MRI ‐Based Radiomics Model for Classifying Axillary Lymph Node Burden and Disease‐Free Survival in Patients With Early‐Stage Breast Cancer

ABSTRACT Background Axillary lymph node (ALN) burden is a key prognostic determinant in breast cancer and plays an important role in diagnosis and treatment planning. The noninvasive assessment of ALN burden might improve patient stratification and guide individualized treatment. Purpose To explore the potential of MRI‐based radiomics in preoperative classification of ALN burden in early‐stage breast cancer and to assess survival differences between patients with high‐ and low‐ALN burden. Study Type Retrospective. Population Pathologically confirmed breast cancer patients ( n = 343): training ( n = 170), testing ( n = 73) and internal validation ( n = 50) from center 1; center 2 ( n = 50) for external validation. Field Strength/Sequence 3T, dynamic contrast‐enhanced (DCE) sequence. Assessment Four different machine learning classifiers were used to develop clinical, radiomics, and combined models for preoperative ALN burden assessment (66 high‐burden cases). DCE‐MRI radiomics features were extracted, and the optimal model was used to determine the Radscore. A clinical model was derived from clinicopathological variables, and integrated with the Radscore to form a combined model. Kaplan–Meier and Cox regression analyses were performed to compare disease‐free survival (DFS) between high‐ and low‐burden groups. Statistical Tests Intraclass Correlation Coefficient (ICC), LASSO, logistic regression, Mann–Whitney U tests, Chi‐squared tests, DeLong's test, Area Under the Curve (AUC), Decision Curve Analysis (DCA), calibration curves and Kaplan–Meier analysis, with p < 0.05 as significant. Results The Random Forest–based combined model yielded AUCs of 0.881 (95% CI, 0.811–0.941) in the training set, 0.826 (0.716–0.917) in the testing set, 0.912 (0.811–0.985) in the internal validation set, and 0.881 (0.737–0.985) in the external validation set. When using the cut‐off value determined from the training set, the overall accuracy was 0.759, 0.795, 0.840, and 0.860, respectively. Kaplan–Meier analysis revealed significant DFS differences between the model‐classified high‐ and low‐burden groups ( p = 0.022, HR = 2.9). Data Conclusion MRI‐based radiomics models show promise for noninvasive evaluation of ALN burden and prognostic stratification of survival outcomes in breast cancer patients. Level of Evidence 3. Technical Efficacy Stage 2.