Nomogram development and evaluating
REGRESSION
To construct an accurate nomogram that predicts the likelihood of PCa, we carried out multivariable logistic regression analyses to determine the correlation between the relevant factors and PCa. We utilized the forward method in multivariable logistic regression analysis to identify independent predictors, and subsequently, to determine the significant variables for creating nomograms. To assess the risk model’s utility, receiver operator characteristic (ROC) curves were generated and the corresponding area under the curves (AUC) values was compared. To determine the statistical significance between the AUC values, the DeLong method was employed. The Nomogram was developed by randomly selecting 384 (70%) cases, with 164 (30%) cases set aside for validation by the R package “verification”. A nomogram that predicts the risk of PCa was developed based on independent risk factors. The calibration curve was employed to evaluate the consistency of nomogram-predicted probabilities with observed probabilities. To evaluate the net benefit obtained from utilizing the developed nomogram, a decision curve analysis (DCA) was performed. Data were plotted using R software v.4.1.0 (R Foundation, Vienna, Austria) for the nomogram (R package ‘rms’), calibration plot (R package ‘rms’), and DCA (R package ‘rmda’).