Heterogeneity Assessment of Breast Cancer Tumor Microenvironment: Multiparametric Quantitative Analysis with DCE-MRI and Discovery of Radiomics Biomarkers

), enables dynamic characterization of tumor vascularization and perfusion heterogeneity. Concurrently, radiomics technology, leveraging high-throughput feature extraction and machine learning modeling, identifies potential biomarkers associated with TME biological properties. This review systematically examines the integration strategies of DCE-MRI multiparametric quantification and radiomics: first, elucidating the capability of DCE-MRI pharmacokinetic models to quantify microvascular heterogeneity, and delineating radiomics feature screening and predictive model construction based on 3D segmentation. Furthermore, it explores the combined application of these techniques in evaluating angiogenesis, resolving immune microenvironment dynamics, and mapping metabolic heterogeneity, with emphasis on clinical translational evidence in molecular subtype discrimination, treatment response prediction, and prognostic assessment. Key limitations persist in technical standardization (eg, 37% variability in Ktrans values across 1.5T/3.0T systems) and biological interpretability, with fewer than 40% of radiomics features linked to known molecular pathways. Future advancements demand multicenter data harmonization, radiogenomics integration, and digital twin technology to optimize personalized therapeutic navigation systems. This work provides methodological insights and technical innovation pathways for noninvasive TME heterogeneity assessment in breast cancer.