Dataset Information

Risk of Hepatocellular Carcinoma Remains High in Patients with HBV-Related Decompensated Cirrhosis and Long-Term Antiviral Therapy.

ABSTRACT:

Background

This study aimed to evaluate the risk factors of HCC development in patients with hepatitis B virus (HBV)-related DC and who underwent long-term antiviral therapy.

Methods

Data from 308 patients with HBV-related DC and long-term antiviral therapy were collected and retrospectively reviewed. Cox regression analysis was used to analyze independent risk factors of HCC development.

Results

Data from 129 patients with definite records were analyzed. The median follow-up time was 5 years (range, 1 to 8 years). At the end of the follow-up, 41 (31.8%) patients developed HCC, and the time from DC diagnosis to HCC incidence who received antiviral therapy was 4.4 years (range, 1-7 years). The incidence of HCC was higher in males (30/78, 38.5%) than in females (11/51, 21.6%) (P?=?0.04). Patients who developed HCC were significantly older than those who did not develop HCC (P?P?=?0.95). Multivariate Cox regression analysis demonstrated that male gender and age ?50 years are independent risk factors of HCC development (OR?=?2.987 and 2.408; 95% CI (1.301-6.858) and (1.126-5.149); P?=?0.01 and 0.02, respectively).

Conclusion

The risk of HCC remains to be high in patients with HBV-related DC, especially in males aged ?50 years.

SUBMITTER: Zhu DM

PROVIDER: S-EPMC7762653 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Risk of Hepatocellular Carcinoma Remains High in Patients with HBV-Related Decompensated Cirrhosis and Long-Term Antiviral Therapy.

Zhu Dong-Mei DM Xie Jing J Ye Chun-Yan CY Qian Mei-Yun MY Xue Yuan Y

Canadian journal of gastroenterology & hepatology 20201217

<h4>Background</h4>This study aimed to evaluate the risk factors of HCC development in patients with hepatitis B virus (HBV)-related DC and who underwent long-term antiviral therapy.<h4>Methods</h4>Data from 308 patients with HBV-related DC and long-term antiviral therapy were collected and retrospectively reviewed. Cox regression analysis was used to analyze independent risk factors of HCC development.<h4>Results</h4>Data from 129 patients with definite records were analyzed. The median follow- ...[more]

PMID: 33381476

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Project description:Background & aimsPersonalised risk prediction of the development of hepatocellular carcinoma (HCC) among patients with liver cirrhosis on potent antiviral therapy is important for targeted screening and individualised intervention. This study aimed to develop and validate a new model for risk prediction of HCC development based on deep learning, and to compare it with previously reported risk models.MethodsA novel deep-learning-based model was developed from a cohort of 424 patients with HBV-related cirrhosis on entecavir therapy with 2 residual blocks, including 7 layers of a neural network, and it was validated using an independent external cohort (n = 316). The deep-learning-based model was compared to 6 previously reported models (platelet, age, and gender-hepatitis B score [PAGE-B], Chinese University HCC score [CU-HCC], HCC-Risk Estimating Score in CHB patients Under Entecavir [HCC-RESCUE], age, diabetes, race, etiology of cirrhosis, sex, and severity HCC score [ADRESS-HCC], modified PAGE-B score [mPAGE], and Toronto HCC risk index [THRI]) using Harrell's concordance (c)-index.ResultsDuring a median 5.2 yr of follow-up (inter-quartile range 2.8-6.9 yr), 86 patients (20.3%) developed HCC. The deep-learning-based model had a Harrell's c-index of 0.719 in the derivation cohort and 0.782 in the validation cohort. Goodness of fit was confirmed by the Hosmer-Lemeshow test (p >0.05). Moreover, this model in the validation cohort had the highest c-index among the 6 previously reported models: PAGE-B (0.570), CU-HCC (0.548), HCC-RESCUE (0.577), ADRESS-HCC (0.551), mPAGE (0.598), and THRI (0.587) (all p <0.001). The misclassification rate of this model was 23.7% (model accuracy: 76.3%) in the validation group.ConclusionsThe deep-learning-based model had better performance than the previous models for predicting the HCC risk in patients with HBV-related cirrhosis on potent antivirals.Lay summaryFor early detection of hepatocellular carcinoma, it is important to maintain regular surveillance. However, there is currently no standard prediction model for risk stratification that can be used to establish a personalised surveillance strategy. We develop and validate a deep-learning-based model that showed better performance than previous models.

Dataset Information

Risk of Hepatocellular Carcinoma Remains High in Patients with HBV-Related Decompensated Cirrhosis and Long-Term Antiviral Therapy.

Background

Methods

Results

Conclusion

Publications

Risk of Hepatocellular Carcinoma Remains High in Patients with HBV-Related Decompensated Cirrhosis and Long-Term Antiviral Therapy.

Similar Datasets

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