title: Prostate Cancer Predictive Simulation Modelling, Assessing the Risk Technique (PCP-SMART): Introduction and Initial Clinical Efficacy Evaluation Data Presentation of a Simple Novel Mathematical Simulation Modelling Method, Devised to Predict the Outcome of Prostate Biopsy on an Individual Basis.
abstract: We developed a mathematical "prostate cancer (PCa) conditions simulating" predictive model (PCP-SMART), from which we derived a novel PCa predictor (prostate cancer risk determinator [PCRD] index) and a PCa risk equation. We used these to estimate the probability of finding PCa on prostate biopsy, on an individual basis.
  
  
  
  A total of 371 men who had undergone transrectal ultrasound-guided prostate biopsy were enrolled in the present study. Given that PCa risk relates to the total prostate-specific antigen (tPSA) level, age, prostate volume, free PSA (fPSA), fPSA/tPSA ratio, and PSA density and that tPSA ≥ 50 ng/mL has a 98.5% positive predictive value for a PCa diagnosis, we hypothesized that correlating 2 variables composed of 3 ratios (1, tPSA/age; 2, tPSA/prostate volume; and 3, fPSA/tPSA; 1 variable including the patient's tPSA and the other, a tPSA value of 50 ng/mL) could operate as a PCa conditions imitating/simulating model. Linear regression analysis was used to derive the coefficient of determination (R2), termed the PCRD index. To estimate the PCRD index's predictive validity, we used the χ2test, multiple logistic regression analysis with PCa risk equation formation, calculation of test performance characteristics, and area under the receiver operating characteristic curve analysis using SPSS, version 22 (P < .05).
  
  
  
  The biopsy findings were positive for PCa in 167 patients (45.1%) and negative in 164 (44.2%). The PCRD index was positively signed in 89.82% positive PCa cases and negative in 91.46% negative PCa cases (χ2test; P < .001; relative risk, 8.98). The sensitivity was 89.8%, specificity was 91.5%, positive predictive value was 91.5%, negative predictive value was 89.8%, positive likelihood ratio was 10.5, negative likelihood ratio was 0.11, and accuracy was 90.6%. Multiple logistic regression revealed the PCRD index as an independent PCa predictor, and the formulated risk equation was 91% accurate in predicting the probability of finding PCa. On the receiver operating characteristic analysis, the PCRD index (area under the curve, 0.926) significantly (P < .001) outperformed other, established PCa predictors.
  
  
  
  The PCRD index effectively predicted the prostate biopsy outcome, correctly identifying 9 of 10 men who were eventually diagnosed with PCa and correctly ruling out PCa for 9 of 10 men who did not have PCa. Its predictive power significantly outperformed established PCa predictors, and the formulated risk equation accurately calculated the probability of finding cancer on biopsy, on an individual patient basis.
authors.affiliation: Urology Department, Naval and Veterans Hospital of Athens, Athens, Greece. Electronic address: bagxar@hol.gr.
authors.forename: Evangelos
authors.initials: E
authors.lastname: Spyropoulos
authors.affiliation: Urology Department, Naval and Veterans Hospital of Athens, Athens, Greece.
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Kotsiris
authors.affiliation: School of Dentistry, University of Athens, Athens, Greece.
authors.forename: Katherine
authors.initials: K
authors.lastname: Spyropoulos
authors.affiliation: Urology Department, Naval and Veterans Hospital of Athens, Athens, Greece.
authors.forename: Aggelos
authors.initials: A
authors.lastname: Panagopoulos
authors.affiliation: Urology Department, Naval and Veterans Hospital of Athens, Athens, Greece.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Galanakis
authors.affiliation: Urology Department, Naval and Veterans Hospital of Athens, Athens, Greece.
authors.forename: Stamatios
authors.initials: S
authors.lastname: Mavrikos
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D006801:Humans; D061705:Image-Guided Biopsy; D007610:Kallikreins / Q000378:metabolism; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D017430:Prostate-Specific Antigen / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D012372:ROC Curve; D012680:Sensitivity and Specificity; D018084:Ultrasonography, Interventional / Q000379:methods*
chemical_list: C549870:KLK3 protein, human; D007610:Kallikreins; D017430:Prostate-Specific Antigen
keywords: PCP-SMART model; PCRD index; PSA testing; Prostate cancer prediction; Prostate cancer risk mathematical equation