Multimodal imaging and advanced quantitative techniques for HER-2 status prediction in breast cancer.

HER-2-positive breast cancer is a biologically distinct subtype, accurate early assessment of HER-2 status is therefore critical for guiding personalized treatment. Currently mainly detected through immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) on biopsy or surgical specimens. However, these methods are invasive, susceptible to sampling errors, and lack the capability for real-time, noninvasive monitoring of tumor heterogeneity or treatment response. Therefore, the development of noninvasive imaging-based predictive methods has gained significant research interest. Multiparametric magnetic resonance imaging (mpMRI) can quantify tumor perfusion parameters (Ktrans-vascular permeability, Ve- extracellular space) through dynamic contrast-enhanced MRI (DCE-MRI), measure the apparent diffusion coefficient (ADC) using diffusion-weighted imaging (DWI), and obtain metabolic information via positron emission tomography-MRI (PET-MRI), which are closely associated with HER-2 expression status. Concurrently, radiomics and deep learning (DL) systematically extract multidimensional features of breast tumors from multimodal imaging data, including morphological parameters (sphericity, surface area), first-order statistical metrics (kurtosis, K; skewness), and textural features (gray-level co-occurrence matrix GLCM, quantisation texture space distribution; gray-level run-length matrix GLRLM, evaluation of homogeneous region size), thereby constructing high-dimensional quantitative analysis datasets. Based on the resolution of the heterogeneity of the feature spatial distribution, the DL algorithms can autonomously mine the potential imaging patterns closely related to the expression of HER-2 molecules and establish a non-invasive prediction model. Although traditional single-parameter models, such as the ADC derived from DWI, can provide valuable information about the tumor microenvironment, their predictive efficacy is often constrained by parameter inconsistencies and a lack of standardization. As a narrative review, this article argues that multimodal imaging, radiomics, and deep learning are better equipped to capture the complex HER-2-related tumor heterogeneity, thereby providing a stronger theoretical foundation for guiding personalized treatment strategies and prognostic evaluation.