Huang Chenjun, Xiao Xiao, Tong Lin, Gao Zhiyuan, Ji Jun, Zhou Lin, Li Ya, Liu Lijuan, Feng Huijuan, Fang Meng, Gao Chunfang
Department of Clinical Laboratory Medicine Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200437, People's Republic of China.
Department of Laboratory Medicine, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai, 200438, People's Republic of China.
J Hepatocell Carcinoma. 2024 Feb 27;11:411-425. doi: 10.2147/JHC.S447700. eCollection 2024.
Early detection of hepatocellular carcinoma (HCC) through surveillance could reduce this cancer-associated mortality. We aimed to develop and validate algorithms using panel serum biomarkers to identify HCC in a real-world multi-center study in China.
A total of 10,359 eligible subjects, including HCCs and benign liver diseases (BLDs), were recruited from six Chinese medical centers. The three nomograms were built using logistic regression and their sensitivities and specificities were carefully assessed in training and validation cohorts. HCC patients after surgical resection were followed to determine the prognostic values of these algorithms. Prospective surveillance performance was assessed in a cohort of chronic hepatitis B patients during 144 weeks follow-up.
Independent risk factors such as alpha-fetoprotein (AFP), lens cuinaris agglutinin-reactive fraction of AFP (AFP-L3), des-gamma-carboxy prothrombin (DCP), albumin (ALB), and total bilirubin (TBIL) obtained from train cohort were used to construct three nomograms (LAD, C-GALAD, and TAGALAD) using logistic regression. In the training and two validation cohorts, their AUCs were all over 0.900, and the higher AUCs appeared in TAGALAD and C-GALAD. Furthermore, the three nomograms could effectively stratify HCC into two groups with different survival and recurrence outcomes in follow-up validation. Notably, TAGALAD could predict HCC up to 48 weeks (AUC: 0.984) and 24 weeks (AUC: 0.900) before clinical diagnosis.
The proposed nomograms generated from real-world Chinese populations are effective and easy-to use for HCC surveillance, diagnosis, as well as prognostic evaluation in various clinical scenarios based on data feasibility.
通过监测早期发现肝细胞癌（HCC）可降低这种癌症相关的死亡率。我们旨在开发并验证使用血清生物标志物面板的算法，以在中国的一项真实世界多中心研究中识别HCC。
从六个中国医疗中心招募了总共10359名符合条件的受试者，包括HCC患者和良性肝病（BLD）患者。使用逻辑回归构建三个列线图，并在训练和验证队列中仔细评估其敏感性和特异性。对手术切除后的HCC患者进行随访，以确定这些算法的预后价值。在一组慢性乙型肝炎患者的144周随访期间评估前瞻性监测性能。
从训练队列中获得的甲胎蛋白（AFP）、甲胎蛋白的刀豆球蛋白A反应分数（AFP-L3）、异常凝血酶原（DCP）、白蛋白（ALB）和总胆红素（TBIL）等独立危险因素，用于通过逻辑回归构建三个列线图（LAD、C-GALAD和TAGALAD）。在训练队列和两个验证队列中，它们的曲线下面积（AUC）均超过0.900，且较高的AUC出现在TAGALAD和C-GALAD中。此外，在随访验证中，这三个列线图可有效地将HCC分为具有不同生存和复发结果的两组。值得注意的是，TAGALAD可在临床诊断前48周（AUC：0.984）和24周（AUC：0.900）预测HCC。
基于实际中国人群生成的拟议列线图有效且易于用于HCC监测、诊断以及基于数据可行性的各种临床场景中的预后评估。
