Performance analysis of predictive model for diagnosis of hepatocellular carcinoma based on PIVKA-II,AFP detection and machine learning algorithms
Objective:To investigate the establishment and diagnostic application value of the auxiliary diagnostic prediction model for hepatocellular carcinoma patients based on PIVKA-Ⅱ and AFP detection and machine learning algorithms.Methods:A total of 112 cases of healthy check-ups,149 cases of patients with benign liver disease,and 265 cases of patients with a primary diagnosis of hepatocellular carcinoma admitted to Zhejiang Provincial Cancer Hospital from March 2022 to December 2022 were selected to evaluate the levels of serum vitamin K absence Ⅱ(PIVKA-Ⅱ)and alpha-fetoprotein(AFP)and to combine with machine-learning algorithms to construct a prediction model for auxiliary diagnosis,and to compare the diagnostic value of different models in hepatocellular carcinoma.Results:Serum PIVKA-Ⅱand AFP had the highest levels in the hepatocellular carcinoma group of patients.They were correlated with clinical characteristics such as tumor size,tumor number,and tumor differentiation in hepatocellular carcinoma patients.The predictive model for the adjuvant diagnosis of hepatocellular carcinoma constructed with the aid of the gradient boost machine(GBM)algorithm,characterized by age,gender,PIVKA-Ⅱ,and AFP levels,outperformed the PIVKA-Ⅱ,AFP alone,and ASAP models in the diagnosis of hepatocellular carcinoma,early-stage hepatocellular carcinoma,advanced-stage hepatocellular carcinoma,and AFP negative hepatocellular carcinoma.Conclusion:The predictive model for hepatocellular carcinoma auxiliary diagnosis constructed with the help of the GBM algorithm characterized by age,gender,PIVKA-Ⅱ,and AFP levels improved the diagnostic accuracy of hepatocellular carcinoma.
hepatocellular carcinomaPIVKA-IIalpha-fetoproteingradient boosting machinediagnostic value
万方数据
维普
