Establishment of Prediction Model of Axillary Lymph Node Metastasis Before Operation for Early-Stage Breast Cancer - Xinhua Zhang MM; Chuang Zhang MM; Jian Zhang MM; Xiuming Zhang MM; Xiaowen Dou MD (Cancer Control)
Introduction This study aimed to assess the predictive value of integrating ultrasonographic features, pathological characteristics, and inflammatory markers for axillary lymph node metastasis (ALNM) in early-stage breast cancer (BC), and to construct a corresponding nomogram. Methods A retrospective review was conducted on clinical data from 287 early-stage BC patients who underwent surgery at Shenzhen Luohu People’s Hospital between January 2020 and March 2024. Based on histopathological evaluation, patients were categorized into ALNM-positive (ALNM + ) and ALNM-negative (ALNM − ) groups. Independent predictors of ALNM were identified using univariate and multivariate logistic regression analyses. These variables were used to develop a predictive nomogram. Model performance was evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA), assessing its accuracy, discrimination, calibration, and clinical utility. Results Multivariate analysis identified vascular invasion, neutrophil-to-lymphocyte ratio (NLR), lymphocyte count, tumor size, lymph node echogenicity, and margin characteristics as independent predictors of ALNM. The nomogram showed excellent discriminative ability (AUC = 0.944, 95% CI: 0.906-0.981; C-index = 0.944, 95% CI: 0.906-0.982) and good calibration (Brier score = 0.063). DCA indicated meaningful clinical benefit across relevant threshold probabilities. Conclusion The nomogram developed in this study demonstrates strong predictive performance and clinical value for preoperative ALNM assessment in early-stage BC. It may serve as a practical tool to guide individualized surgical and therapeutic decision-making.