Development and validation of a nomogram for predicting axillary lymph node burden of early-stage breast cancer

Abstract

Purpose To identify ultrasonic and clinicopathological features associated with high axillary lymph node (ALN) burden ( > 2 metastatic ALNs) and develop a nomogram for preoperatively predicting ALN burden in patients with early-stage breast cancer. Methods In this retrospectively study, a total of 575 women with clinical T1-2N0 breast cancer confirmed by histopathology were included from January 2021 to June 2023. Patients were randomly divided into a training set (n = 403) and a validation set (n = 172). According to the pathological results of the axilla, patients were divided into low burden group and high burden group. Univariate and multivariate logistic regression analysis were performed to determine the risk factors and develop the nomogram. The performance of the nomogram was assessed by receiver operating characteristic (ROC) curves, the calibration curves and decision curve analysis in the training and validation sets. Results In the training and validation sets, 10.4% (42/403) and 10.5% (18/172) of patients showed high burden, respectively. Univariate and multivariate analysis indicated that the distance from the nipple (P = 0.007), cortical thickness (P < 0.001), number of abnormal LNs on ultrasound (P = 0.001) were independent predictors of ALN burden. The nomogram showed good discrimination with AUC values of 0.878 and 0.837 in the training and validation sets, respectively. The calibration curves demonstrated good agreement between predictions of nomogram and pathologic results. And the clinical net benefit was ideal based on decision curves analysis. Conclusion The nomogram developed in this study has good performance for non-invasively predicting ALN burden in early-stage breast cancer and might provide reference for clinical treatment decision-making.