Establishment of Prediction Model of Axillary Lymph Node Metastasis Before Operation for Early-Stage Breast Cancer.

Abstract

IntroductionThis 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.MethodsA 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.ResultsMultivariate 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.ConclusionThe 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.