Serum-biomarker-based population screening model for hepatocellular carcinoma
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 h...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S258900422500241X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850191459707256832 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Wenmin Liao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d182277221ad4e9cb50c9dc7d4d38953 |
| institution | OA Journals |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-d182277221ad4e9cb50c9dc7d4d389532025-08-20T02:14:54ZengElsevieriScience2589-00422025-03-0128311198110.1016/j.isci.2025.111981Serum-biomarker-based population screening model for hepatocellular carcinomaWenmin Liao0Wenbin Lin1Zhonglian He2Chenyang Feng3Yuying Liu4Zixian Wang5Ruizhi Wang6Meifang He7Shuqin Dai8Ying Sun9Wei Wei10Peisong Chen11Chaofeng Li12State 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. ChinaDepartment of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, ChinaState 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. ChinaState 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. ChinaState Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou 510060, P.R. ChinaState 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. ChinaDepartment of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, ChinaLaboratory of General Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, P. R. ChinaState 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, ChinaState 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. ChinaState 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 authorDepartment of Clinical Laboratory, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China; Corresponding authorState 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 authorSummary: 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.http://www.sciencedirect.com/science/article/pii/S258900422500241XPublic healthCancer |
| spellingShingle | 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 Serum-biomarker-based population screening model for hepatocellular carcinoma iScience Public health Cancer |
| title | Serum-biomarker-based population screening model for hepatocellular carcinoma |
| title_full | Serum-biomarker-based population screening model for hepatocellular carcinoma |
| title_fullStr | Serum-biomarker-based population screening model for hepatocellular carcinoma |
| title_full_unstemmed | Serum-biomarker-based population screening model for hepatocellular carcinoma |
| title_short | Serum-biomarker-based population screening model for hepatocellular carcinoma |
| title_sort | serum biomarker based population screening model for hepatocellular carcinoma |
| topic | Public health Cancer |
| url | http://www.sciencedirect.com/science/article/pii/S258900422500241X |
| work_keys_str_mv | AT wenminliao serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT wenbinlin serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT zhonglianhe serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT chenyangfeng serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT yuyingliu serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT zixianwang serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT ruizhiwang serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT meifanghe serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT shuqindai serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT yingsun serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT weiwei serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT peisongchen serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma AT chaofengli serumbiomarkerbasedpopulationscreeningmodelforhepatocellularcarcinoma |