Development and application of hepatocellular carcinoma risk prediction model based on clinical characteristics and liver related indexes

Abstract Background and aims Hepatocellular carcinoma (HCC) is difficult to diagnose and has a low survival rate. The survival of patients with HCC is closely related to the stage of diagnosis. Using logistic regression model, this study aimed to identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators to predict the presence of HCC. Methods The clinical data of patients in Affiliate Hospital of North Sichuan Medical College from 2016 to 2020 were collected. Based on the time of admission, the cases were divided into training cohort (n = 1739) and validation cohort (n = 467). Using HCC as a dependent variable, the research indicators were incorporated into logistic univariate and multivariate analysis. An HCC risk prediction model, which was called NSMC-HCC model, was then established in training cohort and verified in validation cohort. Results The area under receiver operating characteristic curve (AUC) of NSMC-HCC model in HCC diagnosis was 0.960, with sensitivity 94.40% and specificity 95.35% in training cohort, and AUC was 0.966, with sensitivity 90.00% and specificity 94.20% in validation cohort. In early-stage HCC diagnosis, the AUC of NSMC-HCC model was 0.946, with sensitivity 85.93% and specificity 93.62% in training cohort, and AUC was 0.947, with sensitivity 89.10% and specificity 98.49% in validation cohort. Conclusions NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.