Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China.
Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, 212000, People's Republic of China.
BMC Cancer. 2023 Dec 21;23(1):1264. doi: 10.1186/s12885-023-11751-z.
To develop a clinical model for predicting high axillary nodal burden in patients with early breast cancer by integrating ultrasound (US) and clinicopathological features.
Patients with breast cancer who underwent preoperative US examination and breast surgery at the Affiliated Hospital of Nantong University (centre 1, n = 250) and at the Affiliated Hospital of Jiangsu University (centre 2, n = 97) between January 2012 and December 2016 and between January 2020 and March 2022, respectively, were deemed eligible for this study (n = 347). According to the number of lymph node (LN) metastasis based on pathology, patients were divided into two groups: limited nodal burden (0-2 metastatic LNs) and heavy nodal burden (≥ 3 metastatic LNs). In addition, US features combined with clinicopathological variables were compared between these two groups. Univariate and multivariate logistic regression analysis were conducted to identify the most valuable variables for predicting ≥ 3 LNs in breast cancer. A nomogram was then developed based on these independent factors.
Univariate logistic regression analysis revealed that the cortical thickness (p < 0.001), longitudinal to transverse ratio (p = 0.001), absence of hilum (p < 0.001), T stage (p = 0.002) and Ki-67 (p = 0.039) were significantly associated with heavy nodal burden. In the multivariate logistic regression analysis, cortical thickness (p = 0.001), absence of hilum (p = 0.042) and T stage (p = 0.012) were considered independent predictors of high-burden node. The area under curve (AUC) of the nomogram was 0.749.
Our model based on US variables and clinicopathological characteristics demonstrates that can help select patients with ≥ 3 LNs, which can in turn be helpful to predict high axillary nodal burden in early breast cancer patients and prevent unnecessary axillary lymph node dissection.
通过整合超声（US）和临床病理特征，为早期乳腺癌患者建立预测腋窝淋巴结高负荷的临床模型。
分别于 2012 年 1 月至 2016 年 12 月和 2020 年 1 月至 2022 年 3 月，在南通大学附属医院（中心 1，n=250）和江苏大学附属医院（中心 2，n=97）接受术前 US 检查和乳房手术的乳腺癌患者符合本研究条件（n=347）。根据病理上淋巴结（LN）转移的数量，患者分为两组：有限淋巴结负担（0-2 个转移性 LN）和高淋巴结负担（≥3 个转移性 LN）。此外，比较了两组之间的 US 特征与临床病理变量。进行单因素和多因素 logistic 回归分析，以确定预测乳腺癌中≥3 个 LN 最有价值的变量。然后根据这些独立因素制定了一个列线图。
单因素 logistic 回归分析显示，皮质厚度（p<0.001）、长径与横径比（p=0.001）、无门（p<0.001）、T 分期（p=0.002）和 Ki-67（p=0.039）与高淋巴结负担显著相关。在多因素 logistic 回归分析中，皮质厚度（p=0.001）、无门（p=0.042）和 T 分期（p=0.012）被认为是高负荷淋巴结的独立预测因素。该列线图的曲线下面积（AUC）为 0.749。
基于 US 变量和临床病理特征的模型表明，该模型可以帮助选择≥3 个 LN 的患者，从而有助于预测早期乳腺癌患者的腋窝淋巴结高负荷，并避免不必要的腋窝淋巴结清扫。
