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A composite biomarker using multiparametric magnetic resonance imaging and blood analytes accurately identifies patients with non-alcoholic steatohepatitis and significant fibrosis.

ABSTRACT: Non-alcoholic steatohepatitis (NASH) is major health burden lacking effective pharmacological therapies. Clinical trials enrol patients with histologically-defined NAFLD (non-alcoholic fatty liver disease) activity score (NAS)???4 and Kleiner-Brunt fibrosis stage (F)???2; however, screen failure rates are often high following biopsy. This study evaluated a non-invasive MRI biomarker, iron-corrected T1 mapping (cT1), as a diagnostic pre-screening biomarker for NASH. In a retrospective analysis of 86 biopsy confirmed NAFLD patients we explored the potential of blood and imaging biomarkers, both in isolation and in combination, to discriminate those who have NAS???4 and F???2 from those without. Stepwise logistic regression was performed to select the optimal combination of biomarkers, diagnostic accuracy was determined using area under the receiver operator curve and model validated confirmed with and fivefold cross-validation. Results showed that levels of cT1, AST, GGT and fasting glucose were all good predictors of NAS???4 and F???2, and the model identified the combination of cT1-AST-fasting glucose (cTAG) as far superior to any individual biomarker (AUC 0.90 [0.84-0.97]). This highlights the potential utility of the composite cTAG score for screening patients prior to biopsy to identify those suitable for NASH clinical trial enrolment.

SUBMITTER: Dennis A

PROVIDER: S-EPMC7499258 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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A composite biomarker using multiparametric magnetic resonance imaging and blood analytes accurately identifies patients with non-alcoholic steatohepatitis and significant fibrosis.

Dennis Andrea A Mouchti Sofia S Kelly Matt M Fallowfield Jonathan A JA Hirschfield Gideon G Pavlides Michael M Banerjee Rajarshi R

Scientific reports 20200917 1

Non-alcoholic steatohepatitis (NASH) is major health burden lacking effective pharmacological therapies. Clinical trials enrol patients with histologically-defined NAFLD (non-alcoholic fatty liver disease) activity score (NAS) ≥ 4 and Kleiner-Brunt fibrosis stage (F) ≥ 2; however, screen failure rates are often high following biopsy. This study evaluated a non-invasive MRI biomarker, iron-corrected T1 mapping (cT1), as a diagnostic pre-screening biomarker for NASH. In a retrospective analysis of ...[more]

PMID: 32943694

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Project description:ObjectiveTo assess significant liver fibrosis by multiparametric ultrasomics data using machine learning.Materials and methodsThis prospective study consisted of 144 patients with chronic hepatitis B. Ultrasomics-high-throughput quantitative data from ultrasound imaging of liver fibrosis-were generated using conventional radiomics, original radiofrequency (ORF) and contrast-enhanced micro-flow (CEMF) features. Three categories of features were explored using pairwise correlation and hierarchical clustering. Features were selected using diagnostic tests for fibrosis, activity and steatosis stage, with the histopathological results as the reference. The fibrosis staging performance of ultrasomics models with combinations of the selected features was evaluated with machine-learning algorithms by calculating the area under the receiver-operator characteristic curve (AUC).ResultsORF and CEMF features had better predictive power than conventional radiomics for liver fibrosis stage (both p < 0.01). CEMF features exhibited the highest diagnostic value for activity stage (both p < 0.05), and ORF had the best diagnostic value for steatosis stage (both p < 0.01). The machine-learning classifiers of adaptive boosting, random forest and support vector machine were found to be optimal algorithms with better (all mean AUCs = 0.85) and more stable performance (coefficient of variation = 0.01-0.02) for fibrosis staging than decision tree, logistic regression and neural network (mean AUC = 0.61-0.72, CV = 0.07-0.08). The multiparametric ultrasomics model achieved much better performance (mean AUC values of 0.78-0.85) than the features from a single modality in discriminating significant fibrosis (≥ F2).ConclusionMachine-learning-based analysis of multiparametric ultrasomics can help improve the discrimination of significant fibrosis compared with mono or dual modalities.Key points• Multiparametric ultrasomics has achieved much better performance in the discrimination of significant fibrosis (≥ F2) than the single modality of conventional radiomics, original radiofrequency and contrast-enhanced micro-flow. • Adaptive boosting, random forest and support vector machine are the optimal algorithms for machine learning.

Project description:This paper is based upon the "Charles Lieber Satellite Symposia" organized by Manuela G. Neuman at the Research Society on Alcoholism (RSA) Annual Meetings, 2013 and 2014. The present review includes pre-clinical, translational and clinical research that characterize alcoholic liver disease (ALD) and non-alcoholic steatohepatitis (NASH). In addition, a literature search in the discussed area was performed. Strong clinical and experimental evidence lead to recognition of the key toxic role of alcohol in the pathogenesis of ALD. The liver biopsy can confirm the etiology of NASH or alcoholic steatohepatitis (ASH) and assess structural alterations of cells, their organelles, as well as inflammatory activity. Three histological stages of ALD are simple steatosis, ASH, and chronic hepatitis with hepatic fibrosis or cirrhosis. These latter stages may also be associated with a number of cellular and histological changes, including the presence of Mallory's hyaline, megamitochondria, or perivenular and perisinusoidal fibrosis. Genetic polymorphisms of ethanol metabolizing enzymes such as cytochrome p450 (CYP) 2E1 activation may change the severity of ASH and NASH. Alcohol mediated hepatocarcinogenesis, immune response to alcohol in ASH, as well as the role of other risk factors such as its co-morbidities with chronic viral hepatitis in the presence or absence of human immunodeficiency virus are discussed. Dysregulation of hepatic methylation, as result of ethanol exposure, in hepatocytes transfected with hepatitis C virus (HCV), illustrates an impaired interferon signaling. The hepatotoxic effects of ethanol undermine the contribution of malnutrition to the liver injury. Dietary interventions such as micro and macronutrients, as well as changes to the microbiota are suggested. The clinical aspects of NASH, as part of metabolic syndrome in the aging population, are offered. The integrative symposia investigate different aspects of alcohol-induced liver damage and possible repair. We aim to (1) determine the immuno-pathology of alcohol-induced liver damage, (2) examine the role of genetics in the development of ASH, (3) propose diagnostic markers of ASH and NASH, (4) examine age differences, (5) develop common research tools to study alcohol-induced effects in clinical and pre-clinical studies, and (6) focus on factors that aggravate severity of organ-damage. The intention of these symposia is to advance the international profile of the biological research on alcoholism. We also wish to further our mission of leading the forum to progress the science and practice of translational research in alcoholism.

Dataset Information

A composite biomarker using multiparametric magnetic resonance imaging and blood analytes accurately identifies patients with non-alcoholic steatohepatitis and significant fibrosis.

Publications

A composite biomarker using multiparametric magnetic resonance imaging and blood analytes accurately identifies patients with non-alcoholic steatohepatitis and significant fibrosis.

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