OBJECTIVES: This study aims to establish a dual-feature fusion model integrating radiomic features with deep learning features, utilizing single-modality pre-treatment lung CT image data to achieve early warning of brain metastasis (BM) risk within 2 years in EGFR-positive lung adenocarcinoma. MATERIALS AND METHODS: After rigorous screening of 362 EGFR-positive lung adenocarcinoma patients with pre-treatment lung CT images, 173 eligible participants were ultimately enrolled in this study, including 93 patients with BM and 80 without BM. Radiomic features were extracted from manually segmented lung nodule regions, and a selection of features was used to develop radiomics models. For deep learning, ROI-level CT images were processed using several deep learning networks, including the novel vision mamba, which was applied for the first time in this context. A feature-level fusion model was developed by combining radiomic and deep learning features. Model performance was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA), with statistical comparisons of area under the curve (AUC) values using the DeLong test. RESULTS: Among the models evaluated, the fused vision mamba model demonstrated the best classification performance, achieving an AUC of 0.86 (95% CI: 0.82-0.90), with a recall of 0.88, F1-score of 0.70, and accuracy of 0.76. This fusion model outperformed both radiomics-only and deep learning-only models, highlighting its superior predictive accuracy for early BM risk detection in EGFR-positive lung adenocarcinoma patients. CONCLUSION: The fused vision mamba model, utilizing single CT imaging data, significantly enhances the prediction of brain metastasis within two years in EGFR-positive lung adenocarcinoma patients. This novel approach, combining radiomic and deep learning features, offers promising clinical value for early detection and personalized treatment.