Prostate Cancer Gene 3 (PCA3): Development and Internal Validation of a Novel Biopsy Nomogram |
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Authors: | Felix K. Chun Alexandre de la Taille Hendrik van Poppel Michael Marberger Arnulf Stenzl Peter F.A. Mulders Hartwig Huland Clement-Claude Abbou Alexander B. Stillebroer Martijn P.M.Q. van Gils Jack A. Schalken Yves Fradet Leonard S. Marks William Ellis Alan W. Partin Alexander Haese |
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Affiliation: | 1. Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany;2. Martini Clinic, Prostate Cancer Center, University Hospital Eppendorf, Hamburg, Germany;3. Hopital Henri Mondor, Créteil, France;4. Universitair Ziekenhuis Gasthuisberg, Leuven, Belgium;5. University of Vienna, Vienna, Austria;6. Uniklinikum Tübingen, Tübingen, Germany;g Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands;h Department of Urology, Universite Laval, Quebec City, Quebec, Canada;i Urological Sciences Research Foundation, Culver City, CA, USA;j University of Washington Medical Center, Seattle, WA, USA;k Department of Urology, Johns Hopkins University Medical Institution, Baltimore, MD, USA |
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Abstract: | BackgroundUrinary prostate cancer gene 3 (PCA3) represents a promising novel marker of prostate cancer detection.ObjectiveTo test whether urinary PCA3 assay improves prostate cancer (PCa) risk assessment and to construct a decision-making aid in a multi-institutional cohort with pre–prostate biopsy data.Design, setting, and participantsPCA3 assay cut-off threshold analyses were followed by logistic regression models which used established predictors to assess PCa-risk at biopsy in a large multi-institutional data set of 809 men at risk of harboring PCa.MeasurementsRegression coefficients were used to construct four sets of nomograms. Predictive accuracy (PA) estimates of biopsy outcome predictions were quantified using the area under the curve of the receiver operator characteristic analysis in models with and without PCA3. Bootstrap resamples were used for internal validation and to reduce overfit bias. The extent of overestimation or underestimation of the observed PCa rate at biopsy was explored graphically using nonparametric loss-calibration plots. Differences in PA were tested using the Mantel-Haenszel test. Finally, nomogram-derived probability cut-offs were tested to assess the ability to identify patients with or without PCa.Results and limitationsPCA3 was identified as a statistically independent risk factor of PCa at biopsy. Addition of a PCA3 assay improved bootstrap-corrected multivariate PA of the base model between 2% and 5%. The highest increment in PA resulted from a PCA3 assay cut-off threshold of 17, where a 5% gain in PA (from 0.68 to 0.73, p = 0.04) was recorded. Nomogram probability–derived risk cut-off analyses further corroborate the superiority of the PCA3 nomogram over the base model.ConclusionsPCA3 fulfills the criteria for a novel marker capable of increasing PA of multivariate biopsy models. This novel PCA3-based nomogram better identifies men at risk of harboring PCa and assists in deciding whether further evaluation is necessary. |
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Keywords: | Prostate biopsy Prostate cancer gene 3 Biomarker Prostate cancer Nomogram Risk assessment |
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