Construction and validation of a prostatic cancer risk prediction model

Abstract:

Objective To screen the influencing factors of the incidence of prostatic cancer（PCa），build a risk prediction model for PCa and validate it.Methods Based on the Prostatic Cancer Early Warning Data Set of National Clinical Medical Science Data Center，and after the data processing， the data were randomly divided into a modeling group and a verification group according to 7∶3. Least absolute shrink-age and selection operator（LASSO） regression was used to screen the PCa characteristic indicators of the modeling group， multifactor logistic regression analysis was carried out to analyze the characteristic indicators，and the analysis results were used to build a PCa risk prediction model for the data of the modeling group. At the same time，the data of the modeling group were used for internal evaluation and the data of the validation group for internal verification.Results A total of 880 sample data were included，including 616 in the modeling group and 264 in the validation group. The 14 characteristic indexes screened by LASSO regression analysis were used for multivariate logistic regression analysis. The results showed that only globulin（OR=1.112，95%CI=1.044-1.185），inorganic phosphorus（OR=65.167，95%CI=20.437-207.796），total prostate specific antigen（tPSA）（OR=1.026，95%CI=1.014-1.037） and serum uric acid（OR=0.997，95%CI=0.994-0.999） had significant differences（P<0.05），and they were independent influencing factors for PCa. The calibration curve of the internal evaluation and internal verification of the PCa risk prediction model had high accuracy. The area under curve（AUC） of internal evaluation of the model was 0.766（95%CI=0.728-0.804），and the net benefit rate of decision curve analysis（DCA） of patients was 9%-72%；the AUC of internal validation of the model was 0.704（95%CI=0.639-0.768），and the net benefit rate of DCA of patients was 18%-59% and 63%-64%.Conclusion Globulin，inorganic phosphorus，tPSA and serum uric acid are independent influencing factors of PCa. The risk prediction model constructed by them has a good prediction effect.