Analysis of factors affecting axillary lymph node metastasis in breast cancer and the establishment and validation of a predictive model

Accurate preoperative assessment of axillary lymph node metastasis (ALNM) is essential for optimizing surgical planning in breast cancer (BC). We retrospectively analyzed clinical and pathological data from 1,307 BC patients who underwent surgery at Tengzhou Central People's Hospital (January 2019-December 2023). Patients were randomly assigned to a training set (n=914) and an internal validation set (n=393) in a 7:3 ratio. An independent external cohort (n=61) from Zaozhuang Municipal Hospital was used for external validation. Least absolute shrinkage and selection operator (LASSO) regression followed by multivariable logistic regression identified independent predictors of ALNM. A nomogram was constructed from the final model. Discrimination was assessed using the concordance index (C-index) and area under the receiver operating characteristic curve (AUC); calibration and decision curve analysis (DCA) evaluated agreement and clinical utility. Four variables independently predicted ALNM: estrogen receptor (ER) status, suspicious axillary lymph nodes on ultrasound, suspicious axillary lymph nodes on CT, and tumor size. The nomogram achieved C-indices of 0.81 (training), 0.74 (internal validation), and 0.84 (external validation). AUCs were 0.81, 0.74, and 0.84, respectively. Calibration plots showed good agreement between predicted and observed risks, and DCA indicated net clinical benefit across relevant threshold probabilities. We developed and externally validated a practical, interpretable nomogram that predicts ALNM preoperatively using routinely available clinicopathologic and imaging variables.