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Comparative Evaluation of ASAP and GALAD Scores for Detecting Hepatocellular Carcinoma in Patients With Chronic Liver
Thuy Minh Le1, Khanh Cong Pham
1Gastrointestinal Endoscopy and Laparoscopic Surgery Center, Tam Anh General Hospital, Ho Chi Minh City, Vietnam.
Insights
The ASAP score shows strong clinical performance for detecting hepatocellular carcinoma (HCC) in patients with chronic liver disease. This simple, cost-effective tool may aid in early HCC detection.
Area of Science:
- Hepatology
- Oncology
- Biomarker Discovery
Background:
- Hepatocellular carcinoma (HCC) diagnosis benefits from combined biomarkers and clinical data over single markers.
- Novel algorithms like ASAP and GALAD scores are being evaluated for HCC detection in chronic liver disease (CLD) patients.
Purpose of the Study:
- To assess the diagnostic performance of the ASAP and GALAD scores for hepatocellular carcinoma (HCC) detection.
- To compare the efficacy of these novel algorithms against individual biomarkers (AFP, AFP-L3, PIVKA-II).
Main Methods:
- A case-control study involving 105 HCC patients and 104 non-HCC CLD patients.
- Performance evaluation using receiver operating characteristic (ROC) curve analysis for serum biomarkers and predictive models.
- Comparison of ASAP, GALAD, AFP, AFP-L3, and PIVKA-II in identifying HCC across all and early stages.
Main Results:
- Both ASAP (AUC: 0.96) and GALAD (AUC: 0.95) models demonstrated high accuracy for all-stage HCC detection.
- ASAP and GALAD significantly outperformed individual biomarkers: AFP (AUC: 0.75), AFP-L3 (AUC: 0.73), and PIVKA-II (AUC: 0.85).
- Comparable AUCs were observed for early-stage HCC detection: ASAP (0.91) and GALAD (0.90).
Conclusions:
- The ASAP score exhibits strong clinical performance for HCC detection, comparable to the GALAD score.
- ASAP utilizes one fewer laboratory variable than GALAD, suggesting potential for simpler, cost-effective early HCC detection.
- The ASAP score is a promising tool for the early and efficient diagnosis of hepatocellular carcinoma.
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.
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