Comparative Evaluation of ASAP and GALAD Scores for Detecting Hepatocellular Carcinoma in Patients With Chronic Liver Diseases.
[INTRODUCTION] The use of multiple biomarkers combined with clinical characteristics is more effective than a single biomarker for the diagnosis of hepatocellular carcinoma (HCC).
- 연구 설계 case-control
APA
Le TM, Pham KC (2025). Comparative Evaluation of ASAP and GALAD Scores for Detecting Hepatocellular Carcinoma in Patients With Chronic Liver Diseases.. Journal of clinical gastroenterology. https://doi.org/10.1097/MCG.0000000000002257
MLA
Le TM, et al.. "Comparative Evaluation of ASAP and GALAD Scores for Detecting Hepatocellular Carcinoma in Patients With Chronic Liver Diseases.." Journal of clinical gastroenterology, 2025.
PMID
41071278
Abstract
[INTRODUCTION] The use of multiple biomarkers combined with clinical characteristics is more effective than a single biomarker for the diagnosis of hepatocellular carcinoma (HCC). The present study assessed the performance of ASAP and GALAD scores, 2 novel algorithms for HCC detection in patients with chronic liver diseases (CLDs).
[METHODS] This case-control study included data from 105 patients with HCC and 104 patients with CLDs without HCC. The performances of serum alpha-fetoprotein (AFP), lens culinaris agglutinin-reactive AFP (AFP-L3), protein induced by vitamin K absence-II (PIVKA-II), the ASAP and GALAD models in identifying patients with HCC were compared using receiver operating characteristic (ROC) curve analysis.
[RESULTS] The ASAP model identified patients with all-stage HCC, reflected by a high area under the ROC curve (AUC) of 0.96, similar to the GALAD model (AUC: 0.95; P=0.190). Both models significantly outperformed other individual biomarkers in detecting HCC at any stage, including AFP (AUC: 0.75), AFP-L3 (AUC: 0.73), and PIVKA-II (AUC: 0.85). Furthermore, the ASAP and GALAD scores achieved comparable AUCs (0.91 and 0.90, respectively; P=0.432) for the detection of early-stage HCC.
[CONCLUSIONS] Compared with the GALAD score, the ASAP score demonstrated strong clinical performance in detecting HCC at any stage, even with one fewer laboratory variable (AFP-L3). Therefore, the ASAP score may serve as a simple and cost-effective tool for the early detection of HCC.
[METHODS] This case-control study included data from 105 patients with HCC and 104 patients with CLDs without HCC. The performances of serum alpha-fetoprotein (AFP), lens culinaris agglutinin-reactive AFP (AFP-L3), protein induced by vitamin K absence-II (PIVKA-II), the ASAP and GALAD models in identifying patients with HCC were compared using receiver operating characteristic (ROC) curve analysis.
[RESULTS] The ASAP model identified patients with all-stage HCC, reflected by a high area under the ROC curve (AUC) of 0.96, similar to the GALAD model (AUC: 0.95; P=0.190). Both models significantly outperformed other individual biomarkers in detecting HCC at any stage, including AFP (AUC: 0.75), AFP-L3 (AUC: 0.73), and PIVKA-II (AUC: 0.85). Furthermore, the ASAP and GALAD scores achieved comparable AUCs (0.91 and 0.90, respectively; P=0.432) for the detection of early-stage HCC.
[CONCLUSIONS] Compared with the GALAD score, the ASAP score demonstrated strong clinical performance in detecting HCC at any stage, even with one fewer laboratory variable (AFP-L3). Therefore, the ASAP score may serve as a simple and cost-effective tool for the early detection of HCC.