram was constructed, and validated using decision curve analysis (DCA). Results 8 features were significantly correlated with brain invasion. The radiomics model derived from the fusing MRI sequences resulted in the best discrimination ability, with AUC of0.855(95%CI, 0.829-0.882), sensitivity of 80.32% (95%CI, 75.56%-85.25%). Conclusions The radiomics model developed in this study provided a new non-invasive way to facilitate the preoperative prediction of brain invasion in meningioma.