| Autor: |
Yip, Terry Cheuk-Fung, Lai, Jimmy Che-To, Liang, Lilian Yan, Hui, Vicki Wing-Ki, Wong, Vincent Wai-Sun, Wong, Grace Lai-Hung |
| Zdroj: |
Current Hepatology Reports; Dec2022, Vol. 21 Issue 4, p76-86, 11p |
| Abstrakt: |
Purpose of Review : Risk prediction and reduction of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B virus (HBV) infection has been a hot topic over the last decade. This review summarizes the latest evidence of HCC risk prediction and reduction. Recent Findings: Risk prediction models have been moving from traditional regression analysis on clinical parameters as well as HBV and liver fibrosis biomarkers to novel machine-learning models which maximize data use while minimizing bias. Different studies and meta-analyses have been performed to compare the risk of HCC in chronic HBV patients treated by entecavir versus tenofovir disoproxil fumarate, yet suggested inconsistent results. HCC risk is much reduced by antiviral therapy through sustained viral suppression, while aspirin, metformin, and statin were also found useful as chemoprevention for HCC. Summary: Novel machine-learning models for HCC prediction are going to guide HCC surveillance and the need of chemoprevention for HCC with medications on top of antiviral therapy. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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