Objective To develop a predictive model for prostate biopsy outcome with high accuracy. Methods Informations including age, digital rectum examnation result (DRE), prostate specific antigen (PSA), percentfree PSA,prostate volume, transrectum ultrasound findings (TRUS), prostate cancer antigen 3 (PCA3), TMPRSS2-ERG gene fusion, CTAGE5-KHDRBS3 gene fusion and USP9Y-TTTY15 gene fusion were collected by prospective analysis from 100 cases who had been performed prostate biopsy with prostate specific antigen lower than 20 ng/mL.Univariable and multivariable logistic regression analysis was used to develop regression model and nomogram. Receiver operating characteristic curve analysis was used to evaluate its predictive accuracy. Results Five independent predictors of prostate cancer were identified: age, PSA, prostate volume, PCA3 and TMPRSS2-ERG gene fusion. A model topredict prostate biopsy outcome was developed using these five variables. The area under the ROC curve for this model was 0.897. Conclusions Prostate cancer antigen 3 combined with TMPRSS2-ERG gene fusion can highly improve the accuracy of models to predict prostate biopsy outcome, model which contains new tumor markers can be used to guide decision before prostate biopsy.
