ReviewAbdominal radiology (New York)2024
Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers.
Review in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 17 citations in OpenAlex.
- Inflammation burden index as a complementary marker for the assessment of hepatic steatosis and fibrosis: evidence from NHANES 2017-2018.Abdominal radiology (New York) · 2026Article
- Extracellular volume fraction derived from spectral CT for liver function reserve evaluation and complication prediction in clinically stable liver cirrhosis.BMC medical imaging · 2026Article
- A Machine Learning-Based Model for Cirrhosis Risk Stratification Incorporating Noninvasive Markers and Clinical Variables.International journal of general medicine · 2026Article
- Construction and validation of a novel model for guiding targeted combined immunotherapy in advanced hepatocellular carcinoma.Scientific reports · 2025Article
- Review
- MRI-derived extracellular volume fraction as a prognostic biomarker for early recurrence after R0 resection of pancreatic ductal adenocarcinoma.European journal of radiology open · 2025Article
- Molecular Imaging Probes for Early Detection and Staging of Liver Fibrosis.Chemical & biomedical imaging · 2025Review
- Dual-layer spectral detector computed tomography in distinguishing between bland and neoplastic portal vein thrombosis.Quantitative imaging in medicine and surgery · 2025Article
- Review
- Application of artificial intelligence in portal hypertension and esophagogastric varices.World journal of gastroenterology · 2025Review
- Predictive value of dual-energy CT assessment of extracellular volume fraction on efficacy of immune checkpoint inhibitors in patients with liver cancer.Frontiers in oncology · 2025Article
- Predicting Chronic Liver Disease Severity by Liver and Splenic Extracellular Volume Fraction Derived from spectral-CT.Current medical imaging · 2025Article
- Fully automated MRI-based convolutional neural network for noninvasive diagnosis of cirrhosis.Insights into imaging · 2024Article
- Development of fully automated models for staging liver fibrosis using non-contrast MRI and artificial intelligence: a retrospective multicenter study.EClinicalMedicine · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
A diagnosis of cirrhosis initiates a shift in the management of chronic liver disease and affects the diagnostic workflow and treatment decision of primary liver cancer. Liver biopsy remains the gold standard for cirrhosis diagnosis, but it is invasive and susceptible to sampling bias and observer variability. Various qualitative and quantitative imaging biomarkers based on ultrasound, CT and MRI have been proposed for noninvasive diagnosis of cirrhosis. Qualitative imaging features are easy to apply but have moderate diagnostic sensitivity. Elastography techniques allow quantitative assessment of liver stiffness and are highly accurate for cirrhosis diagnosis. Ultrasound elastography are widely used in clinical practice, while MR elastography has narrower availability. Although not applicable in clinical practice yet, other quantitative imaging features, including liver surface nodularity, linear and volumetric measurement, extracellular volume fraction, liver enhancement on hepatobiliary phase, and parameters derived from diffusion-weighted imaging, can provide additional information of liver morphology, perfusion, and function, thus may increase diagnosis performance. The introduction of radiomics and deep learning has further improved diagnostic accuracy while reducing subjectivity. Several imaging features may also help to assess liver function and outcomes in patients with cirrhosis. In this review, we summarize the qualitative and quantitative imaging biomarkers for noninvasive cirrhosis diagnosis, and the assessment of liver function and outcomes, and discuss the challenges and future directions in this field.
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