Development and validation of a novel nomogram predicting axillary lymph node metastasis among breast cancer patients in Egypt.

Axillary lymph node dissection (ALND) remains widely used in the management of breast cancer in Egypt despite its association with significant morbidity. A reliable tool for predicting axillary lymph node metastasis (ALNM) could help reduce unnecessary surgical interventions. This study aimed to develop and validate a predictive nomogram to estimate the risk of ALNM in patients with breast cancer. We conducted a retrospective study of 1246 women with invasive breast cancer (stages I-III) treated at a tertiary center in Alexandria, Egypt (2018-2024). A perioperative, pathology-assisted model was developed as the primary tool, and a secondary, preoperative-only model was evaluated as a sensitivity analysis. A nomogram was constructed using multivariable logistic regression to predict ALNM, and model performance was assessed using the area under the ROC curve (AUC), calibration plots, and decision curve analysis (DCA). Five independent predictors of ALNM were identified. The primary model, which was developed using all clinically important and statistically significant variables, demonstrated excellent discrimination (AUC = 0.917) with good calibration. A sensitivity analysis yielded a preoperative-only model with highly comparable performance (AUC = 0.898). Decision curve analysis supported the use of a 20% risk threshold, at which the model achieved 93.9% sensitivity, 59.5% specificity, and a negative predictive value of 87.7%. It also confirmed a superior clinical net benefit compared to axillary sonography alone or default strategies. Our nomogram offers a practical, noninvasive tool to guide personalized axillary management in Egyptian patients with breast cancer, supporting risk-stratified decisions that may safely reduce unnecessary axillary surgery and align with global de-escalation principles.