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Ferraioli G, Barr RG, Berzigotti A, Sporea I, Wong VWS, Reiberger T, Karlas T, Thiele M, Cardoso AC, Ayonrinde OT, Castera L, Dietrich CF, Iijima H, Lee DH, Kemp W, Oliveira CP, Sarin SK. WFUMB Guideline/Guidance on Liver Multiparametric Ultrasound: Part 1. Update to 2018 Guidelines on Liver Ultrasound Elastography. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1071-1087. [PMID: 38762390 DOI: 10.1016/j.ultrasmedbio.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 05/20/2024]
Abstract
The World Federation for Ultrasound in Medicine and Biology (WFUMB) endorsed the development of this document on multiparametric ultrasound. Part 1 is an update to the WFUMB Liver Elastography Guidelines Update released in 2018 and provides new evidence on the role of ultrasound elastography in chronic liver disease. The recommendations in this update were made and graded using the Oxford classification, including level of evidence (LoE), grade of recommendation (GoR) and proportion of agreement (Oxford Centre for Evidence-Based Medicine [OCEBM] 2009). The guidelines are clinically oriented, and the role of shear wave elastography in both fibrosis staging and prognostication in different etiologies of liver disease is discussed, highlighting advantages and limitations. A comprehensive section is devoted to the assessment of portal hypertension, with specific recommendations for the interpretation of liver and spleen stiffness measurements in this setting.
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Affiliation(s)
- Giovanna Ferraioli
- Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
| | - Richard Gary Barr
- Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio, USA; Southwoods Imaging, Youngstown, Ohio, USA
| | - Annalisa Berzigotti
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ioan Sporea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine II, Center for Advanced Research in Gastroenterology and Hepatology, "Victor Babeș" University of Medicine and Pharmacy, Timișoara, Romania
| | - Vincent Wai-Sun Wong
- Medical Data Analytics Centre, Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China; State Key Laboratory of Digestive Disease, Institute of Digestive Disease, Chinese University of Hong Kong, Hong Kong, China
| | - Thomas Reiberger
- Division of Gastroenterology and Hepatology, Department of Medicine III, Medical University of Vienna, Vienna, Austria; Christian-Doppler Laboratory for Portal Hypertension and Liver Fibrosis, Medical University of Vienna, Vienna, Austria
| | - Thomas Karlas
- Division of Gastroenterology, Department of Medicine II, Leipzig University Medical Center, Leipzig, Germany
| | - Maja Thiele
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ana Carolina Cardoso
- Hepatology Division, School of Medicine, Federal University of Rio de Janeiro, Clementino, Fraga Filho Hospital, Rua Prof. Rodolpho Paulo Rocco, Cidade Universitária da Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Oyekoya Taiwo Ayonrinde
- Department of Gastroenterology and Hepatology, Fiona Stanley Hospital, Murdoch, Western Australia, Australia; Medical School, University of Western Australia, Crawley, Western Australia, Australia; Curtin Medical School, Curtin University, Kent Street, Bentley, Western Australia, Australia
| | - Laurent Castera
- Université Paris-Cité, Inserm UMR1149, Centre de Recherche sur l'Inflammation, Paris, France; Service d'Hépatologie, Hôpital Beaujon, Assistance-Publique Hôpitaux de Paris, Clichy, France
| | - Christoph Frank Dietrich
- Department Allgemeine Innere Medizin (DAIM), Kliniken Hirslanden Beau Site, Salem and Permancence, Bern, Switzerland
| | - Hiroko Iijima
- Division of Hepatobiliary and Pancreatic Disease, Department of Gastroenterology, Hyogo Medical University, Nishinomiya, Hyogo, Japan; Ultrasound Imaging Center, Hyogo Medical University, Nishinomiya, Hyogo, Japan
| | - Dong Ho Lee
- Department of Radiology, Seoul National University Hospital, College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - William Kemp
- Department of Gastroenterology, Alfred Hospital, Melbourne, Australia; Department of Medicine, Central Clinical School, Monash University, Melbourne, Australia
| | - Claudia P Oliveira
- Gastroenterology Department, Laboratório de Investigação (LIM07), Hospital das Clínicas de São Paulo, HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Shiv Kumar Sarin
- Department of Hepatology, Institute of Liver and Biliary Sciences, Vasant Kunj, New Delhi, India
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Fan R, Yu N, Li G, Arshad T, Liu WY, Wong GLH, Liang X, Chen Y, Jin XZ, Leung HHW, Chen J, Wang XD, Yip TCF, Sanyal AJ, Sun J, Wong VWS, Zheng MH, Hou J. Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify MASLD-related liver fibrosis. Liver Int 2024; 44:749-759. [PMID: 38131420 DOI: 10.1111/liv.15818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/13/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND & AIMS aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B-related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD)-related fibrosis diagnosis and establish a machine-learning (ML) model to improve the diagnostic performance. METHODS A total of 946 biopsy-proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125). RESULTS The AUROCs of aMAP versus fibrosis-4 index (FIB-4) and aspartate aminotransferase-platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut-off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB-4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM-plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM-plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM-plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively). CONCLUSIONS The aMAP score is capable of diagnosing MASLD-related fibrosis. The LSM-plus model could accurately identify MASLD-related cirrhosis and advanced fibrosis.
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Affiliation(s)
- Rong Fan
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ning Yu
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Guanlin Li
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Tamoore Arshad
- Department of Internal Medicine, Virginia Commonwealth University, Richmond, Virginia, United States
| | - Wen-Yue Liu
- Department of Endocrinology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Grace Lai-Hung Wong
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Xieer Liang
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yongpeng Chen
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiao-Zhi Jin
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Howard Ho-Wai Leung
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Hong Kong, China
| | - Jinjun Chen
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiao-Dong Wang
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
| | - Terry Cheuk-Fung Yip
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Arun J Sanyal
- Division of Gastroenterology, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Jian Sun
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Vincent Wai-Sun Wong
- Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Ming-Hua Zheng
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
| | - Jinlin Hou
- Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Bera C, Hamdan-Perez N, Patel K. Non-Invasive Assessment of Liver Fibrosis in Hepatitis B Patients. J Clin Med 2024; 13:1046. [PMID: 38398358 PMCID: PMC10889471 DOI: 10.3390/jcm13041046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
The aim of this review is to provide updated information on the clinical use of non-invasive serum and imaging-based tests for fibrosis assessment in chronic hepatitis B (CHB) virus infection. In recent years, non-invasive tests (NIT) have been increasingly used to determine eligibility for treatment. Liver biopsy is still considered the gold standard for assessing inflammatory activity and fibrosis staging, but it is an invasive procedure with inherent limitations. Simple serum markers such as APRI and FIB-4 are limited by indeterminate results but remain useful initial tests for fibrosis severity if imaging elastography is not available. Point-of-care US-based elastography techniques, such as vibration-controlled transient elastography or 2D shear wave elastography, are increasingly available and have better accuracy than simple serum tests for advanced fibrosis or cirrhosis, although stiffness cut-offs are variable based on E-antigen status and inflammatory activity. Current NITs have poor diagnostic performance for following changes in fibrosis with antiviral therapy. However, NITs may have greater clinical utility for determining prognosis in patients with CHB that have advanced disease, especially for the development of hepatocellular carcinoma and/or liver decompensation. Algorithms combining serum and imaging NITs appear promising for advanced fibrosis and prognostic risk stratification.
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Affiliation(s)
- Chinmay Bera
- Division of Gastroenterology, University Health Network Toronto, Toronto General Hospital, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada; (N.H.-P.)
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Duan B, Liu Y, Li X, Han M, Yu H, Hong H, Zhang L, Xing L, Jiang H. An Autologous Macrophage-Based Phenotypic Transformation-Collagen Degradation System Treating Advanced Liver Fibrosis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306899. [PMID: 38064164 PMCID: PMC10870050 DOI: 10.1002/advs.202306899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/24/2023] [Indexed: 02/17/2024]
Abstract
In advanced liver fibrosis (LF), macrophages maintain the inflammatory environment in the liver and accelerate LF deterioration by secreting proinflammatory cytokines. However, there is still no effective strategy to regulate macrophages because of the difficulty and complexity of macrophage inflammatory phenotypic modulation and the insufficient therapeutic efficacy caused by the extracellular matrix (ECM) barrier. Here, AC73 and siUSP1 dual drug-loaded lipid nanoparticle is designed to carry milk fat globule epidermal growth factor 8 (MFG-E8) (named MUA/Y) to effectively inhibit macrophage proinflammatory signals and degrade the ECM barrier. MFG-E8 is released in response to the high reactive oxygen species (ROS) environment in LF, transforming macrophages from a proinflammatory (M1) to an anti-inflammatory (M2) phenotype and inducing macrophages to phagocytose collagen. Collagen ablation increases AC73 and siUSP1 accumulation in hepatic stellate cells (HSCs) and inhibits HSCs overactivation. Interestingly, complete resolution of liver inflammation, significant collagen degradation, and HSCs deactivation are observed in methionine choline deficiency (MCD) and CCl4 models after tail vein injection of MUA/Y. Overall, this work reveals a macrophage-focused regulatory treatment strategy to eliminate LF progression at the source, providing a new perspective for the clinical treatment of advanced LF.
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Affiliation(s)
- Bo‐Wen Duan
- State Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing210009China
| | - Yan‐Jun Liu
- State Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing210009China
| | - Xue‐Na Li
- State Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing210009China
| | - Meng‐Meng Han
- State Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing210009China
| | - Hao‐Yuan Yu
- State Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing210009China
| | - He‐Yuan Hong
- State Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing210009China
| | - Ling‐Feng Zhang
- State Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing210009China
| | - Lei Xing
- State Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing210009China
| | - Hu‐Lin Jiang
- State Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing210009China
- Jiangsu Key Laboratory of Druggability of BiopharmaceuticalsChina Pharmaceutical UniversityNanjing210009China
- Jiangsu Key Laboratory of Drug Discovery for Metabolic DiseasesChina Pharmaceutical UniversityNanjing210009China
- NMPA Key Laboratory for Research and Evaluation of Pharmaceutical Preparations and ExcipientsChina Pharmaceutical UniversityNanjing210009China
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Zheng J, Wang X, Wang Z, Huang L, Xie Y, Jiang S, Feng B. eALT-F: A New Non-Invasive Staging Method to Identify Medium to High-Risk Patients with HCC from Ultra-High HBV Viral Load Population - China, 2010-2023. China CDC Wkly 2023; 5:1107-1114. [PMID: 38125914 PMCID: PMC10728553 DOI: 10.46234/ccdcw2023.207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/03/2023] [Indexed: 12/23/2023] Open
Abstract
Background The objective of this study was to examine the clinical characteristics of individuals with ultra-high hepatitis B virus (HBV) viral load and develop a novel staging method for chronic hepatitis B (CHB) that can more effectively identify patients with medium to high hepatocellular carcinoma (HCC) risk. Methods A total of 2,118 patients with HBV DNA >1×107 IU/mL who visited Peking University People's Hospital between January 2010 and March 2023 were enrolled retrospectively. Clinical data from the first visit were obtained and analyzed. The traditional phases and new 'eALT-F' stages were compared to evaluate the risk of HCC. Results In the overall patients, more than one-third of the patients were under 30 years old. Additionally, a small proportion of older people (>60 years) also had ultra-high HBV viral load (4.3%). 9.1% and 6.7% of individuals with ultra-high HBV viral load showed FIB-4>3.25 and aMAP≥50, respectively. In the traditional stages of CHB, which are based on HBeAg and alanine aminotransferase (ALT) [the upper limit of normal (ULN) ALT level at 40 IU/L for both men and women], regardless of phase, a certain proportion of patients were at risk of developing HCC (4.1%, 6.4%, 25.0%, and 20.3%). However, in the new 'eALT-F' stages, which are based on HBeAg, ALT (the ULN of ALT level at 30 IU/L for men and 19 IU/L for women), and/or FIB-4 levels (>1.45), aMAP≥50 was only observed in chronic hepatitis patients with positive or negative HBeAg (6.4% and 22.1%, respectively). Conclusions The 'eALT-F' staging method, based on HBeAg, ALT (males: the ULN of ALT was 30 IU/L, females: 19 IU/L) and/or FIB-4 levels, was more effective in identifying medium to high-risk patients with HCC from patients with ultra-high HBV viral load than the traditional staging methods.
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Affiliation(s)
- Jiarui Zheng
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing, China
| | - Xiaoxiao Wang
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, China
| | - Zilong Wang
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing, China
| | - Linxiang Huang
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing, China
| | - Yandi Xie
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing, China
| | - Suzhen Jiang
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing, China
| | - Bo Feng
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing, China
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