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.