Theranostics 2020; 10(12):5613-5622. doi:10.7150/thno.45985 This issue Cite
1. Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
2. Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
3. The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
4. School of Communication and Information Engineering, Shanghai University, Shanghai, China
5. Institute of Healthcare Research, Yizhi, Shanghai, China
6. Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
7. Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
8. Department of Infectious Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
9. Department of Urology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
*Joint first authors, contributed equally
#Joint last authors, contributed equally
Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients.
Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models.
Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87~0.99; C-index=0.88, 95% CI 0.81~0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05~1.84, P=0.023) and 1.67 (95% CI 1.17~2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer.
Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.
Keywords: COVID-19, Chest CT, Severe illness, Retrospective cohort, Artificial intelligence