Establishment and validation of nomogram model for the diagnosis of AFP-negative hepatocellular carcinoma

Abstract

IntroductionAs one of the most common malignant tumors in clinical practice, hepatocellular carcinoma (HCC) is a major threat to human health, where alpha-fetoprotein (AFP) is widely used for early screening and diagnoses. However, the level of AFP would not elevate in about 30-40% of HCC patients, which is clinically referred to as AFP-negative HCC, with small tumors at an early stage and atypical imaging features, making it difficult to distinguish benign from malignant by imaging alone.MethodsA total of 798 patients, with the majority being HBV-positive, were enrolled in the study and were randomized 2:1 to the training and validation groups. Univariate and multivariate binary logistic regression analyses were used to determine the ability of each parameter to predict HCC. A nomogram model was constructed based on the independent predictors. ResultsA unordered multicategorical logistic regression analyses showed that the age, TBIL, ALT, ALB, PT, GGT and GPR help identify non-hepatic disease, hepatitis, cirrhosis, and hepatocellular carcinoma. A multivariate logistic regression analyses showed that the gender, age, TBIL, GAR, and GPR were independent predictors for the diagnosis of AFP-negative HCC. And an efficient and reliable nomogram model (AUC=0.837) was constructed based on independent predictors.DiscussionSerum parameters help reveal intrinsic differences between non-hepatic disease, hepatitis, cirrhosis, and HCC. The nomogram based on clinical and serum parameters could be used as a marker for the diagnosis of AFP-negative HCC, providing an objective basis for the early diagnosis and individualized treatment of hepatocellular carcinoma patients.