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Abstract：
Axillary staging is commonly performed via sentinel lymph node biopsy for patients with early breast cancer (EBC) presenting with clinically negative axillary lymph nodes (cN0). The present study aimed to investigate the association between axillary lymph node metastasis (ALNM), clinicopathological characteristics of tumors and results from axillary ultrasound (US) scanning. Moreover, a nomogram model was developed to predict the risk for ALNM based on relevant factors. Data from 998 patients who met the inclusion criteria were retrospectively reviewed. These patients were then randomly divided into a training and validation group in a 7:3 ratio. In the training group, receiver operating characteristic curve analysis was used to identify the cutoff values for continuous measurement data. R software was used to identify independent ALNM risk variables in the training group using univariate and multivariate logistic regression analysis. The selected independent risk factors were incorporated into a nomogram. The model differentiation was assessed using the area under the curve (AUC), while calibration was evaluated through calibration charts and the Hosmer-Lemeshow test. To assess clinical applicability, a decision curve analysis (DCA) was conducted. Internal verification was performed via 1000 rounds of bootstrap resampling. Among the 998 patients with EBC, 228 (22.84%) developed ALNM. Multivariate logistic analysis identified lymphovascular invasion, axillary US findings, maximum diameter and molecular subtype as independent risk factors for ALNM. The Akaike Information Criterion served as the basis for both nomogram development and model selection. Robust differentiation was shown by the AUC values of 0.855 (95% CI, 0.817-0.892) and 0.793 (95% CI, 0.725-0.857) for the training and validation groups, respectively. The Hosmer-Lemeshow test yielded P-values of 0.869 and 0.847 for the training and validation groups, respectively, and the calibration chart aligned closely with the ideal curve, affirming excellent calibration. DCA showed that the net benefit from the nomogram significantly outweighed both the \'no intervention\' and the \'full intervention\' approaches, falling within the threshold probability interval of 12-97% for the training group and 17-82% for the validation group. This underscores the robust clinical utility of the model. A nomogram model was successfully constructed and validated to predict the risk of ALNM in patients with EBC and cN0 status. The model demonstrated favorable differentiation, calibration and clinical applicability, offering valuable guidance for assessing axillary lymph node status in this population.
摘要：
对于临床上腋窝淋巴结（cN0）阴性的早期乳腺癌（EBC）患者，通常通过前哨淋巴结活检进行腋窝分期。本研究旨在探讨腋窝淋巴结转移（ALNM），肿瘤的临床病理特征和腋窝超声（US）扫描结果。此外，建立了基于相关因素预测ALNM风险的列线图模型.对符合纳入标准的998例患者的数据进行回顾性分析。然后将这些患者以7:3的比例随机分为训练和验证组。在训练组中,接收器工作特性曲线分析用于确定连续测量数据的截止值。使用R软件通过单变量和多变量逻辑回归分析来识别训练组中的独立ALNM风险变量。将选定的独立危险因素纳入列线图。使用曲线下面积（AUC）评估模型差异，同时通过校准图表和Hosmer-Lemeshow测试评估校准。为了评估临床适用性，进行了决策曲线分析(DCA).通过1000轮引导重采样进行内部验证。在998例EBC患者中，228（22.84%）发展ALNM。多因素logistic分析确定淋巴血管侵犯，美国腋窝发现，最大直径和分子亚型是ALNM的独立危险因素。Akaike信息标准是列线图开发和模型选择的基础。训练和验证组的AUC值分别为0.855（95％CI，0.817-0.892）和0.793（95％CI，0.725-0.857），分别。Hosmer-Lemeshow检验得出的训练和验证组的P值为0.869和0.847，分别,校准图与理想曲线紧密对齐，确认优良的校准。DCA显示，列线图的净收益大大超过了“无干预”和“全面干预”方法，训练组的阈值概率区间为12-97%,验证组的阈值概率区间为17-82%.这强调了该模型的强大临床实用性。成功构建并验证了一个列线图模型来预测EBC和cN0状态患者的ALNM风险。该模型表现出良好的差异化，校准和临床适用性，为评估该人群的腋窝淋巴结状态提供有价值的指导。
