Serum-biomarker-based population screening model for hepatocellular carcinoma
Wenmin Liao,
Wenbin Lin,
Zhonglian He,
Chenyang Feng,
Yuying Liu,
Zixian Wang,
Ruizhi Wang,
Meifang He,
Shuqin Dai,
Ying Sun,
Wei Wei,
Peisong Chen,
Chaofeng Li
Affiliations
Wenmin Liao
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Information Technology, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China
Wenbin Lin
Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
Zhonglian He
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Information Technology, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China
Chenyang Feng
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Information Technology, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China
Yuying Liu
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China
Zixian Wang
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China
Ruizhi Wang
Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
Meifang He
Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, P. R. China
Shuqin Dai
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Clinical Laboratory, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
Ying Sun
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, P. R. China
Wei Wei
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Hepatobiliary Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; Corresponding author
Peisong Chen
Department of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China; Corresponding author
Chaofeng Li
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. China; Department of Information Technology, Sun Yat-sen University Cancer Center, Guangzhou 510060, P. R. China; Corresponding author
Summary: Hepatocellular carcinoma (HCC) early identification is crucial for improving patient outcomes. Current screening methods are often complex and costly. This study developed a simplified, cost-effective HCC screening model using serum marker data. A diverse study population from two Chinese hospitals was recruited, including cancer patients, hospital patients, and healthy individuals. A two-stage screening model was created: LASSO logistic regression for preliminary screening, followed by logistic regression incorporating alpha-fetoprotein (AFP). The model’s performance was evaluated in multiple cohorts. Across five populations, the model showed strong performance with AUC-ROC ranging from 0.868 to 0.907, accuracy between 87.43% and 96.96%, and sensitivity over 75% with specificity above 90%. Compared with solely AFP models, the second-stage model improved HCC risk estimates in healthy populations, with significantly higher AUC (0.930 vs. 0.827) and net reclassification improvement (NRI) up to 56.2%. This two-stage model offers a practical, cost-efficient tool for early HCC detection, addressing a significant public health need.
