Development and Validation of a CEUS-Based Intratumoral–Peritumoral Radiomics Nomogram for Preoperative Prediction of HER2-Low Breast Cancer

Objective This study aims to develop a preoperative diagnostic model for HER2-low breast cancer by integrating radiomics, conventional B-mode ultrasound (US), contrast-enhanced ultrasound (CEUS), and clinical features. Method This retrospective study included 175 patients with pathologically confirmed breast cancer (91 HER2-low; 84 HER2-zero). All patients underwent preoperative conventional B-mode ultrasound and CEUS examinations. Intratumoral and peritumoral (2 mm and 3 mm) regions of interest were manually segmented, and radiomic features were extracted using PyRadiomics. Feature selection was performed through t-tests, Pearson correlation analysis, mRMR（Max-Relevance and Min-Redundancy）and LASSO regression. Multiple radiomics models (US, CEUS, multi-region) and a clinical model were constructed and evaluated using ROC analysis. A combined clinical–radiomics nomogram was generated using multivariate logistic regression and was assessed with calibration curves and decision curve analysis. Result CEUS-based peritumoral radiomics demonstrated superior discriminative performance compared to conventional ultrasound. The combined CEUS + tumor and peritumoral 3-mm model achieved the highest predictive accuracy (AUC 0.892 in the training cohort; 0.802 in the test cohort). Microcalcification was identified as an independent clinical predictor of HER2-low disease, although the clinical model alone showed moderate performance (AUC 0.721 and 0.717). The integrated nomogram combining radiomics signatures and clinical factors yielded the best overall performance (AUC 0.921 and 0.850), with favorable calibration and superior net clinical benefit. Conclusion A CEUS-based multi-region radiomics nomogram that incorporates intratumoral and peritumoral features enables accurate and noninvasive preoperative prediction of HER2-low breast cancer.