Comparative Analysis of 2.5D Deep Learning, 2D Deep Learning, and Radiomics Models for Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Multi-Center Study

Objective To compare the performance of 2.5D deep learning (DL) with multi-instance learning (MIL), 2D DL, and radiomics models in predicting axillary lymph node (ALN) metastasis in breast cancer (BC) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods In this study, 732 patients from two independent institutions who underwent preoperative DCE-MRI were included. Based on the primary tumor region, we developed and compared four single-modality prediction models: a radiomics model, a 2D DL model, and two 2.5D DL-MIL models using different feature aggregation strategies. A stacking model was subsequently constructed by integrating the optimal single-modality models. The models’ performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Decision Curve Analysis. Results The stacking model achieved the highest predictive performance, with AUCs of 0.962 (95% CI: 0.946–0.977) in the training set, 0.885 (95% CI: 0.837–0.933) in the internal validation set, and 0.890 (95% CI: 0.840–0.939) in the external validation set. It demonstrated high specificity (1.000 and 0.968 in the internal and external validation sets, respectively) and provided a significant net clinical benefit. The 2.5D DL-MIL models significantly outperformed the 2D DL and radiomics models. Furthermore, the stacking model maintained robust performance across key clinical subgroups defined by age, tumor size, and BI-RADS category. Conclusion The 2.5D DL-MIL-based stacking model provides an accurate, non-invasive tool for preoperative prediction of ALN metastasis in BC. Its high specificity holds promise for reducing unnecessary sentinel lymph node biopsies, thereby aiding in personalized surgical planning.