Journal of Clinical and Experimental Hepatology最新文献

英文中文

Innovations to Improve Survival in Acute Liver Failure 提高急性肝衰竭患者存活率的创新方法

IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-20 DOI: 10.1016/j.jceh.2025.103190

Akash Roy , Madhumita Premkumar

Acute liver failure (ALF) is a medical emergency with high mortality rates. Liver transplantation is the optimal treatment for eligible patients. However, it necessitates access to transplantation services, significant financial resources, and frequently poses challenges in ensuring safe transportation. Advances in intensive care have improved survival rates from 20% to over 60%. Key factors include early prognostication, dynamic modeling, neurocritical monitoring, and liver support systems. There is a significant need for better public health services for preventing and managing ALF in India, highlighting the importance of innovative healthcare delivery and algorithm-based care. Recent advancements in liver support systems, novel pharmacological approaches, and enhanced critical care protocols can improve transplant-free survival in ALF. Innovative strategies like early and accessible plasma exchange (PLEX) for rodenticide poisoning-related ALF in Tamil Nadu have offered hope for improving public health services to provide innovative therapeutics in resource-constrained settings. This comprehensive review aims to explore the latest advancements in the management of ALF covering pathobiology, prognostic scores and biomarkers, noninvasive monitoring of intracranial hypertension (optic nerve sheath diameter and transcranial Doppler) and the use of modalities such as PLEX, and continuous renal replacement therapy. We highlight advancements and explore future innovations to enhance outcomes for individuals with ALF. Additionally, we address epidemiological changes in ALF in India and the associated challenges for healthcare policy.

急性肝衰竭（ALF）是一种死亡率很高的医学急症。肝移植是符合条件的患者的最佳治疗方法。然而，它需要获得移植服务，大量的财政资源，并经常在确保安全运输方面带来挑战。重症监护的进步使存活率从20%提高到60%以上。关键因素包括早期预测、动态建模、神经危重症监测和肝脏支持系统。印度迫切需要更好的公共卫生服务，以预防和管理肺痨，这突出了创新的保健服务和基于算法的护理的重要性。肝脏支持系统的最新进展、新的药理方法和增强的重症监护方案可以提高ALF的无移植生存率。泰米尔纳德邦早期和可获得血浆交换（PLEX）等创新战略为改善公共卫生服务提供了希望，以便在资源有限的情况下提供创新治疗方法。本综述旨在探讨ALF治疗的最新进展，包括病理生物学、预后评分和生物标志物、颅内高压的无创监测（视神经鞘直径和经颅多普勒）以及PLEX和持续肾替代治疗等模式的使用。我们强调进步并探索未来的创新，以提高ALF患者的治疗效果。此外，我们还讨论了印度ALF的流行病学变化以及医疗保健政策面临的相关挑战。

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引用次数: 0

Radiological Interventions in Pediatric Budd-Chiari Syndrome: Current Trends and Review of Literature 儿童布-恰里综合征的放射干预：当前趋势和文献综述

IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-19 DOI: 10.1016/j.jceh.2025.103189

Mohak Narang, Sanjay Sharma, Kumble S. Madhusudhan

Pediatric Budd-Chiari syndrome (BCS) is a rare but serious vascular disorder of the liver characterized by obstruction of the hepatic venous outflow leading to portal hypertension and liver dysfunction. Radiological endovascular interventions have revolutionized its management by providing minimally invasive options to restore venous patency and improve clinical outcomes. Early interventions are critical to prevent irreversible hepatic damage. Comparative studies highlight that endovascular therapies have high technical and clinical success with low complication rates. This review consolidates current evidence on the role of hepatic vein and inferior vena cava angioplasty, stenting, mechanical thromboaspiration, transjugular intrahepatic portosystemic shunt (TIPS), and direct intrahepatic portosystemic shunt (DIPS) in children with BCS. Doppler ultrasonography (US) remains the primary diagnostic modality, accurately localizing venous obstructions and guiding interventions. Post-procedural anticoagulation and surveillance with Doppler US are essential for long-term optimization. Novel techniques like 2D shear wave elastography enable non-invasive assessment of liver and splenic stiffness, reflecting fibrosis regression and hemodynamic improvement over time, and are being increasingly used for response assessment. This review underscores the evolving role of radiological endovascular techniques as first-line management for pediatric BCS, drawing upon established techniques and recent advancements to optimize patient outcomes.

小儿Budd-Chiari综合征（BCS）是一种罕见但严重的肝脏血管疾病，其特征是肝静脉流出受阻，导致门静脉高压和肝功能障碍。放射血管内干预通过提供微创选择来恢复静脉通畅和改善临床结果，彻底改变了其管理。早期干预对于预防不可逆转的肝损害至关重要。比较研究表明，血管内治疗具有很高的技术和临床成功率，并发症发生率低。这篇综述巩固了目前关于肝静脉和下腔静脉血管成形术、支架植入、机械血栓抽吸、经颈静脉肝内门静脉分流术（TIPS）和直接肝内门静脉分流术（DIPS）在BCS儿童中的作用的证据。多普勒超声（US）仍然是主要的诊断方式，准确定位静脉阻塞和指导干预。术后抗凝和多普勒超声监测对长期优化至关重要。2D横波弹性成像等新技术能够无创地评估肝脏和脾脏僵硬度，反映纤维化消退和血液动力学随时间的改善，并越来越多地用于反应评估。这篇综述强调了放射血管内技术作为儿科BCS一线治疗的不断发展的作用，利用现有的技术和最新的进展来优化患者的预后。

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引用次数: 0

Look Beyond Platelets: Why Spleen Size Must Inform Prognosis in Metabolic Dysfunction–Associated Steatotic Liver Disease–Related Cirrhosis 超越血小板：为什么脾脏大小必须影响代谢功能障碍相关脂肪变性肝病相关肝硬化的预后

IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-18 DOI: 10.1016/j.jceh.2025.103187

Panlert Prasanwon, Sakkarin Chirapongsathorn

引用次数: 0

The Interaction of Human Factors and Resistance-associated Substitutions in Hepatitis C Elimination 丙型肝炎消除中人为因素与耐药性相关替代的相互作用

IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-17 DOI: 10.1016/j.jceh.2025.103188

Judah Kupferman, Paul Y. Kwo

引用次数: 0

Clinical Practice Guidelines: How Much to Trust and Follow? 临床实践指南：多少信任和遵循？

IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-13 DOI: 10.1016/j.jceh.2025.103185

Anugrah Dhooria, Rakesh Aggarwal

Clinical practice guidelines (CPGs) are aimed at guiding clinicians in making sound decisions and thus help optimize patient care. However, their development is a complex process, compromise with which can undermine the quality of the resultant CPG. The foremost risk lies in conflict of interest on part of those developing the CPG. In addition, formulation of a good-quality CPG requires balanced composition of the development panel, formulation of relevant clinical questions, use of rigorous systematic review methodology, well-defined processes for rating of evidence and grading of recommendations, complete transparency of processes, and full disclosure regarding funding and sponsorship.

This article reviews the steps in the formulation of a CPG, and various considerations that determine the quality of a CPG. It also discusses the common pitfalls in their development, and the issue of existence of multiple conflicting CPGs on the same topic, using guidelines from India on hepatocellular carcinoma published in this journal and elsewhere as an example.

临床实践指南（CPGs）旨在指导临床医生做出合理的决策，从而帮助优化患者护理。然而，他们的发展是一个复杂的过程，妥协可能会破坏最终CPG的质量。最重要的风险在于CPG的开发人员之间的利益冲突。此外，制定高质量的CPG需要开发小组的平衡组成，制定相关临床问题，使用严格的系统审查方法，明确定义证据评级和建议分级的流程，流程完全透明，并充分披露有关资金和赞助的信息。本文回顾了制定CPG的步骤，以及决定CPG质量的各种考虑因素。它还讨论了它们发展中的常见缺陷，以及在同一主题上存在多个相互冲突的cpg的问题，并以本杂志和其他地方发表的印度肝细胞癌指南为例。

引用次数: 0

Artificial Intelligence for Predictive Diagnostics, Prognosis, and Decision Support in MASLD, Hepatocellular Carcinoma, and Digital Pathology 人工智能在MASLD、肝细胞癌和数字病理学中的预测诊断、预后和决策支持

IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-06 DOI: 10.1016/j.jceh.2025.103184

Nicholas Dunn , Nipun Verma , Winston Dunn

Artificial intelligence (AI) has fundamentally transformed the landscape of hepatology by enhancing disease diagnosis, risk stratification, and decision support. In metabolic dysfunction–associated steatotic liver disease (MASLD), AI has been integrated into large-scale consortia such as NIMBLE, LITMUS, TARGET-NASH, and SteatoSITE to improve diagnostic accuracy and patient management. These consortia utilize AI to derive and validate non-invasive biomarkers in fibrosis staging. AI-based models also enhance the detection of hepatocyte ballooning and metabolic dysfunction–associated steatohepatitis, minimizing interobserver variability and improving clinical trial enrollment criteria. Additionally, AI applications differentiate MASLD from alcohol-associated liver disease using gut microbiome and metabolic profiling.

In hepatocellular carcinoma (HCC), AI has improved risk stratification, diagnosis, and prognostication. AI-driven models based on liver stiffness and clinical parameters can risk stratify patients for HCC development. Enhanced imaging techniques, radiomics, and histopathology powered by AI improve the accuracy of detecting indeterminate liver nodules and predicting microvascular invasion. AI also improves treatment response prediction for therapies such as transarterial chemoembolization (TACE) and immune checkpoint inhibitors and thereby individualizes therapeutic strategies and improves survival outcomes.

In digital pathology, AI has redefined fibrosis staging, donor liver steatosis assessment, and disease diagnosis. FibroNest™ and qFibrosis are two exceptional AI platforms that utilize imaging techniques for the purposes of both standardizing histological assessments, as well as increasing diagnostic precision. The field of MASLD, HCC, and digital pathology is advancing towards precision medicine.

FibroNest™ and qFibrosis are two exceptional AI platforms that utilize imaging techniques for the purposes of both standardizing histological assessments, as well as increasing diagnostic precision.

人工智能（AI）通过增强疾病诊断、风险分层和决策支持，从根本上改变了肝病学的格局。在代谢功能障碍相关的脂肪变性肝病（MASLD）中，人工智能已被整合到大型联盟中，如NIMBLE、LITMUS、TARGET-NASH和SteatoSITE，以提高诊断准确性和患者管理。这些联盟利用人工智能来推导和验证纤维化分期的非侵入性生物标志物。基于人工智能的模型还增强了肝细胞膨胀和代谢功能障碍相关脂肪性肝炎的检测，最大限度地减少了观察者之间的差异，并改善了临床试验的入组标准。此外，人工智能应用程序通过肠道微生物组和代谢谱来区分MASLD与酒精相关的肝脏疾病。在肝细胞癌（HCC）中，人工智能改善了风险分层、诊断和预后。基于肝脏硬度和临床参数的人工智能驱动模型可以对HCC发展的患者进行风险分层。人工智能增强的成像技术、放射组学和组织病理学提高了检测不确定肝结节和预测微血管侵袭的准确性。人工智能还可以改善经动脉化疗栓塞（TACE）和免疫检查点抑制剂等疗法的治疗反应预测，从而使治疗策略个性化，提高生存结果。在数字病理学中，人工智能重新定义了纤维化分期、供肝脂肪变性评估和疾病诊断。FibroNest™和qFibrosis是两个卓越的人工智能平台，它们利用成像技术来标准化组织学评估，并提高诊断精度。MASLD、HCC和数字病理学领域正朝着精准医学的方向发展。FibroNest™和qFibrosis是两个卓越的人工智能平台，它们利用成像技术来标准化组织学评估，并提高诊断精度。

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引用次数: 0

Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know 肝病学人工智能的基础：临床医生需要知道什么

IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-05 DOI: 10.1016/j.jceh.2025.103183

Nana Peng , Sherlot J. Song , Vicki Wing-Ki Hui , Jimmy Che-To Lai , Grace Lai-Hung Wong , Vincent Wai-Sun Wong , Terry Cheuk-Fung Yip

This review focuses on foundational knowledge about artificial intelligence (AI) in hepatology, exploring how AI, including machine learning and deep learning, leverages large-scale clinical data to transform the diagnosis, risk assessment, prognostication, and management of liver diseases. Online resources are described to offer fundamental AI knowledge and essential technical skills and to facilitate clinician participation across the entire AI lifecycle, ensuring they contribute not only as end users but also in development and deployment. Unlike traditional statistical approaches that prioritize interpretable parameters and clinical insight, AI focuses on maximizing predictive accuracy by identifying complex, often non-linear patterns using high-dimensional data, albeit often at the cost of model interpretability. AI is demonstrating clinical utility in liver histopathology and radiological imaging, significantly improving detection accuracy for cirrhosis, clinically significant portal hypertension, and hepatocellular carcinoma. Beyond diagnostics, AI-driven prediction models are emerging to provide personalized risk stratification for the development of liver-related complications and treatment guidance, based on complex data including longitudinal laboratory results, comorbidities, and co-medication use to monitor disease progression and therapy response. The field is rapidly expanding into novel areas such as analyzing patient-reported outcomes, genomic data, and real-time liver function monitoring, offering deeper mechanistic insights alongside clinical tools. Despite the potential to revolutionize hepatology practice and research, successful integration into routine care faces challenges. These include seamless workflow integration with existing electronic health records, establishing clear liability frameworks, and guaranteeing protection of patient privacy. Addressing these hurdles requires collaborative efforts from clinicians, researchers, and regulators to develop best practices and governance. Understanding the transformative capabilities, current applications, emerging frontiers, and essential implementation considerations is crucial for clinicians navigating the evolving AI landscape and responsibly utilizing its power for improved patient outcomes.

本文综述了人工智能（AI）在肝病学中的基础知识，探讨了包括机器学习和深度学习在内的人工智能如何利用大规模临床数据来改变肝脏疾病的诊断、风险评估、预后和管理。在线资源被描述为提供基本的人工智能知识和基本的技术技能，并促进临床医生在整个人工智能生命周期中的参与，确保他们不仅作为最终用户，而且在开发和部署中做出贡献。与优先考虑可解释参数和临床洞察力的传统统计方法不同，人工智能侧重于通过使用高维数据识别复杂的、通常是非线性的模式来最大限度地提高预测准确性，尽管通常以模型可解释性为代价。人工智能在肝脏组织病理学和放射学成像方面的临床应用，显著提高了肝硬化、临床表现明显的门脉高压和肝细胞癌的检测准确性。除了诊断，人工智能驱动的预测模型正在兴起，为肝脏相关并发症的发展提供个性化的风险分层和治疗指导，基于复杂的数据，包括纵向实验室结果、合并症和联合用药，以监测疾病进展和治疗反应。该领域正在迅速扩展到新的领域，如分析患者报告的结果、基因组数据和实时肝功能监测，为临床工具提供更深入的机制见解。尽管有可能彻底改变肝病学实践和研究，但成功融入常规护理面临挑战。这些措施包括与现有电子健康记录无缝集成工作流程、建立明确的责任框架以及确保保护患者隐私。解决这些障碍需要临床医生、研究人员和监管机构共同努力，以制定最佳实践和治理。了解变革能力、当前应用、新兴领域和基本实施考虑因素对于临床医生驾驭不断发展的人工智能环境并负责任地利用其力量改善患者预后至关重要。

{"title":"Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know","authors":"Nana Peng , Sherlot J. Song , Vicki Wing-Ki Hui , Jimmy Che-To Lai , Grace Lai-Hung Wong , Vincent Wai-Sun Wong , Terry Cheuk-Fung Yip","doi":"10.1016/j.jceh.2025.103183","DOIUrl":"10.1016/j.jceh.2025.103183","url":null,"abstract":"<div><div>This review focuses on foundational knowledge about artificial intelligence (AI) in hepatology, exploring how AI, including machine learning and deep learning, leverages large-scale clinical data to transform the diagnosis, risk assessment, prognostication, and management of liver diseases. Online resources are described to offer fundamental AI knowledge and essential technical skills and to facilitate clinician participation across the entire AI lifecycle, ensuring they contribute not only as end users but also in development and deployment. Unlike traditional statistical approaches that prioritize interpretable parameters and clinical insight, AI focuses on maximizing predictive accuracy by identifying complex, often non-linear patterns using high-dimensional data, albeit often at the cost of model interpretability. AI is demonstrating clinical utility in liver histopathology and radiological imaging, significantly improving detection accuracy for cirrhosis, clinically significant portal hypertension, and hepatocellular carcinoma. Beyond diagnostics, AI-driven prediction models are emerging to provide personalized risk stratification for the development of liver-related complications and treatment guidance, based on complex data including longitudinal laboratory results, comorbidities, and co-medication use to monitor disease progression and therapy response. The field is rapidly expanding into novel areas such as analyzing patient-reported outcomes, genomic data, and real-time liver function monitoring, offering deeper mechanistic insights alongside clinical tools. Despite the potential to revolutionize hepatology practice and research, successful integration into routine care faces challenges. These include seamless workflow integration with existing electronic health records, establishing clear liability frameworks, and guaranteeing protection of patient privacy. Addressing these hurdles requires collaborative efforts from clinicians, researchers, and regulators to develop best practices and governance. Understanding the transformative capabilities, current applications, emerging frontiers, and essential implementation considerations is crucial for clinicians navigating the evolving AI landscape and responsibly utilizing its power for improved patient outcomes.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 1","pages":"Article 103183"},"PeriodicalIF":3.2,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Prevailing Role of Diabetes Mellitus Among Cardiometabolic Risk Factors in Metabolic Dysfunction-associated Steatotic Liver Disease Prognostication. 糖尿病在代谢功能障碍相关脂肪变性肝病预后中的心脏代谢危险因素中的主要作用

IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-01 Epub Date: 2025-07-21 DOI: 10.1016/j.jceh.2025.103119

Karen Cheuk-Ying Ho, Lung-Yi Mak

引用次数: 0

Role of Magnetic Resonance Imaging in Evaluating Donor Eligibility for Living Donor Liver Transplantation: Present Status and Future Directions 磁共振成像在评估活体肝移植供者资格中的作用：现状和未来方向

IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-01 DOI: 10.1016/j.jceh.2025.103182

Ruchi Rastogi , Subash Gupta , Sanjiv Saigal , Mukesh Kumar , Aditi Rastogi , Bharat Aggarwal

Contrast-enhanced computed tomography (CECT) evaluation of a potential living donor liver transplantation (LDLT) donor is an established component of donor eligibility tests. Usually noncontrast magnetic resonance imaging (MRI) is performed with the aim of assessing biliary anatomy and liver fat fraction. While a few donors are considered ineligible for LDLT after CECT, primarily due to moderate liver steatosis or inadequate liver remnant, other hepatic or extrahepatic abnormalities may also preclude donation. Knowledge regarding vascular anatomy is essential to provide a roadmap to the surgeon but is seldom a reason for donor rejection with the developments in surgical technique and expertise.

Noncontrast MRI can be utilized to comprehensively screen eligible LDLT donors, even before CECT evaluation, as it provides a detailed hepatic and extrahepatic abdominal evaluation along with volumetric estimation without any extra expenditure. This practice not only helps to avoid undue exposure to CT radiation and iodinated contrast in unsuitable donors but also provides guidance for pretransplant modifications in terms of weight reduction in marginal donors with borderline high-fat content by taking advantage of the robust MRI-based liver fat estimation.

对比增强计算机断层扫描（CECT）评估潜在的活体肝移植（LDLT）供体是供体资格测试的一个既定组成部分。通常进行非对比磁共振成像（MRI）的目的是评估胆道解剖和肝脏脂肪分数。虽然少数供者被认为不适合在CECT后进行LDLT，主要是由于中度肝脂肪变性或肝残余不足，但其他肝脏或肝外异常也可能排除捐赠。血管解剖学知识对于外科医生的指导是必不可少的，但随着手术技术和专业知识的发展，它很少成为供体排斥的原因。非对比MRI可用于全面筛查合格的LDLT供体，甚至在CECT评估之前，因为它提供了详细的肝脏和肝外腹部评估以及体积估计，而无需任何额外支出。这种做法不仅有助于避免不合适供体的过度暴露于CT辐射和碘化造影剂，而且还可以利用基于mri的可靠肝脏脂肪估计，为具有临界高脂肪含量的边缘供体的移植前减重提供指导。

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引用次数: 0

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IF 3.2 Q2 GASTROENTEROLOGY & HEPATOLOGY

Journal of Clinical and Experimental Hepatology

Pub Date : 2025-09-01 DOI: 10.1016/S0973-6883(25)00665-6

引用次数: 0

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