Background/Objectives: Accurate prognostication of hepatocellular carcinoma (HCC) remains essential for treatment selection and risk stratification. This study aimed to compare the prognostic performance of individual serum biomarkers and composite scoring models, including GALAD, BALAD, BALAD-2, GAAP, ASAP, the Doylestown algorithm, and aMAP, using data from a biobank-based HCC cohort.Methods: This study enrolled 186 patients with confirmed HCC diagnosed between 2019 and 2024. Serum biomarkers (AFP, AFP-L3%, DCP) and composite models were evaluated for their association with overall survival (OS). Prognostic performance was assessed using time-dependent area under the receiver operating characteristic curve (AUROC) at 1-, 2-, 3-, and 5-year intervals and Harrel’s concordance index (c-index). Subgroup analyses were performed based on treatment intent and liver disease etiology.Results: All three biomarkers and composite models were independently associated with OS in multivariate analyses (allp< 0.05). Among all models, BALAD-2 demonstrated the best overall performance (c-index: 0.737), with the highest AUROCs at 1 year (0.827), 2 years (0.846), 3 years (0.781), and 5 years (0.716). BALAD-2 consistently showed superior discrimination in patients treated with curative or noncurative therapies and in the viral etiology subgroup. In the non-viral etiology subgroup, BALAD-2 remained among the top performers, although the GAAP, ASAP, and Doylestown algorithms showed slightly higher metrics.Conclusions: BALAD-2 demonstrated consistent and robust prognostic performance compared with other biomarker-based and clinical models across different patient subgroups, particularly among those receiving curative therapy and viral etiologies. These findings support its integration into clinical risk stratification and decision-making for HCC management.
**背景/目的：** 肝细胞癌（HCC）的准确预后评估对于治疗方案选择和风险分层仍然至关重要。本研究旨在利用基于生物样本库的HCC队列数据，比较单一血清生物标志物和复合评分模型（包括GALAD、BALAD、BALAD-2、GAAP、ASAP、Doylestown算法和aMAP）的预后预测性能。 **方法：** 本研究纳入了2019年至2024年间确诊的186例HCC患者。评估了血清生物标志物（AFP、AFP-L3%、DCP）和复合模型与总生存期（OS）的关联性。预后性能通过1年、2年、3年和5年时间点的受试者工作特征曲线下面积（AUROC）以及Harrell一致性指数（c-index）进行评估。根据治疗意向和肝病病因进行了亚组分析。 **结果：** 在多变量分析中，所有三种生物标志物和复合模型均与OS独立相关（所有p < 0.05）。在所有模型中，BALAD-2表现出最佳的整体性能（c-index: 0.737），其1年（0.827）、2年（0.846）、3年（0.781）和5年（0.716）的AUROC值最高。在接受根治性或非根治性治疗的患者以及病毒性病因亚组中，BALAD-2始终显示出更优的区分能力。在非病毒性病因亚组中，尽管GAAP、ASAP和Doylestown算法的指标略高，但BALAD-2仍属于表现最佳的模型之一。 **结论：** 与其他基于生物标志物和临床的模型相比，BALAD-2在不同患者亚组中，特别是在接受根治性治疗和病毒性病因的患者中，表现出了一致且稳健的预后预测性能。这些发现支持将其整合到HCC管理的临床风险分层和决策制定中。
肿瘤(癌症)患者之家