PREDICTION OF AXILLARY LYMPH NODE METASTASIS IN BREAST CANCER BASED ON RADIOMICS: A MULTICENTER STUDY

To develop a radiomics-based machine learning model for the noninvasive prediction of axillary lymph node metastasis (ALNM) in breast cancer patients, thereby providing an objective basis for clinical diagnosis and treatment. This multicenter retrospective study included 501 patients from four hospitals. All patients underwent preoperative MRI examinations, and their axillary lymph node status was pathologically confirmed. A total of 13,184 radiomics features were extracted from each patient. Feature selection was performed using one-way analysis of variance (ANOVA), Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Based on the selected features, four machine learning models were constructed, and their predictive performance was assessed using receiver operating characteristic (ROC) analysis in the training, internal validation, and external validation cohorts. Among the four models, the logistic regression (LR) model demonstrated the best predictive performance. The areas under the ROC curve (AUC) for the training, internal validation, and external validation cohorts were 0.753, 0.743, and 0.698, respectively, indicating good predictive accuracy and generalizability. Conclusion: The radiomics-based LR model enables noninvasive prediction of ALNM in breast cancer patients and holds potential clinical value for guiding individualized treatment strategies.