Prognostic Analysis Of Prostate Cancer Combined With PI-RADS Score And Nomogram Model Construction

Objective:To investigate the independent prognostic factors of prostate cancer(PCa)patients based on prostate imaging-reporting and data system(PI-RADS)and other indicators,and to establish a prediction model and validate it.Methods:The case data of patients with pathologically confirmed prostate cancer from 2014 to 2020 were collected,and the case data were randomly divided into training set and validation set in a 7:3 ratio using RStudio.After performing univariate analysis of all variables,the selected variables were included in multivariate Cox regression analysis to pick out independent risk factors and build prediction models for them.The predictive discrimination of the model was evaluated by C-index and ROC curve,the predictive accuracy of the model was evaluated by calibration curve,and the clinical practicability of the model was evaluated by Decision Curve Analysis.Results:A total of 436 patients with prostate cancer were enrolled,and Cox multivariate regression analysis showed the following independent risk factors: PI-RADS [adjusted hazard ratio(a HR): 2.03;95% CI: 1.56 – 2.63],prostate volume(a HR: 1.27;95% CI:1.04 – 1.55),prostate specific antigen density(a HR: 0.80;95% CI: 0.61 – 1.06),presence of metastasis(a HR: 0.08;95% CI: 0.03 – 0.19),T stage(a HR: 3.88;95% CI: 2.56 –5.89),and pathological Gleason score(a HR: 1.42;95% CI: 1.09 – 1.85).A nomogram model was constructed and validated according to the above six independent risk factors.Conclusion:PI-RADS score,prostate volume,PSAD,T stage,pathological Gleason score and metastasis are independent risk factors affecting the prognosis of prostate cancer patients.The nomogram constructed by these independent risk factors can be used to predict the prognosis of prostate cancer patients.