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Publications (2)15.07 Total impact

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    ABSTRACT: BACKGROUND & AIMS:: Little is known about the validity of hepatocellular carcinoma (HCC) risk scores derived from treatment-naïve patients with chronic hepatitis B for patients receiving entecavir. METHODS:: We performed a retrospective-prospective cohort study of 1531 patients with chronic hepatitis B (age, 51±12 years; 1099 male; 332 with clinical cirrhosis) who received entecavir (0.5 mg) daily for at least 12 months at the Prince of Wales Hospital in Hong Kong, from December 2005 to August 2012. The patients were assessed once every 3-6 months for symptoms, drug history, and adherence; blood samples were collected for biochemical analyses. We validated 3 HCC risk scores (CU-HCC, GAG-HCC, and REACH-B scores) based on data collected when patients began entecavir therapy and 2 years later. RESULTS:: After 42±13 months of follow up, 47 patients (2.9%) developed HCC. The 5-year cumulative incidence of HCC was 4.3% (95% confidence interval [CI], 3.6%-5.0%). Old age, cirrhosis, and virologic remission after 24 months or more of therapy were independently associated with HCC in the entire cohort; advanced age and hypoalbuminemia were associated with HCC in patients without cirrhosis. The areas under receiver operating characteristic (AUC) curves for baseline CU-HCC, GAG-HCC, and REACH-B scores for HCC were 0.80 (95% CI, 0.75-0.86), 0.76 (95% CI, 0.70-0.82), and 0.71 (95% CI, 0.62-0.81) respectively; the time-dependent AUCs 1 to 4 years after patients started treatment were comparable to those at baseline. The cutoff value of the baseline CU-HCC score identified patients who would develop HCC with 93.6% sensitivity and 47.8% specificity, the baseline GAG-HCC score with 55.3% sensitivity and 78.9% specificity, and the baseline REACH-B score with 95.2% sensitivity and 16.5% specificity. Compared to patients with CU-HCC scores <5 at baseline, those with CU-HCC scores that either decreased from =5 to <5, or remained =5, had a higher risk of HCC (5-year cumulative incidences: 0% vs 3.9% and 7.3%; P =.002 and <.001 respectively). CONCLUSIONS:: The CU-HCC, GAG-HCC, and REACH-B HCC risk scores accurately predict which patients with chronic hepatitis B treated with entecavir will develop HCC.
    Gastroenterology 02/2013; · 12.82 Impact Factor
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    ABSTRACT: Extraction of meaningful information from large experimental data sets is a key element in bioinformatics research. One of the challenges is to identify genomic markers in Hepatitis B Virus (HBV) that are associated with HCC (liver cancer) development by comparing the complete genomic sequences of HBV among patients with HCC and those without HCC. In this study, a data mining framework, which includes molecular evolution analysis, clustering, feature selection, classifier learning, and classification, is introduced. Our research group has collected HBV DNA sequences, either genotype B or C, from over 200 patients specifically for this project. In the molecular evolution analysis and clustering, three subgroups have been identified in genotype C and a clustering method has been developed to separate the subgroups. In the feature selection process, potential markers are selected based on Information Gain for further classifier learning. Then, meaningful rules are learned by our algorithm called the Rule Learning, which is based on Evolutionary Algorithm. Also, a new classification method by Nonlinear Integral has been developed. Good performance of this method comes from the use of the fuzzy measure and the relevant nonlinear integral. The nonadditivity of the fuzzy measure reflects the importance of the feature attributes as well as their interactions. These two classifiers give explicit information on the importance of the individual mutated sites and their interactions toward the classification (potential causes of liver cancer in our case). A thorough comparison study of these two methods with existing methods is detailed. For genotype B, genotype C subgroups C1, C2, and C3, important mutation markers (sites) have been found, respectively. These two classification methods have been applied to classify never-seen-before examples for validation. The results show that the classification methods have more than 70 percent accuracy and 80 percent sensitivity for most data sets, which are considered high as an initial scanning method for liver cancer diagnosis.
    IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 03/2011; 8(2):428-40. · 2.25 Impact Factor