Evidence map›Paper›PMID 29436842›Full record

ArticleThe British journal of radiology2018

Histogram analysis derived from apparent diffusion coefficient (ADC) is more sensitive to reflect serological parameters in myositis than conventional ADC analysis.

Hans Jonas Meyer, Alexander Emmer, Malte Kornhuber, Alexey Surov

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Article in The British journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

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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

3 citing papers in PubMed, 8 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

4 authors at 2 institutions in 1 country.

Hans Jonas Meyer1 Department of Diagnostic and Interventional Radiology, University of Leipzig , Leipzig , Germany.
Alexander Emmer2 Department of Neurology, Martin-Luther University Halle-Wittenberg , Halle (Saale) , Germany.
Malte Kornhuber2 Department of Neurology, Martin-Luther University Halle-Wittenberg , Halle (Saale) , Germany.
Alexey Surov1 Department of Diagnostic and Interventional Radiology, University of Leipzig , Leipzig , Germany.
Leipzig University · DEMartin Luther University Halle-Wittenberg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveDiffusion-weighted imaging (DWI) has the potential of being able to reflect histopathology architecture. A novel imaging approach, namely histogram analysis, is used to further characterize tissues on MRI. The aim of this study was to correlate histogram parameters derived from apparent diffusion coefficient (ADC) maps with serological parameters in myositis.

methods16 patients with autoimmune myositis were included in this retrospective study. DWI was obtained on a 1.5 T scanner by using the b-values of 0 and 1000 s mm

resultsKurtosis correlated inversely with CRP (p = -0.55 and 0.03). Furthermore, ADCp10 and ADCp90 values tended to correlate with creatine kinase (p = -0.43, 0.11, and p = -0.42, = 0.12 respectively). In addition, ADCmean, p10, p25, median, mode, and entropy were different between Jo1-positive and Jo1-negative patients.

conclusionADC histogram parameters are sensitive for detection of muscle alterations in myositis patients. Advances in knowledge: This study identified that kurtosis derived from ADC maps is associated with CRP in myositis patients. Furthermore, several ADC histogram parameters are statistically different between Jo1-positive and Jo1-negative patients.

Indexed as

AdultAgedAlanine TransaminaseC-Reactive ProteinCreatine KinaseDiffusion Magnetic Resonance ImagingFemaleHumansImage Interpretation, Computer-AssistedL-Lactate DehydrogenaseMaleMiddle AgedMuscle, SkeletalMyoglobinMyositisReproducibility of ResultsAlanine TransaminaseC-Reactive ProteinCreatine KinaseL-Lactate DehydrogenaseMyoglobin

Identifiers

PMID29436842
PMCPMC6190783
OpenAlexW2787066615

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.