1. 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 (June 2013)
DOI: /j.eururo
Copyright © 2013 European Association of Urology Terms and Conditions

2. 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  , DOI: ( /j.eururo )
Copyright © 2013 European Association of Urology Terms and Conditions

3. 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  , DOI: ( /j.eururo )
Copyright © 2013 European Association of Urology Terms and Conditions

4. 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  , DOI: ( /j.eururo )
Copyright © 2013 European Association of Urology Terms and Conditions

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