Background/Objectives:Biomarkers such as lens agglutinin-reactive alpha-fetoprotein and des-gamma-carboxy prothrombin, as well as biomarker- and/or clinical-parameter-derived composite models (GALAD, GAAP, ASAP, aMAP, Doylestown), may improve detection in addition to alpha-fetoprotein, yet comparative data across diverse populations remain limited.Methods: In this biobank-based case–control study, we evaluated 562 adults (120 healthy controls, 277 chronic liver disease, 165 hepatocellular carcinoma) from January 2019 to 2024. Diagnostic performance for any-stage and early-stage hepatocellular carcinoma was assessed across three thresholds: Youden-index-derived optimal cut-offs, research-established cut-offs, and cut-offs ensuring 90% specificity. Receiver operating characteristic analysis was performed. Subgroup analyses were stratified by etiology and alpha-fetoprotein status.Results: At optimal cut-offs, GALAD showed the highest sensitivity for any-stage (90.3%) and early-stage (89.1%) hepatocellular carcinoma, with 70–80% specificity. Using established cut-offs, GALAD retained the highest sensitivity for any-stage (75.8%) and early-stage (57.8%) hepatocellular carcinoma, with 93.5% specificity. GALAD demonstrated the best performance in non-viral hepatocellular carcinomas (area under the curve 0.872), whereas GAAP and ASAP showed similarly high area under the curve values in viral etiology (area under the curve 0.955–0.960).Conclusions: Our results demonstrate the consistent performance of the GALAD score across diverse populations and underscore its superiority over individual biomarkers and other composite models. Notably, the GAAP and ASAP scores—which use one less biomarker (AFP-L3)—exhibited comparable performance, particularly in viral etiology. These findings support the integration of the composite biomarker models into tailored hepatocellular carcinoma surveillance strategies.
背景/目的：除甲胎蛋白外，透镜凝集素反应性甲胎蛋白和脱-γ-羧基凝血酶原等生物标志物，以及基于生物标志物和/或临床参数的复合模型（GALAD、GAAP、ASAP、aMAP、Doylestown）可能提升检测效能，但不同人群间的比较数据仍有限。方法：在这项基于生物样本库的病例对照研究中，我们评估了2019年1月至2024年期间的562名成人（120名健康对照者、277名慢性肝病患者、165名肝细胞癌患者）。通过三种阈值评估了针对全分期和早期肝细胞癌的诊断性能：约登指数导出的最佳截断值、研究确定的截断值以及确保90%特异性的截断值。进行了受试者工作特征分析。亚组分析按病因学和甲胎蛋白状态进行分层。结果：在最佳截断值下，GALAD模型对全分期（90.3%）和早期（89.1%）肝细胞癌显示出最高的敏感性，特异性为70–80%。使用既定截断值时，GALAD模型对全分期（75.8%）和早期（57.8%）肝细胞癌仍保持最高的敏感性，特异性为93.5%。GALAD在非病毒性肝细胞癌中表现出最佳性能（曲线下面积0.872），而GAAP和ASAP在病毒性病因中显示出同样高的曲线下面积值（0.955–0.960）。结论：我们的研究结果证明了GALAD评分在不同人群中具有一致的性能，并强调了其优于单一生物标志物和其他复合模型。值得注意的是，使用少一种生物标志物（AFP-L3）的GAAP和ASAP评分表现出可比的性能，尤其是在病毒性病因中。这些发现支持将复合生物标志物模型整合到个体化的肝细胞癌监测策略中。
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