Lei Huizi, Yuan Pei, Guo Changyuan, Ying Jianming
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Oncol. 2023 Mar 10;13:1096589. doi: 10.3389/fonc.2023.1096589. eCollection 2023.
The aim of this study was to develop a nomogram for predicting positive non-sentinel lymph nodes (non-SLNs) in positive SLN breast cancer patients and validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram for non-SLN metastasis in Chinese patients.
The pathological features of 2,561 breast cancer patients were retrospectively reviewed, and the patients were divided into training and validation cohorts. Positive non-SLN predictors were identified using univariate and multivariate analyses and used to construct the nomogram. In patients with positive SLNs, the MSKCC nomogram was used to calculate the probability of non-SLN metastasis. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of this model and the MSKCC nomogram.
According to multivariate logistic regression analysis, the number of positive and negative SLNs, tumor stage, lymphovascular invasion, perineural invasion, and extracapsular extension were independent predictive factors for non-SLN metastasis and were selected to establish the nomogram for predicting positive non-SLNs. This nomogram performed favorably in predicting positive non-SLNs, with AUCs of 0.765 and 0.741 for the training and validation cohorts, respectively. The MSKCC nomogram predicted non-SLN metastasis with an AUC of 0.755.
A nomogram was developed and validated to assist clinicians in evaluating the likelihood of positive non-SLN. For Chinese patients with a known ER status before surgery, the MSKCC nomogram can be used to predict non-SLN metastases.
本研究旨在开发一种列线图,用于预测前哨淋巴结阳性(SLN)的乳腺癌患者非前哨淋巴结(non-SLN)转移,并在中国患者中验证纪念斯隆凯特琳癌症中心(MSKCC)的非前哨淋巴结转移列线图。
回顾性分析2561例乳腺癌患者的病理特征,并将患者分为训练组和验证组。通过单因素和多因素分析确定非前哨淋巴结阳性的预测因素,并用于构建列线图。在前哨淋巴结阳性的患者中,使用MSKCC列线图计算非前哨淋巴结转移的概率。计算受试者工作特征曲线(AUC)下面积,以评估该模型和MSKCC列线图的准确性。
多因素logistic回归分析显示,前哨淋巴结阳性和阴性的数量、肿瘤分期、淋巴管浸润、神经周围浸润和包膜外扩展是非前哨淋巴结转移的独立预测因素,并被选择用于建立预测非前哨淋巴结阳性的列线图。该列线图在预测非前哨淋巴结阳性方面表现良好,训练组和验证组的AUC分别为0.765和0.741。MSKCC列线图预测非前哨淋巴结转移的AUC为0.755。
开发并验证了一种列线图,以协助临床医生评估非前哨淋巴结阳性的可能性。对于术前已知雌激素受体(ER)状态的中国患者,MSKCC列线图可用于预测非前哨淋巴结转移。