Intermediate-stage hepatocellular carcinoma (HCC) encompasses a diverse patient population that requires individualized treatment strategies and a multidisciplinary approach. Recent advancements in systemic therapy have expanded the therapeutic options for intermediate-stage HCC, allowing for combination strategies such as systemic therapy with transarterial chemoembolization (TACE) and upfront systemic therapy for individuals deemed unsuitable for TACE. Additionally, the ongoing development of treatment modalities for intermediate-stage HCC has improved the potential for curative conversion and tumor downstaging. Nevertheless, consensus on the optimal management of intermediate-stage HCC remains limited. Thus, the primary aim of this study was to develop a set of consensus guidelines for the management of intermediate-stage HCC. To address this gap, the Taiwan Liver Cancer Association (TLCA) established a working group to develop a multidisciplinary strategy for managing intermediate-stage HCC. Here, we present eight consensus statements formulated by this expert panel, which outline criteria for TACE unsuitability, treatment recommendations based on TACE eligibility, and considerations for various modalities, including conventional TACE, drug-eluting bead TACE, and transarterial radioembolization, as well as the appropriate timing for initiating systemic therapy to enable curative conversion and downstaging. These statements provide specific, evidence-based recommendations for clinicians, addressing treatment pathways based on TACE eligibility and other key considerations for intermediate-stage HCC management. The development of this consensus guideline is intended to aid clinicians in selecting the most appropriate treatment pathway for intermediate-stage HCC, support personalized treatment planning, and ultimately enhance the feasibility of achieving curative conversion.
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Liver disease has emerged as a critical and escalating public health concern worldwide, with the Asia-Pacific region at the forefront of this challenge due to its vast population and diverse socioeconomic landscape. Over the coming five decades, this region will experience profound changes in liver disease patterns, shaped by rapid urbanization, lifestyle modifications, advancements in medical technologies, and evolving public health strategies. This article offers an in-depth analysis of six transformative areas defining the trajectory of liver disease in the region. First, it highlights the alarming rise of metabolic dysfunction-associated fatty liver disease and metabolic dysfunction-associated steatohepatitis, diseases driven by modern lifestyle factors and inherent metabolic susceptibilities. Concurrently, it celebrates the declining burden of viral hepatitis, underscoring the success of sustained public health interventions. However, new challenges are emerging, such as the growing impact of environmental and occupational exposures on liver health. Breakthroughs in genomic and epigenetic research promise to advance precision medicine, offering targeted therapeutic solutions. Additionally, the integration of artificial intelligence, big data, and telemedicine is poised to revolutionize liver disease management, improving accessibility and personalized care. Finally, the article emphasizes the critical role of robust health policies, preventive strategies, and cross-border collaboration in shaping a healthier future. By synthesizing these insights, the study aims to guide innovative and effective responses to the evolving liver disease landscape in the Asia-Pacific region.
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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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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 The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups.
Methods The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort.
Result s: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores.
Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.
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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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