We assessed whether radiomics features on diffusion tensor imaging and postcontrast T1-weighted (T1C) images differentiates the epidermal growth factor receptor (EGFR) status in brain metastases from non-small cell lung cancer (NSCLC). Radiomics features (n=5046) were extracted from 54 brain metastases patients with NSCLC (29 EGFR-wildtype, 25 EGFR-mutant). After feature selection, radiomics models were constructed by various machine learning algorithms. Diagnostic performances were compared between multiparametric and single MRI radiomics models. The best performing multiparametric radiomics model (AUC 0.97) showed better performance than any single radiomics model using ADC (AUC 0.79, p=0.007), FA (AUC 0.75, p=0.001), or T1C (AUC 0.96, p=0.678).