Radiomics Nomogram Based on Multiparametric MRI for Predicting the Hormone Receptor Status of HER2-Low Expression Breast Cancer

Objective: To develop and validate a multiparametric magnetic resonance imaging (mpMRI)-based radiomics nomogram to predict hormone receptor (HR) status in HER2-low breast cancer. Methods: A total of 198 HER2-low expression breast cancer patients who underwent mpMRI in The First Affiliated Hospital of Anhui Medical University Hospital from January 2019 to January 2025 were retrospectively analyzed. 69.2% (n=137) of patients were HR-positive, and 30.8% (n=61) were HR-negative. Patients were divided into a training set (n=138) and a testing set (n=60) in a 7:3 ratio. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) images separately, and the radiomics score (radscore) was calculated. The clinical-radiological model (CM), single radiomics model (RM), and mpMRI RM were constructed, and a nomogram integrating radscore with clinical-radiological characteristics was developed. The predictive performance of the models was evaluated by receiver operating characteristic (ROC) curve analysis. Results: The area under the curve (AUC) of mpMRI RM in the training set and the testing set was 0.940 and 0.897, respectively, which were superior to that of single-modality models. The nomogram incorporating radscore and clinical-radiological characteristics, including ADC value, T2SI radio, and enhancement pattern, demonstrated higher AUC in both the training set (AUC=0.957) and testing set (AUC=0.891) than other RMs in predicting HR status of HER2-low expression breast cancer. Conclusion: An mpMRI-based nomogram incorporating radscore and clinical-radiological characteristics showed good predictive efficacy for assessing the HR status of HER2-low expression breast cancer.