AUTHOR=Guo Xinyi , Ling Yue , Peng Yulan , Tan Qiuwen , Xie Yanyan , Zhao Haina , Lv Qing 

TITLE=A preoperative prediction model for ipsilateral axillary lymph node metastasis of breast cancer based on clinicopathological and ultrasonography features: a prospective cohort study

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1536984

DOI=10.3389/fonc.2025.1536984

ISSN=2234-943X

ABSTRACT=BackgroundFor breast cancer, developing non-invasive methods to accurately predict axillary lymph node (ALN) status before surgery has become a general trend. This study aimed to develop and evaluate a nomogram to predict the probability of ALN metastasis (ALNM) preoperatively based on clinicopathological and ultrasonography (US) features.MethodsPatients diagnosed with breast cancer by preoperative histopathologic biopsy in West China Hospital from 1 August, 2022 to 31 January, 2024 and undergoing surgical treatment with preoperative US in West China Hospital were prospectively included. Preoperative clinicopathological and US features, along with postoperative pathological ALN status, were collected. Patients included were randomly divided into a training set and a test set (7:3). In the training cohort, the independent predictors of ALNM were obtained by univariate and multivariate binary logistic regression analyses and were used to develop a binary logistic regression model presented as a nomogram. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsA total of 610 patients were included for analysis: 427 in the training set and 183 in the test set. Molecular subtypes, tumor infiltration of the subcutaneous layer, tumor infiltration of the retromammary space, lymph node (LN) short axis, LN long/short (L/S) axis ratio, LN corticomedullary demarcation, and LN cortical thickness evenness were independent predictors of ALNM. The nomogram showed good discrimination with an area under the ROC curve (AUC) of 0.854 for the training set and 0.822 for the test set, presented good agreement between predicted and observed probabilities, and acquired net benefit across a wide threshold range.ConclusionsThe nomogram demonstrated strong discrimination, calibration, and clinical net benefit to assist clinical decisions.