Abstract
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.
Highlights
Prostate biopsy (PBx) is the standard method for diagnosing prostate cancer (PCa) but the diagnostic yield of this procedure remains low
We demonstrated that an elevated post-void residual urinary volume (PVR) and the absence of bladder outlet obstruction (BOO), as assessed by a peak flow rate (PFR) of >10 mL/s, are independent predictors of PBx outcome [10, 11]. Since these simple non-invasive parameters are commonly assessed in the initial evaluation of men “prostate health,” in the present study we aimed to assess whether the addition of PFR and PVR to a multivariate logistic regression model based on standard clinical parameters could increase the model predictive accuracy
We found that the addition of the benign prostatic obstruction (BPO)-related variables prostate volume (PVol) and PVR to a model based on age, prostate-specific antigen (PSA) and digital rectal examination (DRE) findings increased the model predictive accuracy from 0.664 to 0.768 for PCa and from 0.7365 to 0.8002 for clinicallysignificant PCa (CSPCa)
Summary
Prostate biopsy (PBx) is the standard method for diagnosing prostate cancer (PCa) but the diagnostic yield of this procedure remains low. A recent meta-analysis [3] demonstrated that some but not all the most common PCa risk prediction models perform better than serum PSA in predicting PCa diagnosis Novel biomarkers, such as the precursor isoform [-2]proPSA (p2PSA) and the Prostate Cancer Antigen 3 (PCA3), perform better than serum PSA and have further, but not dramatically, increased the accuracy of PCa risk prediction models [4, 5]. Multiparametric magnetic resonance imaging (mpMRI) of the prostate has been suggested to improve PBx diagnostic yield; it does not dramatically increase the accuracy of PCa risk prediction models [6,7,8] and is not recommended in the setting of first PBx [9]
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