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| http://dx.doi.org/10.1053/j.gastro.2024.11.017 | DOI Listing |
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Article Synopsis
- - The study aimed to create and validate a prognostic model for assessing the risk of hepatocellular carcinoma (HCC) in noncirrhotic adults with chronic hepatitis B, focusing on those without significant alanine aminotransferase (ALT) elevation.
- - Researchers analyzed data from over 13,000 patients across several cohorts in Taiwan, Korea, and Hong Kong, finding that baseline HBV DNA levels were key predictors of HCC risk, especially at moderate viral loads.
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Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis.
JHEP Rep
March 2022
Medical Data Analytic Centre, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
Background & Aims: Accurate hepatocellular carcinoma (HCC) risk prediction facilitates appropriate surveillance strategy and reduces cancer mortality. We aimed to derive and validate novel machine learning models to predict HCC in a territory-wide cohort of patients with chronic viral hepatitis (CVH) using data from the Hospital Authority Data Collaboration Lab (HADCL).
Methods: This was a territory-wide, retrospective, observational, cohort study of patients with CVH in Hong Kong in 2000-2018 identified from HADCL based on viral markers, diagnosis codes, and antiviral treatment for chronic hepatitis B and/or C.