You have accessJournal of UrologyProstate Cancer: Detection and Screening II1 Apr 2015MP60-18 PCP-SMART STUDY AND PCRD INDEX: INTRODUCTION AND RESULTS OF A NOVEL MATHEMATICAL SIMULATION MODELING METHOD, DEVISED TO PREDICT THE OUTCOME OF PROSTATE BIOPSY. Evangelos Spyropoulos, Dimitrios Kotsiris, Aggelos Panagopoulos, Stamatios Mavrikos, Evangelos Hatziplis, and Ioannis Galanakis Evangelos SpyropoulosEvangelos Spyropoulos More articles by this author , Dimitrios KotsirisDimitrios Kotsiris More articles by this author , Aggelos PanagopoulosAggelos Panagopoulos More articles by this author , Stamatios MavrikosStamatios Mavrikos More articles by this author , Evangelos HatziplisEvangelos Hatziplis More articles by this author , and Ioannis GalanakisIoannis Galanakis More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2220AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES To introduce the PCP-SMART study (Prostate Cancer Predictive Simulation Modeling, Assessing Risk, Technique)and its derivative PCRDIndex (Prostate Cancer Risk Determinator), aiming to predict the probability of prostate cancer (PCa) in patients undergoing prostate biopsy. METHODS PCP-SMART was applied to 371 men undergoing prostate biopsy. Model development was based on the facts: tPSA correlates to age and prostate volume, f/tPSA is established predictor of PCa, tPSA >50ng/ml has 98,5% PPV for PCa diagnosis. We hypothesized that the correlation of two variables, each consisting of three ratios such as: PSA/age – PSA/prostate volume[PSAD] – Free/total PSA, with one including patient's tPSA value and the other tPSA value 50ng/ml, could operate as a “PCa conditions imitating - simulating model”. Linear regression of these variables derived the coefficient of determination Rsquare [signed + or - (equation slope)] considered potent estimator of the probability of biopsy outcome and was termed PCRD index. Statistics to quantify model's predictive ability were performed using SPSS-22 including chi-square test, logistic regression analysis with equation formation for predicting probability of positive biopsy, calculation of sensitivity, specificity, PPV, NPV, likelihood ratio, accuracy and AUC-ROC curve analysis, p<0.05 statistically significant. RESULTS Histologic examination was PCa(+) in 167 (45,1%) patients, negative in 164 (44,2%) while, in 40 (10,7%) it showed HGPIN/ASAP. PCRD index(+) was found in 89,82% PCa(+)and 10,18% PCa(-) cases while it was negative in 91,46% PCa(-) and 9,54% PCa(+) patients (chi-square p<0,001- RR:8,98). Sensitivity was 89,8%, specificity 91,5%, PPV 91,5%, NPV 89,8%, LR(+) 10,5, LR(-) 0,11 and accuracy 90,6%. Multiple logistic regression demonstrated PCRD index (p<0,001) and prostate volume (p=0,039) to be significant predictors of PCa diagnosis while, the logistic regression equation formed, predicted with 91% accuracy the probability of PCa(+) biopsy outcome. ROC curve analysis showed PCRD index AUC [0,926] was significantly greater (p<0,001) vs PSAD, prostate volume, f/tPSA, fPSA,age,tPSA. CONCLUSIONS PCRD index predicted with high accuracy biopsy outcome, identifying correctly 9 in 10 patients with prostate cancer as well as, 9 in 10 without PCa. Its predictive power was significantly higher compared to established PCa predictors while, the logistic regression equation, calculated accurately the probability of PCa positive prostate biopsy outcome. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e745-e746 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Evangelos Spyropoulos More articles by this author Dimitrios Kotsiris More articles by this author Aggelos Panagopoulos More articles by this author Stamatios Mavrikos More articles by this author Evangelos Hatziplis More articles by this author Ioannis Galanakis More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...