Chen Wenxin, Hu Rihua, Chen Changming, Zhang Maoquan, Fu Xinghang, Wen Yanmei
Department of Breast Surgery, Affiliated Sanming First Hospital of Fujian Medical University, Sanming, Fujian 365001, P.R. China.
Department of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian 350001, P.R. China.
Mol Clin Oncol. 2025 Jul 23;23(4):89. doi: 10.3892/mco.2025.2884. eCollection 2025 Oct.
Axillary lymph node (ALN) metastasis is a key prognostic factor in breast cancer (BC). Although neoadjuvant chemotherapy (NAC) is widely used to downstage tumors and facilitate surgical management, accurately predicting ALN status after NAC remains a clinical challenge. The present study aimed to develop a predictive model using clinical and pathological variables to assess the risk of ALN metastasis following NAC. A retrospective analysis was conducted on 156 female patients with BC who received NAC, of whom 131 met inclusion criteria and were analyzed. The patients were randomly divided into a training cohort (97 patients) and a validation cohort (34 patients). Clinical and pathological variables, including age, menopausal status, tumor stage before chemotherapy, lymph node stage, histological grade, molecular subtyping, estrogen and progesterone receptor expression, HER-2 status, Ki67 expression, post-chemotherapy tumor stage, and the proportion of tumor and Ki67 regression before and after chemotherapy were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of ALN metastasis. A logistic regression-based nomogram was constructed using the multivariate analysis, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). In the training cohort, age, pre-chemotherapy lymph node status (N stage), Ki67 reduction level, and pre-chemotherapy molecular subtyping were identified as independent predictors of ALN metastasis. The nomogram demonstrated favorable predictive accuracy, with an AUC of 0.877. The validation cohort showed an AUC of 0.842, with sensitivity, specificity and positive predictive value of 76, 82 and 81%, respectively. The false negative rate in the validation cohort was 24%. In conclusion, a predictive model based on age, pre-chemotherapy lymph node status, Ki67 reduction level and molecular subtyping was developed to assess ALN metastasis after NAC in patients with BC. While the model demonstrated favorable accuracy, further refinement is needed to reduce the false negative rate and improve clinical utility. The incorporation of molecular biomarkers and advanced imaging techniques may enhance the model's performance.
腋窝淋巴结（ALN）转移是乳腺癌（BC）的一个关键预后因素。尽管新辅助化疗（NAC）被广泛用于降低肿瘤分期并便于手术治疗，但准确预测NAC后的ALN状态仍然是一项临床挑战。本研究旨在开发一种使用临床和病理变量的预测模型，以评估NAC后ALN转移的风险。对156例接受NAC的BC女性患者进行了回顾性分析，其中131例符合纳入标准并进行了分析。患者被随机分为训练队列（97例患者）和验证队列（34例患者）。收集了临床和病理变量，包括年龄、绝经状态、化疗前肿瘤分期、淋巴结分期、组织学分级、分子亚型、雌激素和孕激素受体表达、HER-2状态、Ki67表达、化疗后肿瘤分期以及化疗前后肿瘤和Ki67退缩的比例。进行单因素和多因素逻辑回归分析以确定ALN转移的独立预测因素。使用多因素分析构建基于逻辑回归的列线图，并使用受试者操作特征曲线（AUC）下的面积评估其性能。在训练队列中，年龄、化疗前淋巴结状态（N分期）、Ki67降低水平和化疗前分子亚型被确定为ALN转移的独立预测因素。列线图显示出良好的预测准确性，AUC为0.877。验证队列的AUC为0.842，敏感性、特异性和阳性预测值分别为76%、82%和81%。验证队列中的假阴性率为24%。总之，开发了一种基于年龄、化疗前淋巴结状态、Ki67降低水平和分子亚型的预测模型，以评估BC患者NAC后的ALN转移。虽然该模型显示出良好的准确性，但需要进一步优化以降低假阴性率并提高临床实用性。纳入分子生物标志物和先进的成像技术可能会提高该模型的性能。
