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Tracheal computed tomography radiomics model for prediction of the Omicron variant of severe acute respiratory syndrome coronavirus 2.
- Source :
-
Radiologie (Heidelberg, Germany) [Radiologie (Heidelb)] 2024 Nov; Vol. 64 (Suppl 1), pp. 66-75. Date of Electronic Publication: 2024 Mar 06. - Publication Year :
- 2024
-
Abstract
- Objectives: The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious, fast-spreading, and insidious. Most patients present with normal findings on lung computed tomography (CT). The current study aimed to develop and validate a tracheal CT radiomics model to predict Omicron variant infection.<br />Materials and Methods: In this retrospective study, a radiomics model was developed based on a training set consisting of 157 patients with an Omicron variant infection and 239 healthy controls between 1 January and 30 April 2022. A set of morphological expansions, with dilations of 1, 3, 5, 7, and 9 voxels, was applied to the trachea, and radiomic features were extracted from different dilation voxels of the trachea. Logistic regression (LR), support vector machines (SVM), and random forests (RF) were developed and evaluated; the models were validated on 67 patients with the Omicron variant and on 103 healthy controls between 1 May and 30 July 2022.<br />Results: Logistic regression with 12 radiomic features extracted from the tracheal wall with dilation of 5 voxels achieved the highest classification performance compared with the other models. The LR model achieved an area under the curve of 0.993 (95% confidence interval [CI]: 0.987-0.998) in the training set and 0.989 (95% CI: 0.979-0.999) in the validation set. Sensitivity, specificity, and accuracy of the model for the training set were 0.994, 0.946, and 0.965, respectively, whereas those for the validation set were 0.970, 0.952, and 0.959, respectively.<br />Conclusion: The tracheal CT radiomics model reliably identified the Omicron variant of SARS-CoV‑2, and may help in clinical decision-making in future, especially in cases of normal lung CT findings.<br />Competing Interests: Declarations. Conflict of interest: X. Fang, F. Shi, F. Liu, Y. Wei, J. Li, J. Wu, T. Wang, J. Lu, C. Shao and Y. Bian declare that they have no competing interests. For this article no studies with human participants or animals were performed by any of the authors. All studies mentioned were in accordance with the ethical standards indicated in each case. The retrospective cross-sectional study was reviewed and approved by the Biomedical Research Ethics Committee of Changhai Hospital. The requirement for patient consent was waived. The supplement containing this article is not sponsored by industry.<br /> (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)
- Subjects :
- Humans
Male
Female
Middle Aged
Retrospective Studies
Adult
Aged
Betacoronavirus
Pneumonia, Viral diagnostic imaging
Pneumonia, Viral virology
Coronavirus Infections diagnostic imaging
Coronavirus Infections virology
Pandemics
Radiomics
COVID-19 diagnostic imaging
COVID-19 virology
Tomography, X-Ray Computed methods
SARS-CoV-2
Trachea diagnostic imaging
Trachea virology
Subjects
Details
- Language :
- English
- ISSN :
- 2731-7056
- Volume :
- 64
- Issue :
- Suppl 1
- Database :
- MEDLINE
- Journal :
- Radiologie (Heidelberg, Germany)
- Publication Type :
- Academic Journal
- Accession number :
- 38446170
- Full Text :
- https://doi.org/10.1007/s00117-024-01275-3