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European Radiology
Published in: European Radiology 7/2022

29-01-2022 | Artificial Intelligence | Imaging Informatics and Artificial Intelligence

Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images

Authors: Shengping Cai, Yang Chen, Shixuan Zhao, Dehuai He, Yongjie Li, Nian Xiong, Zhidan Li, Shaoping Hu

Published in: European Radiology | Issue 7/2022

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Abstract

Objective

To develop a dynamic 3D radiomics analysis method using artificial intelligence technique for automatically assessing four disease stages (i.e., early, progressive, peak, and absorption stages) of COVID-19 patients on CT images.

Methods

The dynamic 3D radiomics analysis method was composed of three AI algorithms (the lung segmentation, lesion segmentation, and stage-assessing AI algorithms) that were trained and tested on 313,767 CT images from 520 COVID-19 patients. This proposed method used 3D lung lesion that was segmented by the lung and lesion segmentation algorithms to extract radiomics features, and then combined with clinical metadata to assess the possible stage of COVID-19 patients using stage-assessing algorithm. Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate diagnostic performance.

Results

Of 520 patients, 66 patients (mean age, 57 years ± 15 [standard deviation]; 35 women), including 203 CT scans, were tested. The dynamic 3D radiomics analysis method used 30 features, including 27 radiomics features and 3 clinical features to assess the possible disease stage of COVID-19 with an accuracy of 90%. For the prediction of each stage, the AUC of stage 1 was 0.965 (95% CI: 0.934, 0.997), AUC of stage 2 was 0.958 (95% CI: 0.931, 0.984), AUC of stage 3 was 0.998 (95% CI: 0.994, 1.000), and AUC of stage 4 was 0.975 (95% CI: 0.956, 0.994).

Conclusion

With high diagnostic performance, the dynamic 3D radiomics analysis using artificial intelligence could represent a potential tool for helping hospitals make appropriate resource allocations and follow-up of treatment response.

Key Points

• The AI segmentation algorithms were able to accurately segment the lung and lesion of COVID-19 patients of different stages.
• The dynamic 3D radiomics analysis method successfully extracted the radiomics features from the 3D lung lesion.
• The stage-assessing AI algorithm combining with clinical metadata was able to assess the four stages with an accuracy of 90%, a macro-average AUC of 0.975.
Appendix
Available only for authorised users
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Metadata
Title
Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images
Authors
Shengping Cai
Yang Chen
Shixuan Zhao
Dehuai He
Yongjie Li
Nian Xiong
Zhidan Li
Shaoping Hu
Publication date
29-01-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 7/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-021-08533-1

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