Background/Aims The survival benefit of direct-acting antiviral (DAA) therapy for hepatitis C virus (HCV) infection in patients with hepatocellular carcinoma (HCC), particularly in Barcelona Clinic Liver Cancer (BCLC) stages B/C, remains largely uncertain. We aimed to explore the impact of DAA therapy on overall survival (OS) in HCC patients using a nationwide cohort study.
Methods We utilized the nationwide Taiwan Association for the Study of the Liver (TASL) HCV Registry (TACR) database to include all adults receiving a DAA therapy for HCV, excluding those with other viral infections, liver transplantation, non-HCC malignancies, and terminal-staged HCC. We respectively analyzed the adjusted odds ratio (aOR) for sustained virological response (SVR) and adjusted hazard ratio (aHR) for OS.
Result s: Between December 2013 and December 2020, 2,205 (9.3%) patients with HCC and 21,569 (90.7%) patients without HCC were include. The SVR rates were 96.6% in the HCC group and 98.8% in the non-HCC group (P<0.001), with HCC being an independent risk factor affecting SVR (aOR 0.41; 95% CI 0.31–0.54; P<0.001). In the whole patient cohort, SVR was independently associated with improved OS (aHR 0.46; 95% CI 0.35–0.60; P<0.001). Among patients with baseline HCC, SVR remained an independent factor related to OS (aHR 0.41; 95% CI 0.28–0.59; P<0.001). The impact of SVR on OS persisted significantly across BCLC stages 0/A and stages B/C.
Conclusions High SVR rates among HCC patients underscore the importance of DAA therapy in enhancing OS, reaffirming its efficacy across various HCC stages.
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Revisiting unmet needs in clinical research on direct-acting antiviral therapy for HCC patients: Correspondence to letter to the editor on “Direct-acting antiviral therapy for patients with HCV-related hepatocellular carcinoma: A nationwide cohort study” Teng-Yu Lee, Pei-Chien Tsai, Shou-Wu Lee, Ming- Lung Yu Clinical and Molecular Hepatology.2026; 32(1): e99. CrossRef
Emerging evidence supports direct-acting antiviral therapy for HCC patients beyond the early stage: Correspondence to editorial on “Direct-acting antiviral therapy for patients with HCV-related hepatocellular carcinoma: A nationwide cohort study” Teng-Yu Lee, Pei-Chien Tsai, Shou-Wu Lee, Ming-Lung Yu Clinical and Molecular Hepatology.2026; 32(1): e68. CrossRef
Survival impact of hepatitis C virus eradication in patients with or without active hepatocellular carcinoma: A nationwide cohort study Teng-Yu Lee, Sheng-Shun Yang, Pei-Chien Tsai, Chung-Feng Huang, Chi-Yi Chen, Chao-Hung Hung, Chien-Hung Chen, Chi-Ming Tai, Pin-Nan Cheng, Hsing-Tao Kuo, Kuo-Chih Tseng, Lein-Ray Mo, Ching-Chu Lo, Yi-Hsiang Huang, Han-Chieh Lin, Pei-Lun Lee, Ming-Jong Bai European Journal of Cancer.2026; 232: 116109. CrossRef
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Setting the Record Straight: Utility and Outcomes in Patients With HCV Related HCC María Fernanda Guerra‐Veloz, Sital Shah, Beatrice Emmanouil, Mia Olsen, Renita George, Sarah Selemani, Paul J. Ross, Ivana Carey, Neha Mehta, Mark Gillyon‐Powell, Kosh Agarwal Journal of Viral Hepatitis.2026;[Epub] CrossRef
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Background/Aims Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients.
Methods We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development.
Result s: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients.
Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.
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Background/Aims Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy.
Methods We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment.
Result s: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset.
Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.
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