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

Tracheales Computertomographie-Radiomics-Modell zur Vorhersage der Omikronvariante des Schweres-akutes-Atemwegssyndrom-Coronavirus 2

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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.

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.

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.

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.

Zusammenfassung

Ziel

Die Omikronvariante von SARS-CoV‑2 („severe acute respiratory syndrome coronavirus 2“) ist hoch ansteckend, verbreitet sich schnell und ist tückisch. Die meisten Patienten weisen einen Normalbefund in der Computertomographie (CT) der Lunge auf. Ziel der vorliegenden Studie war es, ein CT-Radiomics-Modell der Trachea zu entwickeln und zu validieren, anhand dessen sich eine Infektion mit der Omikronvariante vorhersagen lässt.

Material und Methoden

In dieser retrospektiven Studie wurde zwischen 1. Januar und 30. April 2022 auf der Grundlage eines Trainingsdatensatzes von 157 Patienten mit einer Infektion durch die Omikronvariante und 239 gesunden Kontrollpersonen ein Radiomics-Modell erzeugt. Ein Satz morphologischer Expansionen mit Dilatationen von 1, 3, 5, 7 und 9 Voxeln wurde auf die Trachea angewandt, und radiomische Charakteristika wurden aus verschiedenen Dilatationsvoxeln der Trachea extrahiert. Die logistische Regression (LR), die Support-Vector-Machines(SVM)- und die Random-Forests(RF)-Methode wurden angewandt und bewertet; die Modelle wurden an 67 Patienten mit der Omikronvariante und 103 gesunden Kontrollen zwischen 1. Mai und 30. Juli 2022 validiert.

Ergebnisse

Die LR mit 12 radiomischen, aus der Trachealwand mit Dilatation von 5 Voxeln extrahierten Merkmalen erzielte die höchste Klassifikationsleistung im Vergleich zu anderen Modellen. Mit dem LR-Modell wurde eine Fläche unter der Kurve („area under the curve“ [AUC]) von 0,993 (95%-Konfidenzintervall [95%-KI]: 0,987–0,998) im Trainingsdatensatz und 0,989 (95%-KI: 0,979–0,999) im Validierungsdatensatz erzielt. Sensitivität, Spezifität und Genauigkeit des Modells für den Trainingsdatensatz betrugen 0,994; 0,946 bzw. 0,965, während die Werte für den Validierungsdatensatz bei 0,970; 0,952 bzw. 0,959 lagen.

Schlussfolgerung

Mit dem CT-Radiomics-Modell wurde die Omikronvariante von SARS-CoV‑2 zuverlässig erkannt, es kann möglicherweise in Zukunft zur klinischen Entscheidungsfindung beitragen, insbesondere in Fällen mit einem normalen Befund in der Lungen-CT.

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Data availability statement

The data are not publicly available to protect the individuals’ privacy. The data that support the findings of this study are available from the corresponding author, Yun Bian, upon reasonable request.

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Funding

This work was supported in part by the National Science Foundation for Scientists of China (81871352, 82171915, 82171930, and 82271972), The Natural Science Foundation of Shanghai Science and Technology Innovation Action Plan (21ZR1478500, 21Y11910300), Clinical Research Plan of SHDC (SHDC2020CR4073, SHDC2022CRD028), and 234 Platform Discipline Consolidation Foundation Project (2020YPT001).

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Authors and Affiliations

  1. Department of Radiology, Changhai Hospital, Naval Medical University, 168 Changhai Road, 200433, Shanghai, China

    Xu Fang, Fang Liu, Jing Li, Tiegong Wang, Jianping Lu, Chengwei Sha & Yun Bian

  2. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China

    Feng Shi, Ying Wei & Jiaojiao Wu

Authors
  1. Xu Fang
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  2. Feng Shi
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  3. Fang Liu
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  10. Yun Bian
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Contributions

Xu Fang, Fang Liu, and Jing Li collected and analyzed the clinical data. Feng Shi, Ying Wei, and Jiaojiao Wu analyzed the radiomics data. Xu Fang wrote the paper. Feng Shi and Ying Wei reviewed and edited paper. Jianping Lu, Chengwei Shao, and Yun Bian were involved in investigation and funding acquisition and supervision. Chengwei Shao and Yun Bian revised this paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Chengwei Sha or Yun Bian.

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Conflict of interest

X. Fang, F. Shi, F. Liu, Y. Wei, J. Li, J. Wu, T. Wang, J. Lu, C. Sha 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.

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The authors Xu Fang and Feng Shi contributed equally to the manuscript.

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Fang, X., Shi, F., Liu, F. et al. Tracheal computed tomography radiomics model for prediction of the Omicron variant of severe acute respiratory syndrome coronavirus 2. Radiologie (2024). https://doi.org/10.1007/s00117-024-01275-3

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