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

Purpose: 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.

Materials and methods: 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 (K^trans), 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.

Results: MK, K^trans, 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 K^trans had significantly greater area under ROC curve (AUC) than Kep and MK in predicting the NPI (p = 0.038 and 0.0217, respectively). Higher K^trans, 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 K^trans and Kep values in identifying Ki-67 expression status (p = 0.0097 and 0.0008, respectively). The combined model (MD + MK + K^trans + 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).

Conclusion: K^trans 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.