BACKGROUND: Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectiveness of integrating MRI radiomic features with clinical data using a deep learning approach to predict vascular invasion in breast cancer patients. METHODS: A retrospective analysis was conducted on 102 patients with invasive breast cancer confirmed by surgical pathology. Using the MR750 3.0 T as the examination device, the subject underwent the examination in standard breast positions and sequences. Diffusion-weighted imaging (DWI) was performed with two selected b-values, specifically 0 and 1000 s/mm RESULTS: The univariate models based on individual MRI sequences or clinical data demonstrated variable diagnostic performance. In contrast, the multifactorial model that combined radiomic features with clinical data achieved significantly higher accuracy, with an AUC of 0.829, sensitivity of 76.9 %, and specificity of 83.3 %. CONCLUSION: Integrating MRI radiomics and clinical data enhances the preoperative prediction of vascular invasion in breast cancer. This approach can improve diagnostic accuracy, providing valuable insights for clinical decision-making and personalized treatment strategies.