ASAP Score versus GALAD Score for detection of hepatitis C-related hepatocellular carcinoma: A multicenter case-control analysis

Abstract

BackgroundThe GALAD and ASAP scores are two well-recognized algorithms to estimate the risk of hepatocellular carcinoma (HCC) based on gender, age, alpha-fetoprotein (AFP), protein induced by vitamin K absence or Antagonist-II (PIVKA-II) and AFP-L3 (included in the GALAD score but not in the ASAP score). The current study sought to compare the diagnostic performance of each score to detect HCC among patients infected with hepatitis C virus (HCV).MethodsA multicenter case-control study was undertaken in which blood samples were collected from HCVinfected patients with and without HCC. Using the area under the receiver operating characteristic curve (AUROC), ASAP and GALAD scores were compared relative to diagnostic performance to detect any stage HCV-HCC and early-stage HCV-HCC.ResultsThe analytic cohort included 168 HCV-HCC patients and a control group of 193 HCV-infected patients. The ASAP score had a higher AUROC to detect any stage HCV-HCC versus the GALAD score, both in the overall group (0.917 vs. 0.894, P=0.057) and in the cirrhosis subgroup (0.909 vs. 0.889, P=0.132). Similar results were noted relative to the detection of early-stage HCV-HCC, whether defined by BCLC staging (stage 0-A: 0.898 vs. 0.860, P=0.026) or 8th TNM staging (stage I: 0.899 vs. 0.870, P=0.070). In subgroup analysis to detect AFP-negative HCV-HCC, the ASAP score also demonstrated a higher AUROC than the GALAD score to detect any stage HCV-HCC in the AFP-negative subgroup (0.815 vs. 0.764, P=0.063).ConclusionsThe ASAP score had better diagnostic performance for early detection of HCV-HCC compared with the GALAD score. The ASAP score may be preferrable to the GALAD score for HCC screening and surveillance among HCV-infected patients.

Alpha-fetoprotein: Past, present, and future

“…In addition to the aforementioned GALAD score and the algorithms, there are several AFP-integrated protein-based panels still under phase 2 biomarker validation (Table 2 ). For example, using the same variables included in the GALAD score, GALAD-C, 86 87 GAAP, 86 87 116 ASAP, 116 117 118 and AALP 87 models were tailored and validated for HCC detection in Chinese populations. Another approach includes the incorporation of machine-learning models, such as gradient boosting that optimize the combination of AFP, AFP-L3%, and DCP to augment HCC detection.…”