Development and Internal Validation of Novel Nomograms Based on Benign Prostatic Obstruction-Related Parameters to Predict the Risk of Prostate Cancer at First Prostate Biopsy - Cormio, Luigi; Cindolo, Luca; Troiano, Francesco; Marchioni, Michele; Di Fino, Giuseppe; Mancini, Vito; Falagario, Ugo; Selvaggio, Oscar; Sanguedolce, Francesca; Fortunato, Francesca; Schips, Luigi; Carrieri, Giuseppe (Frontiers in Oncology, 2018)
We aimed to develop novel nomograms for prostate cancer patient risk stratification by testing the ability of benign prostatic obstruction-related parameters to predict first prostate biopsy (PBx) outcome. The ability of age, PSA, digital rectal examination (DRE), prostate volume (PVol), post-void residual urinary volume (PVR) and peak flow rate (PFR) in predicting prostate cancer (PCa) and clinically-significant Pca (CSPCa) in patients scheduled for first PBx was tested by univariable and multivariable logistic regression analysis. Given their high risk of harboring PCa, patients with PSA>20ng/mL were excluded. The predictive accuracy of the multivariate models was assessed using receiver operator characteristic (ROC) curves analysis, calibration plot, and decision-curve analyses (DCAs). Nomograms predicting PCa and CSPCa were built using the coefficients of the logit function. Between January 2006 and May 2017, 3461 patients underwent PBx; 2577 met the inclusion criteria. Multivariable logistic regression analysis showed that all variables but PFR significantly predicted PCA and CSPCa. The addition of the BPO-related variables PVol and PVR to a model based on age, PSA and DRE findings increased the model predictive accuracy from 0.664 to 0.768 for PCa and from 0.7365 to 0.8002 for CSPCa. Calibration plot demonstrated excellent models’ concordance. DCA demonstrated that the model predicting PCa is of value between ~15% and ~80% threshold probabilities, whereas the one predicting CSPCa is of value between ~10% and ~60% threshold probabilities. In conclusion, this study provides grounds for including PVR and PVol in models predicting the outcome of first PBx.