Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model

There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present.

To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM).

Twenty-three patients were enrolled in this study. For DKI model and MEM, the D_app, K_app, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman's test was used to evaluate the correlation of parameters and Gleason scores.

The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and D_app were significantly lower in prostate cancer than non-cancerous foci (P < 0.001). The mean, 50th, 75th, and 90th percentiles of K_app were significantly higher in prostate cancer (P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased (ρ = -0.583, P = 0.018), but the 90th K_app increased (ρ = 0.642, P = 0.007).

Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.