Predicting Axillary Lymph Node Metastasis in Breast Cancer by Integrating Dual-ROI Features from Multi-Parametric MRI

Prediction of axillary lymph node metastasis in breast cancer is a critical factor in determining the prognosis of breast cancer patients and the necessity of axillary intervention. However, current research methods are limited to single-parameter MRI sequences, only use images from the primary tumor area or the axillary lymph node region, and the methods for achieving feature fusion pay relatively little attention to modal interactivity. To address these, we propose a multi-modal fusion model that uses a Cross-Sequence Module to capture relationships between different sequences, while the interaction between the primary tumor and axillary lymph node regions of interest (ROIs) is achieved through a module we refer to as the Dual-ROI Dependency Module, in order to predict axillary lymph node metastasis. Experimental results demonstrate that this method achieves strong predictive performance (ACC=0.887, AUC=0.931), outperforming single-parametric sequence models, single-region models, and models based on simple feature concatenation, validating the superiority of our approach.