Validation of the GALAD model for diagnosing HBV-related hepatocellular carcinoma in Chinese patients.
코호트
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
210 patients with benign Liver disease without cirrhosis, 247 Cirrhosis patients, and 220 healthy controls.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[CONCLUSION] The GALAD model outperforms individual biomarkers in diagnosing HBV-related HCC in the Chinese population, highlighting its potential utility in clinical practice. This cost-effective tool holds particular promise for the tertiary prevention of HBV-related HCC, especially in resource-limited settings.
[BACKGROUND] The GALAD model integrates serological biomarkers (AFP, AFP-L3%, DCP) with demographic factors to estimate the presence of Hepatocellular Carcinoma (HCC).
- Specificity 89.91 %
- 연구 설계 cohort study
APA
Li K, Xia F, Wu X (2026). Validation of the GALAD model for diagnosing HBV-related hepatocellular carcinoma in Chinese patients.. Clinics (Sao Paulo, Brazil), 81, 100882. https://doi.org/10.1016/j.clinsp.2026.100882
MLA
Li K, et al.. "Validation of the GALAD model for diagnosing HBV-related hepatocellular carcinoma in Chinese patients.." Clinics (Sao Paulo, Brazil), vol. 81, 2026, pp. 100882.
PMID
41707450 ↗
Abstract 한글 요약
[BACKGROUND] The GALAD model integrates serological biomarkers (AFP, AFP-L3%, DCP) with demographic factors to estimate the presence of Hepatocellular Carcinoma (HCC). However, its applicability in Chinese populations with HBV-related HCC remains underexplored. This study validated the clinical utility of GALAD in this specific population.
[METHODS] A retrospective cohort study enrolled 217 HBV-related HCC patients, 210 patients with benign Liver disease without cirrhosis, 247 Cirrhosis patients, and 220 healthy controls. Serum levels of AFP, AFP-L3%, and DCP were measured. Receiver Operating Characteristic (ROC) curve analysis assessed diagnostic performance, and the Hosmer-Lemeshow test evaluated model calibration. Serum levels of AFP, AFP-L3%, and DCP were measured. Receiver Operating Characteristic (ROC) curve analysis assessed diagnostic performance, and the Hosmer-Lemeshow test evaluated model calibration.
[RESULTS] GALAD demonstrated superior diagnostic performance, with an Area Under the Curve (AUC) of 0.942. At an optimal cutoff of 1.89, sensitivity and specificity were 89.91 % and 81.51 %, respectively. The model showed excellent calibration (χ = 8.934, p > 0.05), confirming its reliability for Chinese HBV-related HCC patients.
[CONCLUSION] The GALAD model outperforms individual biomarkers in diagnosing HBV-related HCC in the Chinese population, highlighting its potential utility in clinical practice. This cost-effective tool holds particular promise for the tertiary prevention of HBV-related HCC, especially in resource-limited settings.
[METHODS] A retrospective cohort study enrolled 217 HBV-related HCC patients, 210 patients with benign Liver disease without cirrhosis, 247 Cirrhosis patients, and 220 healthy controls. Serum levels of AFP, AFP-L3%, and DCP were measured. Receiver Operating Characteristic (ROC) curve analysis assessed diagnostic performance, and the Hosmer-Lemeshow test evaluated model calibration. Serum levels of AFP, AFP-L3%, and DCP were measured. Receiver Operating Characteristic (ROC) curve analysis assessed diagnostic performance, and the Hosmer-Lemeshow test evaluated model calibration.
[RESULTS] GALAD demonstrated superior diagnostic performance, with an Area Under the Curve (AUC) of 0.942. At an optimal cutoff of 1.89, sensitivity and specificity were 89.91 % and 81.51 %, respectively. The model showed excellent calibration (χ = 8.934, p > 0.05), confirming its reliability for Chinese HBV-related HCC patients.
[CONCLUSION] The GALAD model outperforms individual biomarkers in diagnosing HBV-related HCC in the Chinese population, highlighting its potential utility in clinical practice. This cost-effective tool holds particular promise for the tertiary prevention of HBV-related HCC, especially in resource-limited settings.
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