Application of Risk Scores for Hepatocellular Carcinoma in Patients with Chronic Hepatitis B: Current Status and Future Perspective

Author:

Hsu Yao-Chun1234,Tseng Cheng-Hao25,Huang Yen-Tsung6,Yang Hwai-I78910

Affiliation:

1. Center for Liver Diseases, E-Da Hospital, Kaohsiung, Taiwan

2. School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan

3. Department of Medicine, Fu-Jen Catholic University Hospital, New Taipei, Taiwan

4. Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan

5. Division of Gastroenterology and Hepatology, E-Da Cancer Hospital, Kaohsiung, Taiwan

6. Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

7. Genomics Research Center, Academia Sinica, Taipei, Taiwan

8. Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan

9. Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

10. Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan

Abstract

AbstractAccurate risk prediction for hepatocellular carcinoma (HCC) among patients with chronic hepatitis B (CHB) may guide treatment strategies including initiation of antiviral therapy and also inform implementation of HCC surveillance. There have been 26 risk scores developed to predict HCC in CHB patients with (n = 14) or without (n = 12) receiving antiviral treatment; all of them invariably include age in the scoring formula. Virological biomarkers of replicative activities (i.e., hepatitis B virus DNA level or hepatitis B envelope antigen status) are frequently included in the scores derived from patients with untreated CHB, whereas measurements that gauge severity of liver fibrosis and/or reserve of hepatic function (i.e., cirrhosis diagnosis, liver stiffness measurement, platelet count, or albumin) are essential components in the scores developed from treated patients. External validation is a prerequisite for clinical application but not yet performed for all scores. For the future, higher predictive accuracy may be achieved with machine learning based on more comprehensive data.

Funder

Ministry of Science and Technology in Taiwan

E-Da Hospital

Tomorrow Medical Foundation

Publisher

Georg Thieme Verlag KG

Subject

Hepatology

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