Evidence map›Paper›PMID 32876833›Full record

ArticleEuropean radiology2021

Intravoxel incoherent motion diffusion-weighted MRI for the characterization of inflammation in chronic liver disease.

Thierry Lefebvre, Mélanie Hébert, Laurent Bilodeau, Giada Sebastiani, Milena Cerny, Damien Olivié, Zu-Hua Gao, Marie-Pierre Sylvestre, Guy Cloutier, Bich N Nguyen and 2 more

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In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 1 country.

Thierry LefebvreDepartment of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Mélanie HébertDepartment of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Laurent BilodeauDepartment of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Giada SebastianiDivision of Gastroenterology and Hepatology, Department of Medicine, McGill University Health Centre (MUHC), Montréal, Canada.
Milena CernyDepartment of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Damien OliviéDepartment of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Zu-Hua GaoDepartment of Pathology, McGill University, Montréal, Canada.
Marie-Pierre SylvestreCentre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.
Guy CloutierDepartment of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Bich N NguyenService of Pathology, Centre hospitalier de l'Université de Montréal (CHUM), Montréal, Canada.
Guillaume GilbertDepartment of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
An TangDepartment of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada. an.tang@umontreal.ca.ORCID http://orcid.org/0000-0001-8967-5503
Centre Hospitalier de l’Université de Montréal · CACARE Canada · CAMcGill University · CAMcGill University Health Centre · CAUniversité de Montréal · CA

Funding

CIHR 273738CIHR 301520Fonds de Recherche du Québec - Santé 267806Fonds de Recherche du Québec - Santé 26993Fonds de Recherche du Québec - Santé 27127Fonds de Recherche du Québec - Santé 34875Fonds de Recherche du Québec - Santé 34939
6 · The paper itself

Abstract

objectiveTo evaluate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for grading hepatic inflammation.

methodsIn this retrospective cross-sectional dual-center study, 91 patients with chronic liver disease were recruited between September 2014 and September 2018. Patients underwent 3.0-T MRI examinations within 6 weeks from a liver biopsy. IVIM parameters, perfusion fraction (f), diffusion coefficient (D), and pseudo-diffusion coefficient (D*), were estimated using a voxel-wise nonlinear regression on DWI series (10 b-values from 0 to 800 s/mm

resultsParameters f, D, and D* had ICCs of 0.860, 0.839, and 0.916, respectively. Correlations of f, D, and D* with inflammation grade were ρ = - 0.70, p < 0.0001; ρ = 0.10, p = 0.35; and ρ = - 0.27, p = 0.010, respectively. When adjusting for fibrosis and steatosis, the correlation between f and inflammation (p < 0.0001) remained, and that between f and fibrosis was also significant to a lesser extent (p = 0.002). AUCs of f, D, and D* for distinguishing inflammation grades 0 vs. ≥ 1 were 0.84, 0.53, and 0.70; ≤ 1 vs. ≥ 2 were 0.88, 0.57, and 0.60; and ≤ 2 vs. 3 were 0.86, 0.54, and 0.65, respectively.

conclusionPerfusion fraction f strongly correlated, D very weakly correlated, and D* weakly correlated with inflammation. Among all IVIM parameters, f accurately graded inflammation and showed promise as a biomarker of hepatic inflammation. KEY POINTS: • IVIM parameters derived from DWI series with 10 b-values are reproducible for liver tissue characterization. • This retrospective two-center study showed that perfusion fraction provided good diagnostic performance for distinguishing dichotomized grades of inflammation. • Fibrosis is a significant confounder on the association between inflammation and perfusion fraction.

Indexed as

Diffusion Magnetic Resonance ImagingLiver DiseasesCross-Sectional StudiesHumansInflammationMagnetic Resonance ImagingMotionRetrospective StudiesBiopsyDiffusion-weighted MRIHepatitisInflammationNonalcoholic fatty liver disease

Identifiers

PMID32876833
OpenAlexW3082717001

What OpenQuestion holds

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Registered trials

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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.