To explore the risk factors for axillary lymph node burden (ALNB) after breast cancer surgery, and to construct and validate a risk prediction model.

A retrospective study was conducted on the clinical data of 363 breast cancer patients treated from January 2020 to December 2023. All patients underwent axillary lymph node dissection (ALND) or sentinel lymph node biopsy (SLNB). According to the postoperative pathological results, the patients were divided into the high nodal burden (HNB) group (≥3 metastatic lymph nodes) and the non-HNB group (≤2 metastatic lymph nodes). Multivariate Logistic regression analysis was used to identify independent risk factors, which were then incorporated into R software to construct a risk nomogram. The Bootstrap method was applied to verify the discriminative ability of the model. Calibration curves and receiver operating characteristic (ROC) curves were plotted to evaluate the goodness of fit and predictive performance of the model.

Compared with the non-HNB group, the HNB group had a higher proportion of patients with tumor size>2 cm, abnormal axillary lymph node ultrasound, pathological TNM stage Ⅲ-Ⅳ, HER-2 overexpression subtype of breast cancer, positive HER-2 expression, nerve invasion, lymphovascular invasion (LVI), and skin infiltration (all P<0.05). In contrast, the proportions of Luminal A subtype and histological grade Ⅰ were lower in the HNB group than in the non-HNB group (both P<0.05). Multivariate Logistic regression analysis showed that tumor size>2 cm, abnormal axillary lymph node ultrasound, clinical stage Ⅲ-Ⅳ, nerve invasion, and lymphovascular invasion (LVI) were independent risk factors for axillary lymph node HNB in patients (all P<0.05). A risk nomogram for axillary lymph node HNB was constructed using the 5 independent risk factors identified by Logistic regression analysis. Internal validation demonstrated a good goodness of fit of the nomogram. The area under the curve (AUC) of the predictive model constructed based on the risk factors for axillary lymph node HNB in breast cancer patients was 0.963 (95%CI: 0.942-0.984), indicating good predictive performance.

The risk prediction model constructed based on axillary lymph node HNB in breast cancer patients has good performance, and provides high clinical value for selecting appropriate axillary lymph node management strategies in breast cancer treatment.