Development and External Validation of a Preoperative Nomogram for Predicting Axillary Lymph Node Metastasis in Breast Cancer Using Routine Ultrasound and Core Needle Biopsy Variables.

BACKGROUND

Accurate preoperative assessment of axillary lymph node metastasis (ALNM) is essential for individualized axillary management in breast cancer, yet imaging-based evaluation alone remains imperfect. We aimed to develop and externally validate a practical nomogram based on routinely available preoperative ultrasound and core needle biopsy (CNB) variables.

METHODS

This retrospective, two-center cohort study included 295 women with pathologically confirmed primary breast cancer who underwent breast surgery with axillary nodal staging between January 2021 and January 2025. The training cohort included 182 patients from the Changji Branch, and the external validation cohort included 113 patients from the First Affiliated Hospital of Xinjiang Medical University. Candidate predictors were evaluated using univariable analysis, LASSO regression, and multivariable logistic regression. Model discrimination, calibration, and clinical utility were assessed using receiver operating characteristic analysis, calibration analysis, and decision curve analysis.

RESULTS

ALNM was confirmed in 88 of 182 patients (48.4%) in the training cohort and 50 of 113 patients (44.2%) in the external validation cohort. The final nomogram included ultrasound-reported calcifications, tumor size, histological grade, CNB-derived lymphovascular invasion, and HER2 status. The model showed good discrimination, with an AUC of 0.858 (95% CI 0.805-0.911) in the training cohort and 0.854 (95% CI 0.779-0.929) in the external validation cohort. Calibration and decision curve analyses supported acceptable model performance and potential clinical utility in both cohorts.

CONCLUSION

We developed and externally validated an interpretable preoperative nomogram using routinely obtainable ultrasound and CNB variables for individualized estimation of ALNM risk in breast cancer. The model may support preoperative risk stratification and axillary management planning, although prospective multicenter validation is warranted.