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

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.

Combined Use of Prostate-specific Antigen Density and Magnetic Resonance Imaging for Prostate Biopsy Decision Planning: A Retrospective Multi-institutional Study Using the Prostate Magnetic Resonance Imaging Outcome Database (PROMOD)

“…Such an approach binds the decision to perform biopsy to the clinical suspicion of PCa to be determined using MRI, risk calculators, and biomarkers [1]. While several risk calculators and biomarkers have been developed and are currently under evaluation [2][3][4], the "MRI pathway" is supported by level 1 evidence, but seems to be limited by the low to moderate inter-reader and -center reproducibility and the low specificity/positive predictive value (PPV) [5][6][7]. Studies have shown that omitting biopsies in patients with no lesion on MRI would miss up to 5-11% of all csPCa cases (ISUP grade >2) [5,8], whereas performing biopsy in all patients with an equivocal MRI finding (PI-RADS 3 lesions considered to be positive by guidelines [9]) can result in a diagnosis of csPCa in 3-50% of the patients [5,8,10,11].…”

The association between sleep duration and prostate cancer

“…A number of factors can be used to build mathematical models for the prediction and diagnosis of PCa, such as age, pentraxin 3, prostate-specific antigen, absence of bladder outlet obstruction, etc. [ 2 , 3 ] While sleep is very important for physical and mental health. [ 4 ] Sleep duration, quality of sleep, and so on might be related to the development of some diseases.…”

Multivariable Models Incorporating Multiparametric Magnetic Resonance Imaging Efficiently Predict Results of Prostate Biopsy and Reduce Unnecessary Biopsy

“…A dozen of nomograms for prediction of PCa and/or clinically significant prostate cancer (CSPCa) with Gleason score (GS) ≥3+4 had been developed in Western countries (6)(7)(8)(9). Researchers found that the models derived from Western populations overestimated the predicated risk of PCa and CSPCa among the East Asian populations, mainly due to the racial differences between two populations (10,11).…”