Construction of a prediction model for axillary lymph node metastatic load in non-triple-negative breast cancer based on ultrasound and clinicopathological features

Objective To construct a nomogram model based on ultrasound features and clinicopathological data to predict axillary lymph node metastatic load in non-triple-negative breast cancer (non-TNBC) and provide guidance for surgical treatment.Methods Retrospective analysis was conducted on 376 patients with non-TNBC breast cancer who were admitted to the Affiliated Hospital of Xuzhou Medical University from January 2018 to December 2022. According to the number of metastatic lymph nodes, the patients were divided into a low-load group (≤2 nodes) and a high-load group (≥3 nodes). They were randomly assigned to a training set (n=263) and a validation set (n=113) at a 7∶3 ratio. The chi-square test was used for univariate analysis, and variables with statistical significance (P<0.05) were included in multivariate logistic regression analysis. A nomogram prediction model was constructed based on significant variables. The model was evaluated and validated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA).Results Univariate analysis showed that tumor maximum diameter, lymph node cortical thickness, and lymphatic hilum absence among ultrasound features, as well as pathological type and age among clinicopathological data, were statistically significant (P<0.05). Multivariate analysis identified tumor maximum diameter (OR=0.37, P=0.043), lymph node cortical thickness (OR=6.81, P=0.001), lymphatic hilum absence (OR=4.73, P=0.001), pathological type (OR=2.91, P=0.009), and age (OR=1.46, P=0.034) as independent predictive factors for axillary lymph node metastatic load in non-TNBC. A nomogram was then constructed accordingly. The AUC values for the training and validation sets were 0.854 (95%CI:0.798-0.911) and 0.882 (95%CI: 0.808-0.957), respectively. The calibration curves indicated good alignment between predicted and actual outcomes. The Hosmer-Lemeshow goodness-of-fit test yielded P-values of 0.708 and 0.643 for the training and validation sets, respectively. DCA demonstrated that clinical benefits could be obtained within a threshold probability range of 0-90%.Conclusions The nomogram model constructed based on ultrasound imaging features and clinicopathological data shows high accuracy and clear clinical benefit in predicting axillary lymph node metastatic load in non-TNBC breast cancer patients, providing valuable guidance for surgical decision-making.