Int J Med Sci 2020; 17(12):1773-1782. doi:10.7150/ijms.48432

Research Paper

A Quantitative and Radiomics approach to monitoring ARDS in COVID-19 patients based on chest CT: a retrospective cohort study

Yuntian Chen1#, Yi Wang1#, Yuwei Zhang3, Na Zhang4, Shuang Zhao1, Hanjiang Zeng1, Wen Deng1, Zixing Huang1, Sanyuan Liu2, Bin Song1✉

1. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
2. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd. Shanghai 200232, China.
3. Department of Endocrinology, West China Hospital, Sichuan University, Chengdu 610041, China.
4. Department of Radiology, Chengdu Public Health Clinical Medical Center, Chengdu 610066, China.
#These authors contributed equally to this work.

This is an open access article distributed under the terms of the Creative Commons Attribution License ( See for full terms and conditions.
Chen Y, Wang Y, Zhang Y, Zhang N, Zhao S, Zeng H, Deng W, Huang Z, Liu S, Song B. A Quantitative and Radiomics approach to monitoring ARDS in COVID-19 patients based on chest CT: a retrospective cohort study. Int J Med Sci 2020; 17(12):1773-1782. doi:10.7150/ijms.48432. Available from

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Rationale: Acute respiratory distress syndrome (ARDS) is one of the major reasons for ventilation and intubation management of COVID-19 patients but there is no noninvasive imaging monitoring protocol for ARDS. In this study, we aimed to develop a noninvasive ARDS monitoring protocol based on traditional quantitative and radiomics approaches from chest CT.

Methods: Patients diagnosed with COVID-19 from Jan 20, 2020 to Mar 31, 2020 were enrolled in this study. Quantitative and radiomics data were extracted from automatically segmented regions of interest (ROIs) of infection regions in the lungs. ARDS existence was measured by Pa02/Fi02 <300 in artery blood samples. Three different models were constructed by using the traditional quantitative imaging metrics, radiomics features and their combinations, respectively. Receiver operating characteristic (ROC) curve analysis was used to assess the effectiveness of the models. Decision curve analysis (DCA) was used to test the clinical value of the proposed model.

Results: The proposed models were constructed using 352 CT images from 86 patients. The median age was 49, and the male proportion was 61.9%. The training dataset and the validation dataset were generated by randomly sampling the patients with a 2:1 ratio. Chi-squared test showed that there was no significant difference in baseline of the enrolled patients between the training and validation datasets. The areas under the ROC curve (AUCs) of the traditional quantitative model, radiomics model and combined model in the validation dataset was 0.91, 0.91 and 0.94, respectively. Accordingly, the sensitivities were 0.55, 0.82 and 0.58, while the specificities were 0.97, 0.86 and 0.98. The DCA curve showed that when threshold probability for a doctor or patients is within a range of 0 to 0.83, the combined model adds more net benefit than “treat all” or “treat none” strategies, while the traditional quantitative model and radiomics model could add benefit in all threshold probability.

Conclusions: It is feasible to monitor ARDS from CT images using radiomics or traditional quantitative analysis in COVID-19. The radiomics model seems to be the most practical one for possible clinical use. Multi-center validation with a larger number of samples is recommended in the future.

Keywords: COVID-19, Computed tomography, Acute respiratory distress syndrome, Radiomics, Quantitative