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Moldovan PC, Van den Broeck T, Sylvester R, Marconi L, Bellmunt J, van den Bergh RCN, Bolla M, Briers E, Cumberbatch MG, Fossati N, Gross T, Henry AM, Joniau S, van der Kwast TH, Matveev VB, van der Poel HG, De Santis M, Schoots IG, Wiegel T, Yuan CY, Cornford P, Mottet N, Lam TB, Rouvière O. What Is the Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in Excluding Prostate Cancer at Biopsy? A Systematic Review and Meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel. Eur Urol 2017; 72:250-266. [PMID: 28336078 DOI: 10.1016/j.eururo.2017.02.026] [Citation(s) in RCA: 270] [Impact Index Per Article: 38.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 02/16/2017] [Indexed: 11/16/2022]
Abstract
CONTEXT It remains unclear whether patients with a suspicion of prostate cancer (PCa) and negative multiparametric magnetic resonance imaging (mpMRI) can safely obviate prostate biopsy. OBJECTIVE To systematically review the literature assessing the negative predictive value (NPV) of mpMRI in patients with a suspicion of PCa. EVIDENCE ACQUISITION The Embase, Medline, and Cochrane databases were searched up to February 2016. Studies reporting prebiopsy mpMRI results using transrectal or transperineal biopsy as a reference standard were included. We further selected for meta-analysis studies with at least 10-core biopsies as the reference standard, mpMRI comprising at least T2-weighted and diffusion-weighted imaging, positive mpMRI defined as a Prostate Imaging Reporting Data System/Likert score of ≥3/5 or ≥4/5, and results reported at patient level for the detection of overall PCa or clinically significant PCa (csPCa) defined as Gleason ≥7 cancer. EVIDENCE SYNTHESIS A total of 48 studies (9613 patients) were eligible for inclusion. At patient level, the median prevalence was 50.4% (interquartile range [IQR], 36.4-57.7%) for overall cancer and 32.9% (IQR, 28.1-37.2%) for csPCa. The median mpMRI NPV was 82.4% (IQR, 69.0-92.4%) for overall cancer and 88.1% (IQR, 85.7-92.3) for csPCa. NPV significantly decreased when cancer prevalence increased, for overall cancer (r=-0.64, p<0.0001) and csPCa (r=-0.75, p=0.032). Eight studies fulfilled the inclusion criteria for meta-analysis. Seven reported results for overall PCa. When the overall PCa prevalence increased from 30% to 60%, the combined NPV estimates decreased from 88% (95% confidence interval [95% CI], 77-99%) to 67% (95% CI, 56-79%) for a cut-off score of 3/5. Only one study selected for meta-analysis reported results for Gleason ≥7 cancers, with a positive biopsy rate of 29.3%. The corresponding NPV for a cut-off score of ≥3/5 was 87.9%. CONCLUSIONS The NPV of mpMRI varied greatly depending on study design, cancer prevalence, and definitions of positive mpMRI and csPCa. As cancer prevalence was highly variable among series, risk stratification of patients should be the initial step before considering prebiopsy mpMRI and defining those in whom biopsy may be omitted when the mpMRI is negative. PATIENT SUMMARY This systematic review examined if multiparametric magnetic resonance imaging (MRI) scan can be used to reliably predict the absence of prostate cancer in patients suspected of having prostate cancer, thereby avoiding a prostate biopsy. The results suggest that whilst it is a promising tool, it is not accurate enough to replace prostate biopsy in such patients, mainly because its accuracy is variable and influenced by the prostate cancer risk. However, its performance can be enhanced if there were more accurate ways of determining the risk of having prostate cancer. When such tools are available, it should be possible to use an MRI scan to avoid biopsy in patients at a low risk of prostate cancer.
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Affiliation(s)
- Paul C Moldovan
- Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Hôpital Edouard Herriot, Lyon, France
| | - Thomas Van den Broeck
- Department of Urology, University Hospitals Leuven, Leuven, Belgium; Laboratory of Molecular Endocrinology, KU Leuven, Leuven, Belgium
| | - Richard Sylvester
- European Association of Urology Guidelines Office, Brussels, Belgium
| | - Lorenzo Marconi
- Department of Urology, Coimbra University Hospital, Coimbra, Portugal
| | - Joaquim Bellmunt
- Bladder Cancer Center, Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Michel Bolla
- Department of Radiation Therapy, CHU Grenoble, Grenoble, France
| | | | | | - Nicola Fossati
- Division of Oncology/Unit of Urology, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, Milan, Italy
| | - Tobias Gross
- Department of Urology, University of Bern, Inselspital, Bern, Switzerland
| | - Ann M Henry
- Leeds Cancer Centre, St. James's University Hospital and University of Leeds, Leeds, UK
| | - Steven Joniau
- Department of Urology, University Hospitals Leuven, Leuven, Belgium; Laboratory of Molecular Endocrinology, KU Leuven, Leuven, Belgium
| | | | | | - Henk G van der Poel
- Department of Urology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Ivo G Schoots
- Department of Radiology & Nuclear Medicine, Erasmus MCUniversity Medical Center, Rotterdam, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Thomas Wiegel
- Department of Radiation Oncology, University Hospital Ulm, Ulm, Germany
| | - Cathy Yuhong Yuan
- Division of Gastroenterology and Cochrane UGPD Group, Department of Medicine, Health Sciences Centre, McMaster University, Hamilton, Canada
| | - Philip Cornford
- Royal Liverpool and Broadgreen Hospitals NHS Trust, Liverpool, UK
| | - Nicolas Mottet
- Department of Urology, University Hospital, St. Etienne, France
| | - Thomas B Lam
- Academic Urology Unit, University of Aberdeen, Aberdeen, UK; Department of Urology, Aberdeen Royal Infirmary, Aberdeen, UK
| | - Olivier Rouvière
- Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Hôpital Edouard Herriot, Lyon, France; Université Lyon 1, faculté de médecine Lyon Est, Lyon, France.
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Final Gleason score prediction using discriminant analysis and support vector machine based on preoperative multiparametric MR imaging of prostate cancer at 3T. BIOMED RESEARCH INTERNATIONAL 2014; 2014:690787. [PMID: 25544944 PMCID: PMC4269213 DOI: 10.1155/2014/690787] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Revised: 09/10/2014] [Accepted: 09/12/2014] [Indexed: 12/11/2022]
Abstract
Objective. This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters. Materials and Methods. Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation. Results. Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively. Conclusion. SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.
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