Volume 63, Issue 6, Pages (June 2013)

Slides:



Advertisements
Similar presentations
The PCA3 Assay improves the prediction of initial biopsy outcome and may be indicative of prostate cancer aggressiveness de la Taille A, Irani J, Graefen.
Advertisements

Volume 56, Issue 5, Pages (November 2009)
Volume 61, Issue 3, Pages (March 2012)
Volume 50, Issue 3, Pages (September 2006)
The PSA Era is not Over for Prostate Cancer
Volume 54, Issue 2, Pages (August 2008)
The Origin of the Bone Scan as a Tumour Marker in Prostate Cancer
International Neurourology Journal 2013;17:73-77
Volume 59, Issue 1, Pages (January 2011)
Testosterone Therapy in Men With Prostate Cancer
Volume 53, Issue 4, Pages (April 2008)
Prostate Cancer: Highlights from 2006
Volume 49, Issue 2, Pages (February 2006)
Volume 61, Issue 5, Pages (May 2012)
Volume 51, Issue 6, Pages (June 2007)
Control of Prostate Cancer by Transrectal HIFU in 227 Patients
Volume 69, Issue 1, Pages (January 2016)
Volume 70, Issue 2, Pages (August 2016)
Volume 52, Issue 3, Pages (September 2007)
Prostate Cancer Epidemic in Sight?
Volume 52, Issue 4, Pages (October 2007)
Tumour Grade, Treatment, and Relative Survival in a Population-based Cohort of Men with Potentially Curable Prostate Cancer  Sam Ladjevardi, Gabriel Sandblom,
Volume 62, Issue 4, Pages (October 2012)
Volume 63, Issue 6, Pages (June 2013)
Volume 60, Issue 5, Pages (November 2011)
Volume 53, Issue 4, Pages (April 2008)
Towards Early and More Specific Diagnosis of Prostate Cancer
Prostate Cancer Detection: A View of the Future
Diagnostic Strategies for Prostate Cancer
Ongoing Gleason Grade Migration in Localized Prostate Cancer and Implications for Use of Active Surveillance  Adam B. Weiner, Ruth Etzioni, Scott E. Eggener 
Volume 68, Issue 3, Pages (September 2015)
Volume 56, Issue 2, Pages (August 2009)
Low-risk Prostate Cancer: Identification, Management, and Outcomes
Volume 66, Issue 6, Pages (December 2014)
Luis Martínez-Piñeiro  European Urology Supplements 
Prostate Cancer Epidemic in Sight?
The PSA Era is not Over for Prostate Cancer
The Origin of the Bone Scan as a Tumour Marker in Prostate Cancer
Validation of Preoperative Nomograms Predicting Lymph Node Involvement in Prostate Cancer: A Bi-institutional Study  Alexander I. Hinev, Vesselin I. Hadjiev,
Volume 53, Issue 4, Pages (April 2008)
Volume 54, Issue 3, Pages (September 2008)
Volume 61, Issue 2, Pages (February 2012)
Volume 66, Issue 2, Pages (August 2014)
Volume 58, Issue 1, Pages 1-7 (July 2010)
Volume 65, Issue 6, Pages (June 2014)
Volume 68, Issue 4, Pages (October 2015)
Is It Necessary to Detect All Prostate Cancers in Men with Serum PSA Levels
Volume 58, Issue 1, Pages (July 2010)
Volume 70, Issue 2, Pages (August 2016)
Volume 50, Issue 5, Pages (November 2006)
European Urology Oncology
European Urology Oncology
Prostate Cancer Nomograms: An Update
Volume 59, Issue 4, Pages (April 2011)
Jonathan S. Brajtbord, Michael S. Leapman, Matthew R. Cooperberg 
Thomas Steuber, Matthew Frank O'Brien, Hans Lilja  European Urology 
Volume 74, Issue 6, Pages (December 2018)
Volume 54, Issue 5, Pages (November 2008)
Volume 51, Issue 5, Pages (May 2007)
Volume 54, Issue 1, Pages (July 2008)
The Comparability of Models for Predicting the Risk of a Positive Prostate Biopsy with Prostate-Specific Antigen Alone: A Systematic Review  Fritz Schröder,
Edith Canby-Hagino, Javier Hernandez, Timothy C. Brand, Ian Thompson 
Volume 53, Issue 2, Pages (February 2008)
Fernando P. Secin, Fernando J. Bianco, Nicholas T
Volume 74, Issue 6, Pages (December 2018)
Volume 54, Issue 3, Pages (September 2008)
Oncoforum Urology: Prostate Cancer 2008 at a Glance
European Urology Oncology
Volume 52, Issue 5, Pages (November 2007)
Assessing a Patient’s Individual Risk of Biopsy-detectable Prostate Cancer: Be Aware of Case Mix Heterogeneity and A Priori Likelihood  Jan F.M. Verbeek,
Presentation transcript:

Volume 63, Issue 6, Pages 986-994 (June 2013) Serum Isoform [−2]proPSA Derivatives Significantly Improve Prediction of Prostate Cancer at Initial Biopsy in a Total PSA Range of 2–10 ng/ml: A Multicentric European Study  Massimo Lazzeri, Alexander Haese, Alexandre de la Taille, Joan Palou Redorta, Thomas McNicholas, Giovanni Lughezzani, Vincenzo Scattoni, Vittorio Bini, Massimo Freschi, Amy Sussman, Bijan Ghaleh, Philippe Le Corvoisier, Josep Alberola Bou, Salvador Esquena Fernández, Markus Graefen, Giorgio Guazzoni  European Urology  Volume 63, Issue 6, Pages 986-994 (June 2013) DOI: 10.1016/j.eururo.2013.01.011 Copyright © 2013 European Association of Urology Terms and Conditions

Fig. 1 Receiver operating characteristic curves depicting the accuracy of individual predictors of prostate cancer. PSA=prostate-specific antigen; fPSA=free PSA; %fPSA=percentage of free PSA to total PSA; p2PSA=[−2]proPSA; %p2PSA=percentage of [−2]proPSA to free PSA; PHI=Prostate Health Index. European Urology 2013 63, 986-994DOI: (10.1016/j.eururo.2013.01.011) Copyright © 2013 European Association of Urology Terms and Conditions

Fig. 2 Decision curve analysis* of the effect of prediction models on the detection of prostate cancer. The net benefit is plotted against various threshold probabilities. Model 1 is a basic model that includes total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), and percentage of fPSA to tPSA. Model 2 is a basic model that includes all the factors in Model 1 plus [−2]proPSA (p2PSA). Model 3 is a basic model that includes all the factors in Model 1 plus the percentage of p2PSA to fPSA. Model 4 is a basic model that includes all the factors in Model 1 plus the Prostate Health Index. * Decision curve analysis consists of showing graphically the so-called net benefit obtained by applying the strategy of treating an individual if and only if his probability of having the disease is equal to or greater than the determined threshold probability. It facilitates the comparison among alternative prediction models used to calculate probability of disease. Consequently, it may facilitate the choice of which of several prediction models to adopt to have the highest net benefit at the clinician's or patient's personally determined threshold probability. European Urology 2013 63, 986-994DOI: (10.1016/j.eururo.2013.01.011) Copyright © 2013 European Association of Urology Terms and Conditions

Fig. 3 Decision curve analysis of the effect of prediction models on the detection of Gleason score ≥7 disease. The net benefit is plotted against various threshold probabilities. The threshold probability is the minimum probability of prostate cancer at which a patient (or clinician) would opt for intervention. Model 1 is a basic model that includes total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), and percentage of fPSA to tPSA. Model 2 is a basic model that includes all the factors in Model 1 plus [−2]proPSA (p2PSA). Model 3 is a basic model that includes all the factors in Model 1 plus the percentage of p2PSA to fPSA. Model 4 is a basic model that includes all the factors in Model 1 plus the Prostate Health Index. European Urology 2013 63, 986-994DOI: (10.1016/j.eururo.2013.01.011) Copyright © 2013 European Association of Urology Terms and Conditions

European Urology 2013 63, 986-994DOI: (10.1016/j.eururo.2013.01.011) Copyright © 2013 European Association of Urology Terms and Conditions