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

Frontiers in Oncology

Francesca Fortunato¹  Francesca Sanguedolce²  Ugo Falagario³  Francesco Troiano³  Giuseppe Carrieri³  Oscar Selvaggio³  Luigi Cormio³  Giuseppe Di Fino³  Vito Mancini³  Luca Cindolo⁴  Luigi Schips⁵  Michele Marchioni⁵ 

[1] Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy;Department of Pathology, University of Foggia, Foggia, Italy;Department of Urology and Renal Transplantation, University of Foggia, Foggia, Italy;Department of Urology, ASL, Chieti, Italy;Department of Urology, SS Annunziata Hospital, “G.D'Annunzio” University of Chieti, Chieti, Italy;

关键词: prostate biopsy;    prostate cancer;    nomogram;    lower urinary tract symptoms;    prostate volume;   

DOI  :  10.3389/fonc.2018.00438

来源: DOAJ

【 摘 要 】

The present study aimed to determine the ability of novel nomograms based onto readily-available clinical parameters, like those related to benign prostatic obstruction (BPO), in predicting the outcome of first prostate biopsy (PBx). To do so, we analyzed our Internal Review Board-approved prospectively-maintained PBx database. Patients with PSA>20 ng/ml were excluded because of their high risk of harboring prostate cancer (PCa). A total of 2577 were found to be eligible for study analyses. 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 PCa and clinically-significant PCa (CSPCa)was tested by univariable and multivariable logistic regression analysis. The predictive accuracy of the multivariate models was assessed using receiver operator characteristic curves analysis, calibration plot, and decision-curve analyses (DCA). Nomograms predicting PCa and CSPCa were built using the coefficients of the logit function. 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, our novel nomograms including PVR and PVol significantly increased the accuracy of the model based on age, PSA and DRE in predicting PCa and CSPCa at first PBx. Being based onto parameters commonly assessed in the initial evaluation of men “prostate health,” these novel nomograms could represent a valuable and easy-to-use tool for physicians to help patients to understand their risk of harboring PCa and CSPCa.

【 授权许可】

Unknown