Your browser doesn't support javascript.
Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT.
Kottlors, Jonathan; Fervers, Philipp; Geißen, Simon; Gertz, Roman Johannes; Bremm, Johannes; Rinneburger, Miriam; Weisthoff, Mathilda; Shahzad, Rahil; Maintz, David; Persigehl, Thorsten.
  • Kottlors J; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
  • Fervers P; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
  • Geißen S; Division of Cardiology, Pneumology, Angiology and Intensive Care, University of Cologne (UOC), Cologne, Germany.
  • Gertz RJ; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
  • Bremm J; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
  • Rinneburger M; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
  • Weisthoff M; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
  • Shahzad R; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
  • Maintz D; Innovative Technology, Philips Healthcare, Aachen, Germany.
  • Persigehl T; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany.
Quant Imaging Med Surg ; 13(2): 1058-1070, 2023 Feb 01.
Article in English | MEDLINE | ID: covidwho-2232072
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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Prognostic study / Randomized controlled trials Topics: Variants Language: English Journal: Quant Imaging Med Surg Year: 2023 Document Type: Article Affiliation country: Qims-22-718

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Experimental Studies / Prognostic study / Randomized controlled trials Topics: Variants Language: English Journal: Quant Imaging Med Surg Year: 2023 Document Type: Article Affiliation country: Qims-22-718