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Abstract：
We aimed to analyze the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis for cT1-2 breast cancer patients with positivity after sentinel lymph node biopsy (SLNB). A total of 830 breast cancer patients who underwent surgery between 2016 and 2021 at multi-center were included in the retrospective analysis. Patients were divided into training (n = 410), internal validation (n = 298), and external validation cohorts (n = 122) based on periods and centers. A nomogram-based prediction model for the risk of NSLN metastasis was constructed by incorporating independent predictors of NSLN metastasis identified through univariate and multivariate logistic regression analyses in the training cohort and then validated by validation cohorts. The multivariate logistic regression analysis revealed that the number of positive sentinel lymph nodes (SLNs) (P < 0.001), the proportion of positive SLNs (P = 0.029), lymph-vascular invasion (P = 0.029), perineural invasion (P = 0.023), and estrogen receptor (ER) status (P = 0.034) were independent risk factors for NSLN metastasis. The area under the receiver operating characteristics curve (AUC) value of this model was 0.730 (95% CI 0.676-0.785) for the training, 0.701 (95% CI 0.630-0.773) for internal validation, and 0.813 (95% CI 0.734-0.891) for external validation cohorts. Decision curve analysis also showed that the model could be effectively applied in clinical practice. The proposed nomogram estimated the likelihood of positive NSLNs and assisted the surgeon in deciding whether to perform further axillary lymph node dissection (ALND) and avoid non-essential ALND as well as postoperative complications.
摘要：
我们旨在分析危险因素并构建新的列线图，以预测前哨淋巴结活检（SLNB）阳性的cT1-2乳腺癌患者的非前哨淋巴结（NSLN）转移。回顾性分析共纳入了2016年至2021年在多中心接受手术的830例乳腺癌患者。患者被分为训练（n=410），内部验证(n=298)，和基于时期和中心的外部验证队列(n=122)。通过在训练队列中纳入通过单变量和多变量逻辑回归分析确定的NSLN转移的独立预测因子，构建了基于列线图的NSLN转移风险预测模型，然后通过验证队列进行验证。多因素logistic回归分析显示前哨淋巴结(SLN)阳性数目(P<0.001),阳性SLN的比例(P=0.029)，淋巴-血管侵犯（P=0.029），神经周浸润（P=0.023），雌激素受体(ER)状态(P=0.034)是NSLN转移的独立危险因素。该模型的受试者工作特征曲线下面积（AUC）值为0.730（95％CI0.676-0.785），内部验证为0.701（95%CI0.630-0.773），外部验证队列为0.813（95%CI0.734-0.891）。决策曲线分析也表明该模型可以有效地应用于临床实践。拟议的列线图估计了NSLN阳性的可能性，并协助外科医生决定是否进行进一步的腋窝淋巴结清扫术（ALND）并避免非必要的ALND以及术后并发症。
