Integration of quantitative diffusion kurtosis imaging and prostate specific antigen in differential diagnostic of prostate cancer

This study aimed to evaluate the diagnostic performance of diffusion kurtosis imaging (DKI) and prostate-specific antigen (PSA) biomarkers in differentiating prostate cancer (PCa) and benign prostatic hyperplasia (BPH). A total of 43 cases of prostate diseases verified by pathology were enrolled in the present study. These cases were assigned to the BPH group (n = 20, 68.85±10.81 years old) and PCa group (n = 23, 74.13 ± 7.37 years old). All patients underwent routine prostate magnetic resonance imaging and DKI examinations, and the mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA) values were calculated. Three serum indicators (PSA, free PSA [fPSA], and f/t PSA) were collected. We used univariate logistic regression to analyze the above quantitative parameters between the 2 groups, and the independent factors were further incorporated into the multivariate logistic regression model. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic efficacy of the single indicator and combined model. The difference in PSA, f/t PSA, MK, and FA between PCa and BPH was statistically significant (P < .05). The AUC for the combined model (f/t PSA, MK, and FA) of 0.972 (95% confidence interval [CI]: 0.928, 1.000) was higher than the AUC of 0.902 (95% CI: 0.801, 1.000) for f/t PSA, 0.833 (95% CI: 0.707, 0.958) for MK, and 0.807 (95% CI: 0.679, 0.934) for FA. The MK and FA values for DKI and f/t PSA effectively identify PCa and BPH, compared to the PSA indicators. Combining DKI and PSA derivatives can further improve the diagnosis efficiency and might help in the clinical setting.