Evidence map›Paper›PMID 36819239›Full record

ArticleQuantitative imaging in medicine and surgery2023

Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT.

Jonathan Kottlors, Philipp Fervers, Simon Geißen, Roman Johannes Gertz, Johannes Bremm, Miriam Rinneburger, Mathilda Weisthoff, Rahil Shahzad, David Maintz, Thorsten Persigehl

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Article in Quantitative imaging in medicine and surgery, 2023. 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
0.7field-weighted citation impact, top 31% 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

4 citing papers in PubMed, 3 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

10 authors at 3 institutions in 1 country.

Jonathan Kottlors *Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
Philipp Fervers *Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
Simon GeißenDivision of Cardiology, Pneumology, Angiology and Intensive Care, University of Cologne (UOC), Cologne, Germany.
Roman Johannes GertzInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
Johannes BremmInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
Miriam RinneburgerInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
Mathilda WeisthoffInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
Rahil ShahzadInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
David MaintzInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
Thorsten PersigehlInstitute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
University of Cologne · DEUniversity Hospital Cologne · DEPhilips (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diagnosing a coronavirus disease 2019 (COVID-19) infection with high specificity in chest computed tomography (CT) imaging is considered possible due to distinctive imaging features of COVID-19 pneumonia. Since other viral non-COVID pneumonia show mostly a different distribution pattern, it is reasonable to assume that the patterns observed caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a consequence of its genetically encoded molecular properties when interacting with the respiratory tissue. As more mutations of the initial SARS-CoV-2 wild-type with varying aggressiveness have been detected in the course of 2021, it became obvious that its genome is in a state of transformation and therefore a potential modification of the specific morphological appearance in CT may occur. The aim of this study was to quantitatively analyze the morphological differences of the SARS-CoV-2-B.1.1.7 mutation and wildtype variant in CT scans of the thorax. Methods: We analyzed a dataset of 140 patients, which was divided into pneumonias caused by n=40 wildtype variants, n=40 B.1.1.7 variants, n=20 bacterial pneumonias, n=20 viral (non-COVID) pneumonias, and a test group of n=20 unremarkable CT examinations of the thorax. Semiautomated 3D segmentation of the lung tissue was performed for quantification of lung pathologies. The extent, ratio, and specific distribution of inflammatory affected lung tissue in each group were compared in a multivariate group analysis. Results: Lung segmentation revealed significant difference between the extent of ground glass opacities (GGO) or consolidation comparing SARS-CoV-2 wild-type and B.1.1.7 variant. Wildtype and B.1.1.7 variant showed both a symmetric distribution pattern of stage-dependent GGO and consolidation within matched COVID-19 stages. Viral non-COVID pneumonias had significantly fewer consolidations than the bacterial, but also than the COVID-19 B.1.1.7 variant groups. Conclusions: CT based segmentation showed no significant difference between the morphological appearance of the COVID-19 wild-type variant and the SARS-CoV-2 B.1.1.7 mutation. However, our approach allowed a semiautomatic quantification of bacterial and viral lung pathologies. Quantitative CT image analyses, such as the one presented, appear to be an important component of pandemic preparedness considering an organism with ongoing genetic change, to describe a potential arising change in CT morphological appearance of possible new upcoming COVID-19 variants of concern.

Indexed as

chest imagingCoronavirus disease 2019 (COVID-19)pandemic preparednessquantitative computed tomography (CT)

Identifiers

PMID36819239
PMCPMC9929392
OpenAlexW4316042064

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.