Prediction model for sentinel lymph node metastasis in patients with clinical node–negative invasive breast cancer

Abstract

Background and Objective: Sentinel lymph node biopsy (SLNB) remains the standard procedure for axillary staging in patients with clinically node-negative (cN0) breast cancer; however, its associated morbidity and the relatively low rate of SLN positivity in this population have prompted reconsideration of its routine use. This study aimed to develop and validate a preoperative nomogram integrating multimodal ultrasound features with clinicopathological indicators to predict the risk of sentinel lymph node (SLN) metastasis in patients with cN0 invasive breast cancer.

Methodology: This retrospective study enrolled 359 patients with cN0 invasive breast cancer from two independent centers in China between May 2022 and October 2025. Preoperative data collected included clinicopathological indicators and multimodal ultrasound features, including grayscale ultrasound, color doppler, and strain elastography. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of SLN metastasis and to construct a nomogram prediction model. The model’s discrimination, calibration, and clinical utility were evaluated in both cohorts using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Results: Independent predictors of SLN metastasis included clinical T2 stage (OR = 2.17, 95% CI: 1.06–4.45, P = 0.034), histological grade III (OR = 4.14, 95% CI: 1.18–14.50, P = 0.026), lymph-node ultrasound Type II (OR = 2.57, 95% CI: 1.18–5.60, P = 0.018) and Type III (OR = 6.58, 95% CI: 2.15–20.16, P < 0.001), peripheral vascularity (OR = 4.81, 95% CI: 2.33–9.96, P < 0.001), and an elastography score of four (OR = 11.74, 95% CI: 1.83–75.24, P = 0.009). The nomogram demonstrated good predictive performance, with areas under the ROC curve of 0.84 (95% CI: 0.78–0.90) in the training cohort and 0.83 (95% CI: 0.73–0.92) in the validation cohort. Calibration plots showed satisfactory agreement between predicted and observed probabilities. DCA suggested potential clinical utility across a range of threshold probabilities.

Conclusion:  We developed and validated a nomogram to predict SLN metastasis in patients with cN0 invasive breast cancer. The nomogram demonstrated good predictive performance and potential clinical utility, providing an intuitive and practical tool for individualized risk assessment.