Advances in DCE-MRI Radiomics for Non-Invasive Prediction of Breast Cancer Molecular Subtypes: Research Progress and Clinical Translation.

The integration of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with radiomics has emerged as a transformative approach for non-invasive prediction of breast cancer molecular subtypes. This review systematically evaluates methodological innovations, clinical validation milestones, and translational applications: (1) Methodological Advancements: Standardized DCE-MRI protocols combined with multidimensional radiomic features (morphological, textural, and wavelet-transformed parameters) significantly improved discriminative performance for ER, HER2, and triple-negative subtypes. (2) Deep Learning Integration: Multitask predictive models achieved early treatment response assessment and recurrence risk stratification through spatiotemporal heterogeneity analysis. (3) Clinical Validation: Prospective multicenter trials demonstrated that radiomic models showed strong concordance with 21-gene assays and could potentially replace 38% of repeat biopsies. Despite these advancements, challenges persist in data heterogeneity and mechanistic interpretation of radiomic biomarkers. Emerging strategies integrating radiogenomic analyses and organoid validation platforms are establishing new paradigms for precision imaging-guided therapy.