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

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 0â1000 s/mm2) were prospectively collected and processed by a monoexponential (DWI) model and compared with a bie...

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Main Authors: Jie Bao, Ximing Wang, Chunhong Hu, Jianquan Hou, Fenglin Dong, Lingchuan Guo
Format: Article
Language:English
Published: Elsevier 2017-01-01
Series:European Journal of Radiology Open
Online Access:http://www.sciencedirect.com/science/article/pii/S2352047717300230
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spelling doaj-9a225b3c31744cadb6df0d59e1b3efa42020-11-24T21:42:00ZengElsevierEuropean Journal of Radiology Open2352-04772017-01-014123128Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRIJie Bao0Ximing Wang1Chunhong Hu2Jianquan Hou3Fenglin Dong4Lingchuan Guo5Department of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, ChinaDepartment of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, ChinaDepartment of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China; Corresponding author.Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, 215000, ChinaDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, ChinaObjective: 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 0â1000 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 = â0.522 and r = â0.407 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: Prostate cancer, Prostate biopsy, DWI, IVIM, MR/TRUS, Transition zonehttp://www.sciencedirect.com/science/article/pii/S2352047717300230
collection DOAJ
language English
format Article
sources DOAJ
author Jie Bao
Ximing Wang
Chunhong Hu
Jianquan Hou
Fenglin Dong
Lingchuan Guo
spellingShingle Jie Bao
Ximing Wang
Chunhong Hu
Jianquan Hou
Fenglin Dong
Lingchuan Guo
Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI
European Journal of Radiology Open
author_facet Jie Bao
Ximing Wang
Chunhong Hu
Jianquan Hou
Fenglin Dong
Lingchuan Guo
author_sort Jie Bao
title Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI
title_short Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI
title_full Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI
title_fullStr Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI
title_full_unstemmed Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI
title_sort differentiation of prostate cancer lesions in the transition zone by diffusion-weighted mri
publisher Elsevier
series European Journal of Radiology Open
issn 2352-0477
publishDate 2017-01-01
description 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 0â1000 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 = â0.522 and r = â0.407 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: Prostate cancer, Prostate biopsy, DWI, IVIM, MR/TRUS, Transition zone
url http://www.sciencedirect.com/science/article/pii/S2352047717300230
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