Prediction of HER-2 expression status in breast cancer based on multi-parameter MRI intratumoral and peritumoral...

This study aims to explore the value of multiparametric magnetic resonance imaging (MRI) techniques-dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and T2-weighted fat-suppressed imaging (T2WI)-in predicting human epidermal growth factor receptor 2 (HER-2) status in breast cancer by integrating intratumoral and peritumoral radiomics features to establish a multiparametric MRI intratumoral and peritumoral radiomics model.

A retrospective cohort of 266 female breast cancer patients was analyzed. Patients from Center 1 (n = 199) were divided into a training set (n = 140) and internal validation set (n = 59; 7:3 ratio), while Center 2 (n = 67) provided the external test set. Using 3D Slicer, tumor boundaries were manually segmented on T2WI, DWI, and DCE-MRI to define intratumoral volumes of interest (VOIs). These VOIs were expanded by 3 mm to generate peritumoral regions (VOI_Peri3mm). Radiomics features were extracted from both regions, optimized via feature selection, and used to train eight random forest (RF) models. Performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

The multiparametric MRI intratumoral and peritumoral radiomics model (DWI_Peri3 + T2WI_Peri3 + DCE_Peri3_RF) demonstrated optimal HER-2 prediction, achieving area under the curve (AUC) values of 0.822 (95 % CI:0.755-0.889), 0.823 (0.714-0.932), and 0.813 (0.712-0.914) in the training, internal validation, and external test sets, respectively. It significantly outperformed single-parameter or single-region models and maintained cross-cohort consistency.

The intratumoral-peritumoral radiomics fusion model integrating DWI, T2WI, and DCE-MRI provides high diagnostic accuracy for HER-2 assessment, offering non-invasive biomarkers and enhancing precision in breast cancer management.