RESUMEN
Objective:To establish a model C-GALAD for detecting hepatocellular carcinoma (HCC) from the chronic liver disease and healthy people based on the serum markers.Methods:A clinical cohort including 229 hepatocellular carcinoma patients, 2 317 patients with chronic liver disease and 982 healthy people, was retrospectively collected from eight hospitals or physical examination institutions from April 2018 to October 2020. The data were divided into a training set and a testing set by stratified sampling with a 6∶4 ratio. A predictive model was established on the training set using a logistic backward regression method and validated on the testing set. In addition, clinical data from March to July 2021 in Beijing You′ an Hospital affiliated to Capital Medical University, including 84 patients with liver cancer and 204 patients with chronic liver disease collected were used for external independent validation of the model. The receiver operating characteristic curve (ROC) area under curve (AUC), the sensitivity and the specificity were used to evaluate the effectiveness of the model.Results:Through the logistic backward regression method, the seven signatures including age, gender, alpha-fetoprotein (AFP), alpha-fetoprotein alloplasm-3 ratio (AFP-L3%), des-gamma-carboxyprothrombin(DCP), platelet (PLT) and total bilirubin (TBIL) were selected as risk factors in the detection model. The area under the ROC curve (AUC) of the model on the testing set was 0.954, with an 88.04% sensitivity and a 94.85% specificity, and the AUC of model on the external independent validation set was 0.943, with an 89.29% sensitivity and a 90.2% specificity, which were better than other published models.Conclusion:The C-GALAD Ⅱ model can accurately predict the risk of hepatocellular carcinoma occurrence, and thus provide a trustworthy diagnosis method of hepatocellular carcinoma.