Mathematical Models for Diffusion-Weighted Imaging of Prostate Cancer Using b Values up to 2000 s/mm2 : Correlation with Gleason Score and Repeatability of Region of Interest Analysis

Purpose: To evaluate four mathematical models for diffusion weighted imaging (DWI) of prostate cancer (PCa) in terms of PCa detection and characterization. Methods: Fifty patients with histologically confirmed PCa underwent two repeated 3 Tesla DWI examinations using 12 equally distributed b values, the highest b value of 2000 s/mm(2). Normalized mean signal intensities of regions-of-interest were fitted using monoexponential, kurtosis, stretched exponential, and biexponential models. Tumors were classified into low, intermediate, and high Gleason score groups. Areas under receiver operating characteristic curve (AUCs) were estimated to evaluate performance in PCa detection and Gleason score classifications. The fitted parameters were correlated with Gleason score groups by using the Spearman correlation coefficient (rho). Coefficient of repeatability and intraclass correlation coefficient [specifically ICC(3,1)], were calculated to evaluate repeatability of the fitted parameters. Results: The AUC and r values were similar between parameters of monoexponential, kurtosis, and stretched exponential (with the exception of the a parameter) models. The absolute r values for ADC(m), ADC(k), K, and ADC(s) were in the range from 0.31 to 0.53 (P<0.01). Parameters of the biexponential model demonstrated low repeatability. Conclusion: In region-of-interest based analysis, the monoexponential model for DWI of PCa using b values up to 2000 s/mm(2) was sufficient for PCa detection and characterization. (C) 2014 Wiley Periodicals, Inc.