BACKGROUND: The early prediction of treatment response for EGFR-tyrosine kinase inhibitors (EGFR-TKIs) is critical to guiding therapy in patients with metastatic non-small cell lung cancer (NSCLC). This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics model based on intratumoral and peritumoral regions to assess the response of patients with metastatic NSCLC to EGFR-TKIs. METHODS: We retrospectively recruited 418 and 160 patients with brain metastases (BMs) from -mutant NSCLC who received EGFR-TKI therapy from hospital 1 and hospital 2, respectively. The intratumoral region of interest (ROI_I) was manually segmented for contrast-enhanced T1-weighted (T1-CE) imaging. Five peritumoral ROIs (ROI_P) at 2-, 4-, 6-, 8-, and 10-mm expansions along ROI_I were defined, and combined ROIs (ROI_I and ROI_P) were automatically generated. The least absolute shrinkage and selection operator (LASSO) was used to select the most predictive features, which was followed by the construction of radiomics models (the ROI_I model, ROI_P model, and the combined model). The area under the curve (AUC) and Shapley method were used to validate the performance of the models and explain the best models. RESULTS: The combined intratumoral and peritumoral 6-mm regions achieved the best performance, with AUCs of 0.913 and 0.826 in the training and test cohort. The ROI_I model also demonstrated a degree of classification power in both the training and test cohort, with AUCs of 0.868 and 0.762, respectively. CONCLUSIONS: As compared to models consisting of intratumoral or peritumoral radiomics features alone, the model combining intratumoral and peritumoral radiomics features achieved better performance in predicting therapeutic response to EGFR-TKIs. The optimal combined region model with 6-mm peritumoral expansion along the tumor may benefit the clinical treatment of NSCLC.