Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI. (2017)

• Record Type: Journal Article
• Authors: Bao, Jie; Wang, Ximing; Hu, Chunhong; Hou, Jianquan; Dong, Fenglin; Guo, Lingchuan
• Is Part Of: European journal of radiology open. Volume 4(2017)
• Page Start: 123
• Page End: 128
• Publication Date: 2017
• Subjects: ADC apparent diffusion coefficient -- AUC Area under the curve -- DCE dynamic contrast-enhanced imaging -- DWI diffusion-weighted imaging -- IVIM intravoxel incoherent motion -- ADC apparent diffusion coefficient -- MRS magnetic resonance spectroscopy -- MR/TRUS magnetic resonance/transrectal ultrasound -- mpMRI multiparametric magnetic resonance imaging -- PCa prostate cancer -- PZ peripheral zone -- ROI region of interest -- T1-VIBE T1-weighted volumetric interpolated breath-hold examination -- T1WI T1-weighed imaging -- T2WI T2-weighted imaging -- TZ transition zone; Prostate cancer -- Prostate biopsy -- DWI -- IVIM -- MR/TRUS -- Transition zone; Medical radiology -- Periodicals; 616.075705
• DOI: 10.1016/j.ejro.2017.08.003
• Languages: English
• ISSNs: 2352-0477

Abstract

Abstract: Objective: To differentiate prostate cancer lesions in transition zone by diffusion-weighted-MRI (DW-MRI). Methods: Data from a total of 63 patients who underwent preoperative DWI (b of 0–1000 s/mm 2 ) were prospectively collected and processed by a monoexponential (DWI) model and compared with a biexponential (IVIM) model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analyses were performed by outlining entire-tumor regions of interest (ROIs). These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on histopathological analysis of Magnetic Resonance/transrectal Ultrasound (MR/TRUS) fusion-guided biopsy. The diagnostic ability of differentiate the PCa from BHP in TZ was analyzed by ROC regression. Histogram analysis of quantitative parameters and Gleason score were assessed with Spearman correlation. Results: Thirty (30 foci) cases of PCa in PZ and 33 (36 foci) cases of BPH were confirmed by pathology. Mean ADC, median ADC, 10th percentile ADC, 90th percentile ADC, kurtosis and skewness of ADC and mean D values, median D and 90th percentile D differed significantly between PCa and BHP in TZ. The highest classification accuracy was achieved by the mean ADC (0.841) and mean D (0.809). A logistic regression model based on mean ADC and mean D led to an AUC of 0.873, however, the difference is not significant. There were 7 Gleason 6 areas, 9 Gleason 7 areas, 8 Gleason 8 areas, 5 Gleason 9 areas and 2 Gleason 10 areas detected from the 31 prostate cancer areas, the mean Gleason value was(7.5 ± 1.2). The mean ADC and mean D had correlation with Gleason score(r = −0.522 and r = −0.407 respectively, P < 0.05). Conclusion: The diagnosis efficiency of IVIM parameters was not superior to ADC in the diagnosis of PCa in TZ. Moreover, the combination of mean ADC and mean D did not perform better than the parameters alone significantly; It is feasible to stratify the pathological grade of prostate cancer by mean ADC.