This study aims to evaluate the relationship between multiparametric magnetic resonance imaging (MRI) features - including T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and dynamic contrast enhancement (DCE) - and molecular subtypes of breast cancer, to enhance non-invasive diagnostic stratification. This retrospective study enrolled 134 consecutive patients with pathologically confirmed breast cancer. A comparative analysis was performed to evaluate intergroup variations in clinicopathological characteristics, morphological features, and multiparametric MRI parameters (including T2WI signal intensity, ADC value, early-phase enhancement rate, and time-intensity curve pattern) across the four molecular subtypes. The cohort comprised 134 breast cancer patients stratified into molecular subtypes as follows: Luminal A (n = 22, 16.4%), Luminal B (n = 82, 61.2%), human epidermal growth factor receptor-2 (HER-2) (+) (n = 13, 9.7%), and triple-negative breast cancer (TNBC) (n = 17, 12.7%). Among the subtypes, there were statistically significant differences in terms of age, Ki-67 index, mass shape, margin, internal enhancement characteristic, T2WI signal, ADC value, early enhancement rate, and time intensity curve (TIC) pattern (P = 0.025; P 0.05). Multiparametric MRI features, particularly ADC values, DCE kinetics, and T2WI signals, demonstrate significant associations with breast cancer molecular subtypes. These imaging biomarkers offer potential for non-invasive subtype prediction, supporting more tailored diagnostic and treatment strategies.
Li et al. (Wed,) studied this question.