ReviewHepatology international2022
Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease.
Review in Hepatology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review.Medicina (Kaunas, Lithuania) · 2023Pooled it
- Early prediction of acute kidney injury after therapeutic paracentesis in decompensated liver cirrhosis: diagnostic value of IL-18, KIM-1, and FGF-23.Renal failure · 2026Article
- Clinically Significant Portal Hypertension in Hepatocellular Carcinoma: Non-Invasive Assessment Across the Continuum.Current oncology reports · 2026Review
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Spatial hepatology: Decoding liver zonation for metabolic and regenerative therapeutics (Review).International journal of molecular medicine · 2026Review
- Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease: Current status and future prospects.World journal of gastroenterology · 2025Review
- Sex- and Age-Stratified relative handgrip strength and risk of eight chronic diseases in Middle-Aged and older adults: evidence from a National aging cohort study in China.Aging clinical and experimental research · 2025Article
- Hepatocellular Carcinoma Risk Stratification for Cirrhosis Patients: Integrating Radiomics and Deep Learning Computed Tomography Signatures of the Liver and Spleen into a Clinical Model.Journal of clinical and translational hepatology · 2025Article
- Deep learning radiomics of elastography for diagnosing compensated advanced chronic liver disease: an international multicenter study.Visual computing for industry, biomedicine, and art · 2025Article
- 7-T MRI-based surrogate for histopathology examination of liver fibrosis.European radiology experimental · 2025Article
- Innovative Strategies in the Diagnosis and Treatment of Liver Cirrhosis and Associated Syndromes.Life (Basel, Switzerland) · 2025Review
- Predictive Radiomics-Based Model for Recurrence-Free Survival After Curative Resection in Patients with Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.Frontiers in radiology · 2025Article
- Artificial intelligence in imaging for liver disease diagnosis.Frontiers in medicine · 2025Review
- Revolutionising portal hypertension diagnosis: the rise of non-invasive techniques in liver cirrhosis.Frontiers in medicine · 2025Review
- Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease.Scientific reports · 2024Article
- Radiology of fibrosis part II: abdominal organs.Journal of translational medicine · 2024Review
- Extracellular Vesicles as Delivery Vehicles for Non-Coding RNAs: Potential Biomarkers for Chronic Liver Diseases.Biomolecules · 2024Review
- Noninvasive Methods for Assessing Liver Fibrosis and Steatosis.Gastroenterology & hepatology · 2024Article
- Advances in Noninvasive Molecular Imaging Probes for Liver Fibrosis Diagnosis.Biomaterials research · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 4 countries.
Funding
Abstract
Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.