Multiparametric Quantitative MRI Combining Synthetic MRI and MUSE-DWI for Noninvasive Stratification of HER2 Expression in Breast Cancer

Background Accurate stratification of HER2 status is crucial for treatment decision-making and prognostic evaluation in breast cancer. With the recognition of HER2-low as a distinct subtype, conventional pathological methods remain the gold standard but are invasive and subject to sampling bias. Imaging provides a noninvasive alternative for evaluating HER2 expression. This study aimed to assess the value of synthetic MRI (SyMRI) combined with multiplexed sensitivity encoding diffusion-weighted imaging (MUSE-DWI) for noninvasive stratification of HER2 status in breast cancer. Methods A total of 138 patients with pathologically confirmed invasive breast cancer underwent standardized MRI protocols, including SyMRI, MUSE-DWI, and DCE-MRI. Quantitative parameters (T₁, T₂, PD, ADC, and their pre-/post-contrast changes) were measured. Differences among HER2-zero, HER2-low, and HER2-oe groups were compared. Univariate and multivariate logistic regression analyses were performed to identify independent predictors and construct nomogram models for predicting HER2 positivity and HER2-low. Model performance was evaluated using ROC and calibration analyses. Results HER2-over tumors more frequently demonstrated heterogeneous enhancement, washout-type TICs, and larger maximum diameters. Quantitative analysis identified ADC, maximum diameter, T2-per, and enhancement pattern as independent predictors of HER2 positivity (combined model AUC = 0.940; corrected AUC = 0.930), while ADC and PD-Δ% were independent predictors of HER2-low (combined model AUC = 0.810; corrected AUC = 0.830). Both models demonstrated good discrimination and calibration. Conclusions Multiparametric quantitative imaging with SyMRI and MUSE-DWI provides multidimensional information on HER2-related alterations in cellularity, water content, and vascular permeability. This approach offers a reliable noninvasive tool for assessing HER2 expression status, with particular clinical relevance for the identification of HER2-low and personalized treatment planning.