Prostate cancer aggressive prediction: preponderant diagnostic performances of intravoxel incoherent motion (IVIM) imaging and diffusion kurtosis imaging (DKI) beyond ADC at 3.0 T scanner with gleason score at final pathology
To explore the preponderant diagnostic performances of IVIM and DKI in predicting the Gleason score (GS) of prostate cancer.
Diffusion-weighted imaging data were postprocessed using monoexponential, lVIM and DK models to quantitate the apparent diffusion coefficient (ADC), molecular diffusion coefficient (D), perfusion-related diffusion coefficient (Dstar), perfusion fraction (F), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). Spearman’s rank correlation coefficient was used to explore the relationship between those parameters and the GS, Kruskal–Wallis test, and Mann–Whitney U test were performed to compare the above parameters between the different groups, and a receiver-operating characteristic (ROC) curve was used to analyze the differential diagnosis ability. The interpretation of the results is in view of histopathologic tumor tissue composition.
The area under the ROC curves (AUCs) of ADC, F, D, Dapp, and Kapp in differentiating GS ≤ 3 + 4 and GS > 3 + 4 PCa were 0.744 (95% CI 0.581–0.868), 0.726 (95% CI 0.563–0.855), 0.732 (95% CI 0.569–0.860), and 0.752 (95% CI 0.590–0.875), 0.766 (95% CI 0.606–0.885), respectively, and those in differentiating GS ≤ 7 and GS > 7 PCa were 0.755 (95% CI 0.594–0.877), 0.734 (95% CI 0.571–0.861), 0.724 (95% CI0.560–0.853), and 0.716 (95% CI 0.552–0.847), 0.828 (95% CI 0.676–0.929), respectively. All the P values were less than 0.05. There was no significant difference in the AUC for the detection of different GS groups by using those parameters.
Both the IVIM and DKI models are beneficial to predict GS of PCa and indirectly predict its aggressiveness, and they have a comparable diagnostic performance with each other as well as ADC.
KeywordsProstate cancer Diffusion-weighted imaging Diffusion kurtosis imaging Magnetic resonance imaging Intravoxel incoherent motion Gleason score
The authors of this manuscript state that this work has not received any funding. Thanks are due to the radiologists of GE 750 scanner for their understanding and support of our research work, and to urologist Zhu Yanjun, Long Qilai, and Xulei et al. for their assistance in our research work.
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