Evidence map›Paper›PMID 39877725›Full record

ArticleFrontiers in radiology2024

Comparison of dark-field chest radiography and CT for the assessment of COVID-19 pneumonia.

Florian T Gassert, Henriette Bast, Theresa Urban, Manuela Frank, Felix G Gassert, Konstantin Willer, Rafael C Schick, Bernhard Renger, Thomas Koehler, Alexandra Karrer and 5 more

Abstract read
In one paragraph

Article in Frontiers in radiology, 2024. 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

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

4 citing papers in PubMed.

  1. Article
  2. Article
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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

15 authors.

Florian T Gassert *Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Henriette Bast *Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Theresa UrbanDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Manuela FrankChair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, Germany.
Felix G GassertDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Konstantin WillerChair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, Germany.
Rafael C SchickChair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, Germany.
Bernhard RengerDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Thomas KoehlerMunich Institute for Advanced Study, Technical University of Munich, Garching, Germany.
Alexandra KarrerDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Andreas P SauterDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Alexander A FingerleDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Marcus R MakowskiDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Franz PfeifferDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Daniela PfeifferDepartment of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dark-field chest radiography allows the assessment of the structural integrity of the alveoli by exploiting the wave properties of x-rays. Purpose: To compare the qualitative and quantitative features of dark-field chest radiography in patients with COVID-19 pneumonia with conventional CT imaging. Materials and methods: In this prospective study conducted from May 2020 to December 2020, patients aged at least 18 years who underwent chest CT for clinically suspected COVID-19 infection were screened for participation. Inclusion criteria were a CO-RADS score ≥4, the ability to consent to the procedure and to stand upright without help. Participants were examined with a clinical dark-field chest radiography prototype. For comparison, a healthy control cohort of 40 subjects was evaluated. Using Spearman's correlation coefficient, correlation was tested between dark-field coefficient and CT-based COVID-19 index and visual total CT score as well as between the visual total dark-field score and the visual total CT score. Results: A total of 98 participants [mean age 58 ± 14 (standard deviation) years; 59 men] were studied. The areas of signal intensity reduction observed in dark-field images showed a strong correlation with infiltrates identified on CT scans. The dark-field coefficient had a negative correlation with both the quantitative CT-based COVID-19 index ( Conclusion: COVID-19 pneumonia-induced signal intensity losses in dark-field chest radiographs are consistent with CT-based findings, showing the technique's potential for COVID-19 assessment.

Indexed as

COVID-19dark-fieldlung imagingpneumoniaradiography

Identifiers

PMID39877725
PMCPMC11772474

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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.