Performance of Diffusion Kurtosis Imaging Versus Diffusion Tensor Imaging in Discriminating Between Benign Tissue, Low and High Gleason Grade Prostate Cancer

Publication date: Available online 11 December 2018

Source: Academic Radiology

Author(s): Maria Giovanna Di Trani, Marco Nezzo, Alessandra S. Caporale, Riccardo De Feo, Roberto Miano, Alessandro Mauriello, Pierluigi Bove, Guglielmo Manenti, Silvia Capuani

Rationale and Objectives

To investigate the performance of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in discriminating benign tissue, low- and high-grade prostate adenocarcinoma (PCa).

Materials and Methods

Forty-eight patients with biopsy-proven PCa of different Gleason grade (GG), who provided written informed consent, were enrolled. All subjects underwent 3T DWI examinations by using b values 0, 500, 1000, 1500, 2000, and 2500 s/mm² and six gradient directions. Mean diffusivity, fractional anisotropy (FA), apparent kurtosis (K), apparent kurtosis-derived diffusivity (D), and proxy fractional kurtosis anisotropy (KFA) maps were obtained. Regions of interest were selected in PCa, in the contralateral benign zone, and in the peritumoral area. Histogram analysis was performed by measuring mean, 10th, 25th, and 90th (p90) percentile of the whole-lesion volume. Kruskal–Wallis test with Bonferroni correction was used to assess significant differences between different regions of interest. The correlation between diffusion metrics and GG and between DKI and DTI parameters was evaluated with Pearson's test. ROC curve analysis was carried out to analyze the ability of histogram variables to differentiate low- and high-GG PCa.

Results

All metrics significantly discriminated PCa from benign and from peritumoral tissue (except for K, KFA_p90, and FA). K_p90 showed the highest correlation with GG and the best diagnostic ability (area under the curve = 0.84) in discriminating low- from high-risk PCa.

Conclusion

Compared to DTI, DKI provides complementary and additional information about prostate cancer tissue, resulting more sensitive to PCa-derived modifications and more accurate in discriminating low- and high-risk PCa.

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