探讨乳腺癌术后腋窝淋巴结负荷的危险因素，构建风险预测模型及验证。

回顾性研究2020年1月至2023年12月363例乳腺癌患者病例资料，全部行腋窝淋巴结清扫术（ALND）或前哨淋巴结活检术（SLNB）。根据术后病理结果，将患者分为高负荷（HNB）组（≥3枚转移淋巴结）和非HNB组（≤2枚转移淋巴结）。采用多因素Logistic回归分析，将独立危险因素引入R软件构建风险列线图，采用Bootstrap法验证模型区分度，绘制Calibration曲线和受试者工作特征（ROC）曲线进行拟合度及预测效能评估。

HNB组患者病灶大小>2cm、腋窝淋巴结超声异常、病理TNM分期Ⅲ-Ⅳ期、乳腺癌分子分型为HER-2过表达型、HER-2表达阳性、神经侵犯、脉管瘤栓、皮肤浸润情况占比高于非HNB组（P<0.05），而乳腺癌Luminal A型、组织分级Ⅰ级占比低于非HNB组（P<0.05）。多因素Logistic回归模型显示，病灶大小>2cm、腋窝淋巴结超声异常、临床分期Ⅲ-Ⅳ期、神经侵犯、脉管侵犯（LVI）是导致患者腋窝淋巴结HNB的独立危险因素（P<0.05）。根据Logistic回归分析筛选5项独立危险因素构建患者腋窝淋巴结HNB风险列线图并进行内部验证显示拟合度好。基于乳腺癌患者腋窝淋巴结HNB危险因素构建的预测模型的曲线下面积(AUC)为0.963（95%CI：0.942-0.984），预测效能较好。

基于乳腺癌患者腋窝淋巴结HNB构建的风险预测模型效能较好，为乳腺癌选择合适的腋窝淋巴结处理策略提供较高的临床价值。

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.