AUTHOR=Yang Kui , Zhang Wei , Zheng Hao , Ji Da Shuang , Chang Hu , Li Feng 

TITLE=Multiparametric quantitative MRI combining SyMRI and MUSE-DWI for noninvasive stratification of HER2 status in breast cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1709170

DOI=10.3389/fonc.2025.1709170

ISSN=2234-943X

ABSTRACT=BackgroundAccurate 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, which has recently gained clinical relevance as HER2-low patients may benefit from emerging HER2-targeted therapies, conventional pathological methods remain the gold standard; however, they are invasive and prone to sampling bias, and may not fully reflect intratumoral heterogeneity. 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.MethodsA total of 138 patients with pathologically confirmed invasive breast cancer underwent standardized MRI protocols, including SyMRI, MUSE-DWI, and DCE-MRI before biopsy or any treatment. Quantitative parameters (T1, T2, PD, ADC, and their pre-/post-contrast changes) were measured. Differences among HER2-zero, HER2-low, and HER2-overexpressing groups were analyzed. Univariate and multivariate logistic regression analyses were performed to identify independent predictors and construct nomogram models for predicting HER2 positivity and HER2-low status. Model performance was evaluated using ROC curves and calibration analysis.ResultsHER2-overexpressing tumors more frequently demonstrated heterogeneous enhancement, washout-type time–intensity curves (TICs), and larger maximum diameters. In multivariable analysis, ADC, maximum diameter, T2-pre, and enhancement pattern were independent predictors of HER2 positivity (AUC = 0.940; bootstrap-corrected AUC = 0.930), whereas ADC and PD-Δ% independently predicted HER2-low status (AUC = 0.810; bootstrap-corrected AUC = 0.830). Both models showed good discrimination and calibration, and decision curve analysis indicated a favorable net clinical benefit across a wide range of threshold probabilities.ConclusionsSyMRI combined with MUSE-DWI enables noninvasive stratification of HER2 status in breast cancer. The proposed models demonstrated high diagnostic performance, good calibration, and favorable clinical utility in decision curve analysis, particularly for identifying HER2-low tumors. This imaging approach has the potential to complement biopsy and assist personalized treatment planning.