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AJR Am J Roentgenol · Nov 2017
Diffusion Kurtosis Imaging Helps to Predict Upgrading in Biopsy-Proven Prostate Cancer With a Gleason Score of 6.
- Chen-Jiang Wu, Yu-Dong Zhang, Mei-Ling Bao, Hai Li, Xiao-Ning Wang, Xi-Sheng Liu, and Hai-Bin Shi.
- 1 Department of Radiology, First Affiliated Hospital with Nanjing Medical University, 300 Guangzhou Rd, Nanjing, China, 210009.
- AJR Am J Roentgenol. 2017 Nov 1; 209 (5): 1081-1087.
ObjectiveThe purpose of this study was to investigate whether diffusion kurtosis imaging (DKI) is useful for predicting upgrades in Gleason score (GS) in biopsy-proven prostate cancer with a GS of 6.Materials And MethodsA total of 46 patients with biopsy-proven GS 6 prostate cancer, 3-T DWI results, and surgical pathologic results were retrospectively included in the study. DWI data were postprocessed with monoexponential and DK models to quantify the apparent diffusion coefficient (ADC), apparent diffusion for gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). The volume of the lesions, prostate-specific antigen (PSA) level, and diffusion variables (ADCmin, Dappmin, Kappmax, ADCmean, Dappmean, and Kappmean) were evaluated. PSA and DKI were combined as a parameter in a logistic regression model. The utility of these parameters in predicting an upgrade in GS was analyzed with ROC regression.ResultsThe rate of GS upgrade was 50.0% (23/46). The GS upgrade group had significantly lower ADCmin (p = 0.007), ADC mean (p = 0.003), D appmin (p < 0.001), and Dappmean (p = 0.001) values and significantly higher Kappmax (p = 0.003), Kappmean (p = 0.005), and PSA (p = 0.004) values than the group that did not have an upgrade. Among single parameters, Kappmax had the highest ROC AUC value (0.819, p < 0.05), and among all the parameters and models, PSA-Kappmax had the highest AUC (0.868, p < 0.05) and Youden index (0.6522).ConclusionThe results showed that DKI may help in prediction of GS upgrade in biopsy-proven GS 6 prostate cancer. The comprehensive consideration of DKI and PSA may be a promising approach to predicting GS upgrade.
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