Ultrasound-Based Intratumoral and Peritumoral Radiomics to Differentiate Between HER2-Low and HER2-Zero Breast Cancers: Model Construction and Performance Evaluation

Background: Breast cancer (BC) is a major global malignancy with rising incidence. The lack of effective traditional anti-human epidermal growth factor receptor 2 (HER2) therapies for HER2-low BC underscores the critical need to identify this subtype early. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics can help to differentiate between HER2-low and HER2-zero BC, although this method has limited contrast and access. Ultrasound (US) is a cost-effective technique, although radiomics research remains limited, and traditional radiomics largely ignores peritumoral value. This study aimed to determine whether intra- and peritumoral radiomic features observed by grayscale US can differentiate between HER2-low and HER2-zero BC. Methods: This retrospective diagnostic study enrolled 209 consecutive BC cases from May 2022 to January 2024. These cases were categorized as HER2-low (immunohistochemistry (IHC) 1+/2+, no erythroblastic leukemia viral oncogene homolog 2 (ERBB2) fluorescence in situ hybridization (FISH) amplification, n = 129) or HER2-zero (IHC 0, n = 80). Patients were age-matched and randomly assigned (block size = 10) to the training (n = 155) and validation (n = 54) cohorts, with predefined exclusion criteria applied (e.g., missing pathological data, poor US quality). After calibration, two experienced radiologists performed blinded manual intratumoral region-of-interest (ROI) segmentation (ITK-SNAP v3.8.0), with interoperator consistency confirmed by immunocytochemistry (ICC) >0.75. Pyradiomics was used to automate the expansion of the 1 mm and 2 mm peritumoral regions, feature extraction, and z-score normalization. Features were filtered via Spearman’s correlation, Mann-Whitney U test, and least absolute shrinkage and selection operator (LASSO) regression (10-fold cross-validation for optimal λ). A predictive model for HER2 status was built using LASSO regression (variance inflation factor (VIF) <1.2 to avoid multicollinearity), and the performance of this model was evaluated for accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves (area under the curve, AUC), calibration curves (Hosmer-Lemeshow test), and decision curve analysis (DCA). A radiomic nomogram integrating radiomic and clinical signatures was evaluated in the validation cohort. Statistical analyses were performed using R v4.2.2 (two-sided p < 0.05 for significance). Results: The clinical model showed limited discrimination in the test set (AUC = 0.594). A total of 3320 radiomic features were extracted from intratumoral, as well as 1 mm and 2 mm peritumoral regions, with the selection of 30, 19, and 24 features, respectively, via LASSO regression. The intratumoral-only model had AUCs of 0.730 (training) and 0.649 (test), while the intratumoral + 1 mm peritumoral model exhibited enhanced performance (training AUC = 0.852; test AUC = 0.868). The 2 mm peritumoral-integrated model demonstrated a high AUC in the training set (0.918), but poor performance in the test set (AUC = 0.509). A combined model (intratumoral + 1 mm peritumoral features + clinical factors) was used to generate a nomogram (no multicollinearity, VIF: 1.039–1.179) with AUCs of 0.882 (training) and 0.835 (test). The DCA confirmed the clinical utility of the combined model, although the diagnostic performance of the model was slightly lower than that of the intratumoral + 1 mm peritumoral model. Conclusions: Combining 1 mm peritumoral radiomics with intratumoral and clinical data improves the discrimination of HER2-low from HER2-zero BC (AUC = 0.882), thus reducing the need for biopsy and assisting with therapy planning. Optimizing peritumoral margins enhances diagnostic accuracy, thereby validating radiomics for BC subtyping.