Dataset Information

Prediction models of hepatocellular carcinoma development in chronic hepatitis B patients.

ABSTRACT: Chronic hepatitis B virus (HBV) infection is a major cause of cirrhosis and hepatocellular carcinoma (HCC). Applying the same strategies for antiviral therapy and HCC surveillance to all chronic hepatitis B (CHB) patients would be a burden worldwide. To properly manage CHB patients, it is necessary to identify and classify the risk for HCC development in such patients. Several HCC risk scores based on risk factors such as cirrhosis, age, male gender, and high viral load have been used, and have negative predictive values of ? 95%. Most of these have been derived from, and internally validated in, treatment-naïve Asian CHB patients. Herein, we summarized various HCC prediction models, including IPM (Individual Prediction Model), CU-HCC (Chinese University-HCC), GAG-HCC (Guide with Age, Gender, HBV DNA, Core Promoter Mutations and Cirrhosis-HCC), NGM-HCC (Nomogram-HCC), REACH-B (Risk Estimation for Hepatocellular Carcinoma in Chronic Hepatitis B), and Page-B score. To develop a noninvasive test of liver fibrosis, we also introduced a new scoring system that uses liver stiffness values from transient elastography, including an LSM (Liver Stiffness Measurement)-based model, LSM-HCC, and mREACH-B (modified REACH-B).

SUBMITTER: Lee HW

PROVIDER: S-EPMC5055862 | biostudies-other | 2016 Oct

REPOSITORIES: biostudies-other

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Prediction models of hepatocellular carcinoma development in chronic hepatitis B patients.

Lee Hye Won HW Ahn Sang Hoon SH

World journal of gastroenterology 20161001 37

Chronic hepatitis B virus (HBV) infection is a major cause of cirrhosis and hepatocellular carcinoma (HCC). Applying the same strategies for antiviral therapy and HCC surveillance to all chronic hepatitis B (CHB) patients would be a burden worldwide. To properly manage CHB patients, it is necessary to identify and classify the risk for HCC development in such patients. Several HCC risk scores based on risk factors such as cirrhosis, age, male gender, and high viral load have been used, and have ...[more]

PMID: 27729738

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Project description:Background & aimsAccurate 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).MethodsThis 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. The cohort was randomly split into training and validation cohorts in a 7:3 ratio. Five popular machine learning methods, namely, logistic regression, ridge regression, AdaBoost, decision tree, and random forest, were performed and compared to find the best prediction model.ResultsA total of 124,006 patients with CVH with complete data were included to build the models. In the training cohort (n = 86,804; 6,821 HCC), ridge regression (area under the receiver operating characteristic curve [AUROC] 0.842), decision tree (0.952), and random forest (0.992) performed the best. In the validation cohort (n = 37,202; 2,875 HCC), ridge regression (AUROC 0.844) and random forest (0.837) maintained their accuracy, which was significantly higher than those of HCC risk scores: CU-HCC (0.672), GAG-HCC (0.745), REACH-B (0.671), PAGE-B (0.748), and REAL-B (0.712) scores. The low cut-off (0.07) of HCC ridge score (HCC-RS) achieved 90.0% sensitivity and 98.6% negative predictive value (NPV) in the validation cohort. The high cut-off (0.15) of HCC-RS achieved high specificity (90.0%) and NPV (95.6%); 31.1% of patients remained indeterminate.ConclusionsHCC-RS from the ridge regression machine learning model accurately predicted HCC in patients with CVH. These machine learning models may be developed as built-in functional keys or calculators in electronic health systems to reduce cancer mortality.Lay summaryNovel machine learning models generated accurate risk scores for hepatocellular carcinoma (HCC) in patients with chronic viral hepatitis. HCC ridge score was consistently more accurate than existing HCC risk scores. These models may be incorporated into electronic medical health systems to develop appropriate cancer surveillance strategies and reduce cancer death.

Dataset Information

Prediction models of hepatocellular carcinoma development in chronic hepatitis B patients.

Publications

Prediction models of hepatocellular carcinoma development in chronic hepatitis B patients.

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