Tian Yang1, Hao Xing1, Guoqiang Wang2, Nianyue Wang3, Miaoxia Liu4, Cunling Yan5, Huijun Li6, Lianhua Wei7, Shunjun Li8, Zhuping Fan9, Ming Shi10, Wei Chen11, Shangli Cai12, Timothy M Pawlik13, Andrew Soh14, Agim Beshiri14, Wan Yee Lau15, Mengchao Wu1, Yijie Zheng16, Feng Shen17. 1. Department of Hepatic Surgery, Second Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China. 2. Peking University Health Science Center, Beijing, China. 3. Department of Clinical Laboratory and Liver Diseases, Medical School of Southeast University, The Second Hospital of Nanjing, Nanjing, China. 4. Department of Hepatology, First Hospital of Jilin University, Changchun, China. 5. Department of Clinical Laboratory, Peking University First Hospital, Beijing, China. 6. Department of Laboratory Medicine, Huazhong University of Science and Technology, Tongji Medical College, Tongji Hospital, Wuhan, China. 7. Department of Clinical Laboratory, Gansu Provincial People's Hospital, Lanzhou, China. 8. Department of Clinical Laboratory, Sichuan Provincial People's Hospital, Chengdu, China. 9. Department of Health Manage Center, Shanghai Jiao Tong University, School of Medicine, Renji Hospital, Shanghai, China. 10. Department of Hepatobiliary Disorders, Sun Yat-sen University, Cancer Center, Guangdong, China. 11. Department of Laboratory Medicine, Xi'an Jiaotong University, School of Medicine, The First Affiliated Hospital, Xi'an, China. 12. School of Public Health, The Second Xiangya Hospital of Central South University, Changsha, China. 13. Department of Surgery, Ohio State University, Wexner Medical Center, Columbus, OH. 14. Medical Scientific Affairs, Abbott Laboratories, Abbott Diagnostics Division, Shanghai, China. 15. Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong, China. 16. Medical Scientific Affairs, Abbott Laboratories, Abbott Diagnostics Division, Shanghai, China; fengshensmmu@gmail.com yijiezheng2015@163.com. 17. Department of Hepatic Surgery, Second Military Medical University, Eastern Hepatobiliary Surgery Hospital, Shanghai, China; fengshensmmu@gmail.com yijiezheng2015@163.com.
Abstract
BACKGROUND: Early detection of hepatocellular carcinoma (HCC) among hepatitis B virus (HBV)-infected patients remains a challenge, especially in China. We sought to create an online calculator of serum biomarkers to detect HCC among patients with chronic hepatitis B (CHB). METHODS: Participants with HBV-HCC, CHB, HBV-related liver cirrhosis (HBV-LC), benign hepatic tumors, and healthy controls (HCs) were recruited at 11 Chinese hospitals. Potential serum HCC biomarkers, protein induced by vitamin K absence or antagonist-II (PIVKA-II), α-fetoprotein (AFP), lens culinaris agglutinin A-reactive fraction of AFP (AFP-L3) and α-l-fucosidase (AFU) were evaluated in the pilot cohort. The calculator was built in the training cohort via logistic regression model and validated in the validation cohort. RESULTS: In the pilot study, PIVKA-II and AFP showed better diagnostic sensitivity and specificity compared with AFP-L3 and AFU and were chosen for further study. A combination of PIVKA-II and AFP demonstrated better diagnostic accuracy in differentiating patients with HBV-HCC from patients with CHB or HBV-LC than AFP or PIVKA-II alone [area under the curve (AUC), 0.922 (95% CI, 0.908-0.935), sensitivity 88.3% and specificity 85.1% for the training cohort; 0.902 (95% CI, 0.875-0.929), 87.8%, and 81.0%, respectively, for the validation cohort]. The nomogram including AFP, PIVKA-II, age, and sex performed well in predicting HBV-HCC with good calibration and discrimination [AUC, 0.941 (95% CI, 0.929-0.952)] and was validated in the validation cohort [AUC, 0.931 (95% CI, 0.909-0.953)]. CONCLUSIONS: Our results demonstrated that a web-based calculator including age, sex, AFP, and PIVKA-II accurately predicted the presence of HCC in patients with CHB. CLINICALTRIALSGOV IDENTIFIER: NCT03047603.
BACKGROUND: Early detection of hepatocellular carcinoma (HCC) among hepatitis B virus (HBV)-infectedpatients remains a challenge, especially in China. We sought to create an online calculator of serum biomarkers to detect HCC among patients with chronic hepatitis B (CHB). METHODS:Participants with HBV-HCC, CHB, HBV-related liver cirrhosis (HBV-LC), benign hepatic tumors, and healthy controls (HCs) were recruited at 11 Chinese hospitals. Potential serum HCC biomarkers, protein induced by vitamin K absence or antagonist-II (PIVKA-II), α-fetoprotein (AFP), lens culinaris agglutinin A-reactive fraction of AFP (AFP-L3) and α-l-fucosidase (AFU) were evaluated in the pilot cohort. The calculator was built in the training cohort via logistic regression model and validated in the validation cohort. RESULTS: In the pilot study, PIVKA-II and AFP showed better diagnostic sensitivity and specificity compared with AFP-L3 and AFU and were chosen for further study. A combination of PIVKA-II and AFP demonstrated better diagnostic accuracy in differentiating patients with HBV-HCC from patients with CHB or HBV-LC than AFP or PIVKA-II alone [area under the curve (AUC), 0.922 (95% CI, 0.908-0.935), sensitivity 88.3% and specificity 85.1% for the training cohort; 0.902 (95% CI, 0.875-0.929), 87.8%, and 81.0%, respectively, for the validation cohort]. The nomogram including AFP, PIVKA-II, age, and sex performed well in predicting HBV-HCC with good calibration and discrimination [AUC, 0.941 (95% CI, 0.929-0.952)] and was validated in the validation cohort [AUC, 0.931 (95% CI, 0.909-0.953)]. CONCLUSIONS: Our results demonstrated that a web-based calculator including age, sex, AFP, and PIVKA-II accurately predicted the presence of HCC in patients with CHB. CLINICALTRIALSGOV IDENTIFIER: NCT03047603.