Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer

To explore the clinical value of diffusional kurtosis imaging (DKI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting genotypes and prognostic factors of breast cancer.A total of 130 female patients with pathologically-confirmed breast cancer and DKI and DCE-MRI data were reviewed retrospectively. Two radiologists independently evaluated mean diffusivity (MD) and mean kurtosis (MK) for the DKI model and volume transfer constant (Ktrans), reverse rate constant (Kep), and extracellular extravascular volume ratio (Ve) for the DCE-MRI model for post-processing analyses. Receiver operating characteristic (ROC) curves were used to analyse the diagnostic efficacies.MK, Ktrans, and Kep values were significantly higher in the high-grade Nottingham prognostic index (NPI) group (NPI ≥ 3.4) than in the low-grade NPI group (NPI < 3.4) (p < 0.01). The Ktrans had significantly greater area under ROC curve (AUC) than Kep and MK in predicting the NPI (p = 0.038 and 0.0217, respectively). Higher Ktrans, Kep, and MK values were observed in the high Ki-67 expression (≥14%) group than in the low Ki-67 expression (<14%) group (p < 0.05). Moreover, the MK value had better diagnostic performance than the Ktrans and Kep values in identifying Ki-67 expression status (p = 0.0097 and 0.0008, respectively). The combined model (MD + MK + Ktrans + Ve) had a significantly higher AUC than the single parameters for differentiating between luminal A/B and non-luminal subtypes (p = 0.003, < 0.001, 0.001, and < 0.001, respectively). The Human epidermal growth factor receptor 2-positive group had higher MD and Ve values than the other subtype groups (p < 0.05), and the Ve had a sensitivity of 100%. Moreover, the Ve AUC was significantly higher than that for MD in the identification of the triple-negative subtype (p = 0.048).Ktrans of DCE-MRI and MK of DKI demonstrated good diagnostic performance in predicting the prognostic factors of breast cancer. Additionally, the combination of the DCE-MRI and DKI models can improve the efficiency of predicting breast cancer genotypes.