Validation of the GALAD Model and Establishment of GAAP Model for Diagnosis of Hepatocellular Carcinoma in Chinese Patients

Purpose: GALAD is a statistical model for estimating the likelihood of having hepatocellular carcinoma (HCC) based on gender, age, AFP, AFP-L3, and PIVKA-II. We aimed to assess its performance and build new models in China, where hepatitis B virus (HBV) is the leading etiology of HCC. Patients and Methods: We built the GALAD-C model with the same five variables in GALAD, and the GAAP model with gender, age, AFP, and PIVKA-II, using logistic regression based on 242 patients with HCC and 283 patients with chronic liver disease (CLD). We also collected 50 patients with other malignant liver tumors (OMTs) and 50 healthy controls (HCs). A test dataset (169 patients with HCC and 139 with CLD) was used to test the performance of GAAP. Results: The GALAD-C and GAAP models achieved comparable performance (area under the receiver operating characteristic curve [AUC], 0.922 vs 0.914), and both were superior to GALAD, PIVKA-II, AFP, and AFP-L3% (AUCs, 0.891, 0.869, 0.750, and 0.711) for discrimination of HCC from CLD for the entire dataset. The AUCs of the GALAD, GALAD-C and GAAP models were excellent for the hepatitis C virus (HCV) subgroup (0.939, 0.958 and 0.954), and for discrimination HCC from HCs (0.988, 0.982, and 0.979), but were relatively lower for the HBV subgroup (0.855, 0.904, and 0.894), and for HCC within Milan Criteria (0.810, 0.841, and 0.840). They were not superior to AFP (0.873) for discrimination of HCC from OMT (0.873, 0.809, and 0.823). GAAP achieved an AUC of 0.922 in the test dataset. Conclusion: GALAD was excellent for discrimination of HCC from CLD in the HCV subgroup of a cohort of Chinese patients. The GAAP and GALAD-C models achieved better performance compared with GALAD. These three models exhibited better performance in patients with an HCV etiology than those with HBV.