Multimodal automated diagnosis of lymphovascular invasion in breast cancer on contrast-enhanced MRI: ResUNet + + segmentation and transformer-based classification

Abstract Objectives To develop and evaluate an automated, multimodal Transformer model for preoperative prediction of lymphovascular invasion (LVI) in invasive breast cancer using contrast-enhanced MRI. Materials and Methods A retrospective study analyzed 288 patients with pathologically confirmed invasive breast cancer who all underwent preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The study included an internal cohort (238 patients) and an external cohort (50 patients). Tumor regions of interest (ROI) were manually delineated by radiologists and automatic tumor segmentation was performed using ResUNet++. The output results were then processed with 4mm boundary dilation, while radiomic features were extracted and radiologists assessed MRI features according to BI-RADS criteria. Single-modality and multi-modality models were constructed for comparison, with the multi-modal fusion network integrating enhanced images, radiomic features, and MRI features. Model differences were assessed using DeLong test, and interpretability analysis was performed using Grad-CAM and SHAP methods. Results Automated segmentation was robust (Dice 0.916 internal and 0.921 external). The two-stage multimodal classifier achieved the highest AUC, 0.873 internally and 0.845 externally, compared with the best single-modality Transformer at 0.801 internally and 0.762 externally. Conclusion Integrating automated MRI segmentation with Transformer-based multimodal learning enables reliable preoperative LVI prediction and shows promising cross-center generalizability for clinical translation.