ReviewBioMed research international2022
Updates on Quantitative MRI of Diffuse Liver Disease: A Narrative Review.
Review in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed, 8 citations in OpenAlex.
- Comparison of hepatic PDFF, T2*, and T2 estimates from breath-hold and free-breathing respiratory-gated MRI acquisitions in children and young adults.Abdominal radiology (New York) · 2026Article
- Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging can Differentiate between Residual or Recurrent Hepatocellular Carcinoma and Inflammatory Reaction Zone: A Single-Center Study.Current medical imaging · 2026Article
- Liver Tissue Mapping in Transfusion-Dependent β-Thalassemia: Reproducibility and Clinical Insights from Multiparametric MRI.Diagnostics (Basel, Switzerland) · 2025Article
- Multimodal Diagnostic Imaging of Metabolic Dysfunction-Associated Steatotic Liver Disease: Noninvasive Analyses by Photoacoustic Ultrasound and Magnetic Resonance Imaging.The American journal of pathology · 2025Article
- Influence of fat on hepatic T2 relaxation time estimation: a preliminary investigation.Abdominal radiology (New York) · 2025Article
- Liver T1 and T2 mapping in a large cohort of healthy subjects: normal ranges and correlation with age and sex.Magma (New York, N.Y.) · 2024Article
- Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma.World journal of gastrointestinal oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diffuse liver diseases are highly prevalent conditions around the world, including pathological liver changes that occur when hepatocytes are damaged and liver function declines, often leading to a chronic condition. In the last years, Magnetic Resonance Imaging (MRI) is reaching an important role in the study of diffuse liver diseases moving from qualitative to quantitative assessment of liver parenchyma. In fact, this can allow noninvasive accurate and standardized assessment of diffuse liver diseases and can represent a concrete alternative to biopsy which represents the current reference standard. MRI approach already tested for other pathologies include diffusion-weighted imaging (DWI) and radiomics, able to quantify different aspects of diffuse liver disease. New emerging MRI quantitative methods include MR elastography (MRE) for the quantification of the hepatic stiffness in cirrhotic patients, dedicated gradient multiecho sequences for the assessment of hepatic fat storage, and iron overload. Thus, the aim of this review is to give an overview of the technical principles and clinical application of new quantitative MRI techniques for the evaluation of diffuse liver disease.
Indexed as
Identifiers
What OpenQuestion holds
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.