Division of Breast Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-Gu, Seoul, 05505, Republic of Korea.
Sci Rep. 2023 Mar 18;13(1):4480. doi: 10.1038/s41598-023-31628-2.
Our aim was to develop a tool to accurately predict the possibility of non-sentinel lymph node metastasis (NSLNM) during surgery so that a surgeon might decide the extent of further axillary lymph node dissection intraoperatively for patients with 1-3 positive sentinel lymph node(s) (SLN) after neoadjuvant chemotherapy. After retrospective analysis of Asan Medical Center (AMC) database, we included 558 patients' records who were treated between 2005 and 2019. 13 factors were assessed for their utility to predict NSLNM with chi-square and logistic regression with a bootstrapped, backward elimination method. Based on the result of the univariate analysis for statistical significance, number of positive SLN(s), number of frozen nodes, Progesterone Receptor (PR) positivity, clinical N stage were selected for the multivariate analysis and were utilized to generate a nomogram for prediction of residual nodal disease. The resulting nomogram was tested for validation by using a patient group of more recent, different time window at AMC. We designed a nomogram to be predictive of the NSLNM which consisted of 4 components: number of SLN(s), number of frozen nodes, PR positivity, and clinical N stage before neoadjuvant chemotherapy. The Area under the receiver operating characteristics curve (AUC) value of this formula was 0.709 (95% CI, 0.658-0.761) for development set and 0.715 (95% CI, 0.634-0.796) for validation set, respectively. This newly created AMC nomogram may provide a useful information to a surgeon for intraoperative guidance to decide the extent of further axillary surgery.
我们的目的是开发一种工具，以便在手术中准确预测非前哨淋巴结转移（NSLNM）的可能性，从而使外科医生能够在新辅助化疗后 1-3 个前哨淋巴结（SLN）阳性的患者中，在手术中决定进一步腋窝淋巴结清扫的程度。对 Asan 医疗中心（AMC）数据库进行回顾性分析后，我们纳入了 558 名患者的记录，这些患者在 2005 年至 2019 年期间接受了治疗。采用卡方检验和逻辑回归（带 bootstrap，逐步向后消除法）评估了 13 个因素对预测 NSLNM 的作用。基于单变量分析的统计学意义、SLN 阳性数量、冷冻节点数量、孕激素受体（PR）阳性、临床 N 分期等因素，进行多变量分析，并生成预测残留淋巴结疾病的列线图。使用 AMC 近期不同时间窗口的患者组对生成的列线图进行验证。我们设计了一个列线图来预测 NSLNM，该列线图由 4 个部分组成：SLN 数量、冷冻节点数量、PR 阳性和新辅助化疗前的临床 N 分期。该公式的受试者工作特征曲线（ROC）下面积（AUC）值在开发组中为 0.709（95%CI，0.658-0.761），在验证组中为 0.715（95%CI，0.634-0.796）。这个新创建的 AMC 列线图可能为外科医生提供有用的信息，以指导术中决定进一步腋窝手术的范围。
