Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI

Jie Bao; Ximing Wang; Chunhong Hu; Jianquan Hou; Fenglin Dong; Lingchuan Guo

doi:10.1016/j.ejro.2017.08.003

Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI

Eur J Radiol Open. 2017 Sep 29:4:123-128. doi: 10.1016/j.ejro.2017.08.003. eCollection 2017.

Authors

Jie Bao¹, Ximing Wang¹, Chunhong Hu¹, Jianquan Hou², Fenglin Dong³, Lingchuan Guo⁴

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China.
² Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China.
³ Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China.
⁴ Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China.

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/mm²) 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.

Keywords: ADC, apparent diffusion coefficient; AUC, Area under the curve; DCE, dynamic contrast-enhanced imaging; DWI; DWI, diffusion-weighted imaging; IVIM; IVIM, intravoxel incoherent motion; MR/TRUS; MR/TRUS, magnetic resonance/transrectal ultrasound; MRS, magnetic resonance spectroscopy; PCa, prostate cancer; PZ, peripheral zone; Prostate biopsy; Prostate cancer; ROI, region of interest; T1-VIBE, T1-weighted volumetric interpolated breath-hold examination; T1WI, T1-weighed imaging; T2WI, T2-weighted imaging; TZ, transition zone; Transition zone; mpMRI, multiparametric magnetic resonance imaging.