Evidence map›Paper›PMID 35689340›Full record

ArticleACR open rheumatology2022

Computer-Assisted Image Analysis in Assessment of Peripheral Joint MRI in Inflammatory Arthritis: A Systematic Review and Meta-analysis.

Arya Haj-Mirzaian, Olga Kubassova, Mikael Boesen, John Carrino, Paul Bird

Abstract read
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Article in ACR open rheumatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

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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

4 citing papers in PubMed.

  1. Article
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Arya Haj-MirzaianJohns Hopkins Hospital, Baltimore, Maryland.
Olga KubassovaImage Analysis Group, London, United Kingdom.
Mikael BoesenUniversity Hospital Bispebjerg and Frederiksberg; The Parker Institute, Copenhagen, Denmark.
John CarrinoHospital for Special Surgery, Hackensack, New Jersey.
Paul BirdUniversity of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-2277-8337

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo summarize the feasibility of computer-assisted quantification of joint pathologies on magnetic resonance imaging (MRI) in patients with inflammatory arthritis by evaluating the published data on reliability, validity, and feasibility.

methodsA systematic literature search was performed for original articles published from January 1, 1985, to January 1, 2021. We selected studies in which patients with inflammatory arthritis were enrolled, and arthritis-related structural damage/synovitis in peripheral joints was assessed on non-contrast-enhanced, contrast-enhanced (CE), or dynamic CE (DCE)-MRI using (semi)automated methods. Data were pooled using random-effects model.

resultsTwenty-eight studies consisting of 1342 MRIs were included (mean age, 54.8 years; 66.7% female; duration of arthritis, 3.6 years). Among clinical/laboratory factors, synovial membrane volume (SV) was moderately correlated with erthrocyte sedimentation rate (ESR) level (P < 0.01). Pooled analysis showed an overall excellent intra- and inter-reader reliability for computer-aided quantification of bone erosion volume (BEV; r = 0.97 [95% CI: 0.92-0.99], 0.93 [0.87-0.97]), SV (r = 0.98 [95% CI: 0.90-0.99], 0.86 [0.78-0.91]), and DCE-MRI perfusion parameters (r = 0.96-0.99). Meta-regression showed that computer-aided and manual methods provide comparable reliability (P > 0.05). Computer-aided measurement of BEV (r = 0.92), SV (r = 0.82), and DCE-MRI biomarkers (r = 0.72 N-total; r = 0.74 N-plateau; r = 0.64 N-washout) were significantly correlated with the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS; P < 0.01), allowing for earlier assessment of drug efficacy. On average, (semi)automated analysis of BEV/SV took 17 minutes (vs. 9 minutes for the RAMRIS) and DCE-MRI took 4 minutes (vs. 33 minutes for manual assessment).

conclusionComputer-aided image quantification technologies demonstrate excellent reliability and validity when used to quantify MRI pathologies of peripheral joints in patients with inflammatory arthritis. Computer-aided evaluation of inflammatory arthritis is an emerging field and should be considered as a viable complement to conventional observer-based scoring methods for clinical trials application.

Identifiers

PMID35689340
PMCPMC9374055

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