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已发表论文
两种化学发光免疫分析平台检测甲胎蛋白、异常凝血酶原、GALAD 模型和 ASAP 模型诊断肝细胞癌的性能比较
Authors Huang Y , Ding R , Cui Y, Li P, Niu J, Wang GH, Qin XZ
Received 19 August 2025
Accepted for publication 6 December 2025
Published 8 January 2026 Volume 2026:13 554305
DOI https://doi.org/10.2147/JHC.S554305
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr David Gerber
Yuan Huang,1,* Rui Ding,1,* Yue Cui,2,* Peng Li,1 Jie Niu,3 Guan-Hua Wang,1 Xu-Zhen Qin1
1Department of Clinical Laboratory, Chinese Academy of Medical Sciences & Peking Union Medical College Hospital, Beijing, 100730, People’s Republic of China; 2Department of Clinical Laboratory, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, People’s Republic of China; 3Department of Clinical Laboratory, Capital Center for Children’s Health, Capital Medical University, Beijing, 100020, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xu-Zhen Qin, Email qxz_01@163.com
Objective: This study aimed to evaluate the diagnostic performance of individual serum biomarkers [alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-II (PIVKA-II)] and composite models (GALAD, ASAP) for hepatocellular carcinoma (HCC) across two immunoassay platforms.
Methods: From 2011 to 2021, 518 serum samples were selected from a liver-related disease biobank at Peking Union Medical College Hospital (Beijing, China), including 102 HCC patients, 117 with benign liver disease, 38 with cholangiocarcinoma, 96 with colorectal cancer, 65 with metastatic hepatic carcinoma, and 100 healthy controls. AFP and PIVKA-II levels were measured on both the Hotgen and Abbott ARCHITECT platforms. The GALAD and ASAP scores were calculated based on the data from each platform. Receiver operating characteristic (ROC) curve analysis and the corresponding areas under the curves (AUCs) were used to evaluate and compare the diagnostic value of the individual biomarkers and the two composite models.
Results: For HCC diagnosis, AFP exhibited comparable efficacy between Hotgen (AUC: 0.821) and Abbott (AUC: 0.846), whereas PIVKA-II performed better on Abbott (AUC: 0.863) than Hotgen (AUC: 0.787). GALAD and ASAP models exhibited significantly better diagnostic performance than individual serum biomarkers on both platforms (P < 0.05): on Hotgen, both models achieved an AUC of 0.872, while on Abbott, ASAP (AUC: 0.913) was marginally superior to GALAD (AUC: 0.901, P = 0.0569). Notably, both models performed better on Abbott than Hotgen (GALAD: 0.901 vs 0.872, P = 0.0001; ASAP: 0.913 vs 0.872, P = 0.0003). Spearman correlation analysis showed moderate inter-platform correlations for AFP (r = 0.573) and PIVKA-II (r = 0.460). Bland-Altman analysis indicated poor inter-platform consistency, with mean biases of 44.32% (AFP) and − 92.02% (PIVKA-II).
Conclusion: GALAD and ASAP models demonstrate superior diagnostic efficacy for HCC compared to individual biomarkers, and their performance is significantly influenced by the immunoassay platform employed.
Keywords: hepatocellular carcinoma, AFP, PIVKA-II, GALAD, ASAP, hotgen biotech, Abbott Architect
1Department of Clinical Laboratory, Chinese Academy of Medical Sciences & Peking Union Medical College Hospital, Beijing, 100730, People’s Republic of China; 2Department of Clinical Laboratory, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, People’s Republic of China; 3Department of Clinical Laboratory, Capital Center for Children’s Health, Capital Medical University, Beijing, 100020, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xu-Zhen Qin, Email qxz_01@163.com
Objective: This study aimed to evaluate the diagnostic performance of individual serum biomarkers [alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-II (PIVKA-II)] and composite models (GALAD, ASAP) for hepatocellular carcinoma (HCC) across two immunoassay platforms.
Methods: From 2011 to 2021, 518 serum samples were selected from a liver-related disease biobank at Peking Union Medical College Hospital (Beijing, China), including 102 HCC patients, 117 with benign liver disease, 38 with cholangiocarcinoma, 96 with colorectal cancer, 65 with metastatic hepatic carcinoma, and 100 healthy controls. AFP and PIVKA-II levels were measured on both the Hotgen and Abbott ARCHITECT platforms. The GALAD and ASAP scores were calculated based on the data from each platform. Receiver operating characteristic (ROC) curve analysis and the corresponding areas under the curves (AUCs) were used to evaluate and compare the diagnostic value of the individual biomarkers and the two composite models.
Results: For HCC diagnosis, AFP exhibited comparable efficacy between Hotgen (AUC: 0.821) and Abbott (AUC: 0.846), whereas PIVKA-II performed better on Abbott (AUC: 0.863) than Hotgen (AUC: 0.787). GALAD and ASAP models exhibited significantly better diagnostic performance than individual serum biomarkers on both platforms (P < 0.05): on Hotgen, both models achieved an AUC of 0.872, while on Abbott, ASAP (AUC: 0.913) was marginally superior to GALAD (AUC: 0.901, P = 0.0569). Notably, both models performed better on Abbott than Hotgen (GALAD: 0.901 vs 0.872, P = 0.0001; ASAP: 0.913 vs 0.872, P = 0.0003). Spearman correlation analysis showed moderate inter-platform correlations for AFP (r = 0.573) and PIVKA-II (r = 0.460). Bland-Altman analysis indicated poor inter-platform consistency, with mean biases of 44.32% (AFP) and − 92.02% (PIVKA-II).
Conclusion: GALAD and ASAP models demonstrate superior diagnostic efficacy for HCC compared to individual biomarkers, and their performance is significantly influenced by the immunoassay platform employed.
Keywords: hepatocellular carcinoma, AFP, PIVKA-II, GALAD, ASAP, hotgen biotech, Abbott Architect