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Invest Radiol. 2019 Jul;54(7):437-447. doi: 10.1097/RLI.0000000000000558.

A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach.

Author information

Cancer Research UK Cambridge institute, Cambridge, United Kingdom.
Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Mannheim, Germany.
Department of Medical Imaging, University of Toronto, Lunenfeld Tanenbaum Research Institute, Sinai Health System, Ontario Institute of Cancer Research, Toronto, Canada.
Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna - General Hospital, Vienna, Austria.
Siemens Healthcare GmbH, MR Application Development, Erlangen, Germany.
Russel H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD.
Prostate MRI and Abdominal Imaging Service, Weill Cornell Medicine, Weill Cornell Imaging, New York-Presbyterian, NY.
Paul Strickland Scanner Centre, Mount Vernon Cancer Center, London, United Kingdom.
Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Radiology and Nuclear Medicine, Ghent University Hospital, Gent, Belgium.



The aims of this study were to assess the discriminative performance of quantitative multiparametric magnetic resonance imaging (mpMRI) between prostate cancer and noncancer tissues and between tumor grade groups (GGs) in a multicenter, single-vendor study, and to investigate to what extent site-specific differences affect variations in mpMRI parameters.


Fifty patients with biopsy-proven prostate cancer from 5 institutions underwent a standardized preoperative mpMRI protocol. Based on the evaluation of whole-mount histopathology sections, regions of interest were placed on axial T2-weighed MRI scans in cancer and noncancer peripheral zone (PZ) and transition zone (TZ) tissue. Regions of interest were transferred to functional parameter maps, and quantitative parameters were extracted. Across-center variations in noncancer tissues, differences between tissues, and the relation to cancer grade groups were assessed using linear mixed-effects models and receiver operating characteristic analyses.


Variations in quantitative parameters were low across institutes (mean [maximum] proportion of total variance in PZ and TZ, 4% [14%] and 8% [46%], respectively). Cancer and noncancer tissues were best separated using the diffusion-weighted imaging-derived apparent diffusion coefficient, both in PZ and TZ (mean [95% confidence interval] areas under the receiver operating characteristic curve [AUCs]; 0.93 [0.89-0.96] and 0.86 [0.75-0.94]), followed by MR spectroscopic imaging and dynamic contrast-enhanced-derived parameters. Parameters from all imaging methods correlated significantly with tumor grade group in PZ tumors. In discriminating GG1 PZ tumors from higher GGs, the highest AUC was obtained with apparent diffusion coefficient (0.74 [0.57-0.90], P < 0.001). The best separation of GG1-2 from GG3-5 PZ tumors was with a logistic regression model of a combination of functional parameters (mean AUC, 0.89 [0.78-0.98]).


Standardized data acquisition and postprocessing protocols in prostate mpMRI at 3 T produce equivalent quantitative results across patients from multiple institutions and achieve similar discrimination between cancer and noncancer tissues and cancer grade groups as in previously reported single-center studies.

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