Preoperative prediction of HER2 expression and sentinel lymph node status in breast cancer using a mammography radiomics model

Abstract

Background: This study aimed to develop and validate radiomic features derived from mammography (MG) to differentiate between various HER2 expression types (HER2-positive, HER2-low, and HER2-zero) and to preoperatively assess sentinel lymph node (SLN) status in breast cancer.

Methods: A retrospective analysis was conducted using clinicopathological and imaging data from 838 female breast cancer patients diagnosed at the Affiliated Tumor Hospital of Xinjiang Medical University between January 2016 and September 2024. The patients were randomly divided into a training set (n=586) and a test set (n=252) in a 7:3 ratio. Multivariate logistic regression analysis identified independent clinical predictors. Tumor segmentation and radiomic feature extraction were performed on mammography images. The least absolute shrinkage and selection operator (LASSO) method was applied for feature selection, and the radiomics model was developed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis.

Results: There were no significant differences in clinicopathological factors and mammographic features between the training and test sets (P>0.05). Multivariate analysis identified ethnicity, lesion size, vascular tumor thrombus, clinical stage, tumor margin, and HER2 expression as independent predictors for SLN metastasis. Lesion size, PR expression, menopausal status, SLN metastasis, Ki67, CK5/6 expression, and calcification were independent predictors for HER2 expression. The SLN metastasis prediction model achieved AUCs of 0.84 in the training set and 0.83 in the test set. The HER2 expression model showed AUCs of 0.87 (positive), 0.82 (low), and 0.85 (zero) in the training set, and 0.84 (positive), 0.78 (low), and 0.84 (zero) in the test set.

Conclusion: Radiomic features based on mammography can effectively preoperatively predict SLN status and HER2 expression types in breast cancer, offering valuable insights for individualized treatment strategies.

Keywords: HER2 expression; breast cancer; mammography; radiomics; sentinel lymph node.

Figure 1

Independent sample t-test and LASSO regression analysis were used to screen the significant features for predicting sentinel lymph nodes.

Figure 2

Sentinel lymph node status prediction model. (A): Training set ROC curve (B): Test set ROC curve (C): Calibration curve analysis of prediction model (D): Decision curve analysis of prediction model. Training Queue AUC (95% CI): 0.84 (0.79-0.87); Testing Queue AUC (95% CI): 0.83 (0.71-0.84).

Figure 3

Independent sample t-test and LASSO regression analysis were used to screen significant features for predicting HER2.

Figure 4

HER2 expression prediction model. (A): Training set ROC curve (B): Test set ROC curve (C): Calibration curve analysis of prediction model (D): Decision curve analysis of prediction model. Training Queue AUC (95% CI): Her2-zero 0.85 (0.74~ 0.87), Her2-low 0.82 (0.72-0.86), Her2-positive 0.87 (0.73-0.93); Testing Queue AUC (95% CI): Her2-zero 0.84 (0.75 ~ 0.89), Her2-low 0.78 (0.61 ~ 0.86), Her2-positive 0.84 (0.76–0.89).