Abstract
OBJECTIVE: The assessment of Human Epidermal Growth Factor Receptor 2 (HER2) expression status is crucial for determining the eligibility of breast cancer (BC) patients for HER2-targeted therapies. This study aims to develop a nomogram model that incorporates multimodal ultrasound imaging features alongside clinicopathological characteristics to evaluate HER2 status. METHODS: A retrospective analysis was conducted on 456 breast cancer patients who underwent breast ultrasound between January 2019 and December 2021. The dataset was randomly divided into a training cohort (n = 319) and a validation cohort (n = 137) in a 7:3 ratio. Independent factors predicting HER2 status in the training cohort were evaluated using univariate and multivariate logistic regression. Subsequently, a combined model was developed and validated in the validation cohort. Model performance was assessed through receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and calibration curves to evaluate discrimination, net clinical benefit, and calibration, respectively. RESULTS: Of the 456 patients enrolled, 120 (26.32%) were HER2-positive and 336 (73.68%) were HER2-negative. The area under the ROC curve (AUC) for the combined model distinguishing HER2-negative from HER2-positive patients was 0.864 (95% CI: 0.823-0.904) in the training cohort and 0.874 (95% CI: 0.815-0.933) in the validation cohort. Significant predictors included estrogen receptor (ER) status, Ki67, ultrasound lesion size, calcification, and posterior acoustic features. Additionally, the calibration curves for the combined model indicated good fit in both the training and validation cohorts. CONCLUSION: A nomogram constructed from clinical and ultrasound features may serve as a promising non-invasive tool for determining HER2 expression status, aiding in the prediction of eligibility for HER2-targeted therapy in clinical practice. CLINICAL TRIAL NUMBER: Not applicable.