Objective To evaluate the risk predictive value of preoperative biochemical indica-tors for prostate cancer(PCa)and to construct a nomogram model.Methods The clinical data of patients undergoing perineal prostate biopsy in the First Affiliated Hospital of Soochow University from September 2021 to December 2022 were analyzed retrospectively.A total of 479 patients were included,among which 340 were used for model training and 139 were used as an independent exter-nal validation set.Based on different pathological results,the training patients were divided into two groups,i.e.,the positive group with 170 PCa cases and the negative group with 170 prostate hyper-plasia cases.The independent risk factors in PCa were screened using Logistic univariate and multi-variate analyses,and a nomogram was constructed.The reliability of the model was validated by re-ceiver operating characteristic(ROC)curve analysis.Results Age,free prostate-specific antigen total Prostale-specific antigen(f/tPSA),prostate volume(PV),prostate imaging reporting and data system,serum phosphorus(P)and γ-glutamyl transpeptadase(GGT)between negative and positive groups were identified as independent risk factors in PCa(P＜0.05).A nomogram was constructed from Logistic multi-factor analysis for PCa risk prediction.The internal validation indicated the high fitting of the model(mean absolute error=0.011,n=340),and the external verification showed that the curve was close to the ideal line(mean absolute error=0.045,n=139).ROC curves showed the highest overall area under the curve(AUC)of the whole model(AUC=0.874)and it had better benefits compared with the baseline model and each single factor model.Further decision curve and clinical impact curve showed that the clinical benefit of the whole model was higher than that of other models.Conclusions The model for PCa risk prediction constructed by integrating serum P,GGT,age,f/tPSA,PV and PI-RADS showed good efficacy for PCa detection.
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