Evidence map›Paper›PMID 30770575›Full record

ArticleMagnetic resonance in medicine2019

Measurement of synovial tissue volume in knee osteoarthritis using a semiautomated MRI-based quantitative approach.

Thomas A Perry, Andrew Gait, Terence W O'Neill, Matthew J Parkes, Richard Hodgson, Michael J Callaghan, Nigel K Arden, David T Felson, Timothy F Cootes

Open access · hybridAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.8field-weighted citation impact, top 16% 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

11 citing papers in PubMed, 17 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

9 authors at 6 institutions in 3 countries.

Thomas A PerryArthritis Research UK Centre for Epidemiology, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Andrew GaitCentre for Imaging Sciences, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, United Kingdom.
Terence W O'NeillArthritis Research UK Centre for Epidemiology, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Matthew J ParkesArthritis Research UK Centre for Epidemiology, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Richard HodgsonCentre for Imaging Sciences, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, United Kingdom.
Michael J CallaghanFaculty of Health, Psychology, and Social Care, Department of Health Professions, Manchester Metropolitan University, Manchester, United Kingdom.
Nigel K ArdenNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom.
David T FelsonNIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom.
Timothy F CootesCentre for Imaging Sciences, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, United Kingdom.
University of Manchester · GBBoston University · USManchester Metropolitan University · GBManchester University NHS Foundation Trust · GBNIHR Manchester Biomedical Research Centre · GBNuffield Orthopaedic Centre · GB

Funding

Research and Evaluation Support Core Unit (RESCU Core)P30AR072571 · NIAMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Michael Paul Lavalley · 2019 to 2026
$6.9M
Arthritis Research UK 20380NIAMS NIH HHS P30 AR072571
6 · The paper itself

Abstract

purposeSynovitis is common in knee osteoarthritis and is associated with both knee pain and progression of disease. Semiautomated methods have been developed for quantitative assessment of structure in knee osteoarthritis. Our aims were to apply a novel semiautomated assessment method using 3D active appearance modeling for the quantification of synovial tissue volume (STV) and to compare its performance with conventional manual segmentation.

methodsThirty-two sagittal T

resultsThere was no significant difference in STV using the semiautomated quantitative method compared to manual segmentation, mean difference = 207.2 mm

conclusionWe describe the application of a semiautomated method that is accurate, reliable, and quicker than manual segmentation for assessment of STV. The method may help increase efficiency of image assessment in large imaging studies and may also assist investigation of treatment efficacy in knee osteoarthritis.

Indexed as

AgedAutomationContrast MediaCross-Over StudiesDiagnosis, Computer-AssistedFemaleHumansImage Processing, Computer-AssistedImaging, Three-DimensionalKnee JointMagnetic Resonance ImagingMaleMiddle AgedObserver VariationOsteoarthritisOsteoarthritis, KneeContrast MediaVitamin Dosteoarthritissegmentationsemiautomatedsynovial tissue volume

Identifiers

PMID30770575
PMCPMC6686166
OpenAlexW2902845550

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.