Tracheal computed tomography radiomics model for prediction of the Omicron variant of severe acute respiratory syndrome coronavirus 2.

Publication date: Mar 06, 2024

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. 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. 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. 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.

Concepts Keywords
67patients Computed tomography
Coronavirus2 COVID-19
Fast Omicron
Forests Radiomics
Tomography Trachea

Semantics

Type Source Name
disease MESH severe acute respiratory syndrome
disease MESH infection
disease MESH COVID-19

Original Article

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