Prediction of HER2‐Low Breast Cancer via Multimodal Ultrasound Imaging

Purpose Human epidermal growth factor receptor 2 (HER2) is a key biomarker for clinical management and prognostic evaluation of breast cancer patients. This study was aimed at assisting the preoperative and non‐invasive prediction of HER2‐low breast cancer using multimodal ultrasound imaging and clinicopathological indicators, providing valuable imaging information for clinical precision diagnosis and personalized treatment strategies, especially in the application of antibody‐drug conjugates such as T‐DXd. Materials and Methods This retrospective study included 147 pathologically confirmed breast cancer patients from two institutions: 101 in the training set and 46 in the external validation set. All patients underwent multimodal ultrasound (grayscale, color Doppler, elastography, and contrast‐enhanced imaging) and had complete clinicopathological data. Patients were categorized as HER2‐negative, HER2‐low, or HER2‐positive based on immunohistochemistry. Logistic regression was used to construct predictive models. Results Compared with the HER2‐negative group, low Ki‐67, PR positivity, longer rise time (RT), and lower Emax values were independent predictors of HER2‐low status ( p < 0.05), yielding an AUC of 0.876, sensitivity 0.833, and specificity 0.781. Compared with HER2‐positive cancers, HER2‐low cases showed low Ki‐67, ER/PR positivity, low Emax, and a DVPC pattern characterized by an initial increase followed by a subsequent decline as independent predictors ( p < 0.05), with an AUC of 0.929, sensitivity 0.905, and specificity 0.856. External validation confirmed robust model performance (AUC = 0.925 and 0.918 for HER2‐low vs. negative and positive, respectively). Conclusion A model integrating multimodal ultrasound and clinicopathological factors effectively predicts HER2‐low breast cancer, offering valuable imaging‐based support for clinical decision‐making.