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ESTIMATING COVID-19 PNEUMONIA EXTENT AND SEVERITY FROM CHEST COMPUTED TOMOGRAPHY
CT-estimated lung volume
CT-estimated lung weight
Computed tomography
Deep learning
Author
Affilliation
University of Porto. Faculty of Medicine. Department of Surgery and Physiology. Cardiovascular R&D Centre. Porto, Portugal / Universidade Federal do Rio de Janeiro. Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering. Biomedical Engineering Program. Laboratory of Pulmonary Engineering. Rio de Janeiro, RJ, Brazil / Universidade Federal do Rio de Janeiro. Carlos Chagas Filho Institute of Biophysics. Laboratory of Respiration Physiology. Rio de Janeiro, RJ, Brazil.
Universidade Federal do Rio de Janeiro. Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering. Biomedical Engineering Program. Laboratory of Pulmonary Engineering. Rio de Janeiro, RJ, Brazil.
Universidade Estácio de Sá. Medical Faculty. Rio de Janeiro, RJ, Brazil.
Universidade Federal do Rio de Janeiro. Department of Radiology. Rio de Janeiro, RJ, Brazil.
Hospital Niteroi D'Or. Niterói, RJ, Brazil.
Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. Department of Radiology. Rio de Janeiro, RJ, Brazil / Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, RJ, Brasil.
Complexo Hospitalar Universitário do Porto. Radiology Department. Porto, Portugal.
Complexo Hospitalar Universitário do Porto. Radiology Department. Porto, Portugal.
Universidade Federal do Rio de Janeiro. Carlos Chagas Filho Institute of Biophysics. Laboratory of Respiration Physiology. Rio de Janeiro, RJ, Brazil.
Complexo Hospitalar Universitário do Porto. Radiology Department. Porto, Portugal / Porto University. Instituto de Ciências Biomeìdicas Abel Salazar. Porto, Portugal.
Universidade Federal do Rio de Janeiro. Alberto Luiz Coimbra Institute of Post-Graduation and Research in Engineering. Biomedical Engineering Program. Laboratory of Pulmonary Engineering. Rio de Janeiro, RJ, Brazil.
Universidade Estácio de Sá. Medical Faculty. Rio de Janeiro, RJ, Brazil.
Universidade Federal do Rio de Janeiro. Department of Radiology. Rio de Janeiro, RJ, Brazil.
Hospital Niteroi D'Or. Niterói, RJ, Brazil.
Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil / Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. Department of Radiology. Rio de Janeiro, RJ, Brazil / Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, RJ, Brasil.
Complexo Hospitalar Universitário do Porto. Radiology Department. Porto, Portugal.
Complexo Hospitalar Universitário do Porto. Radiology Department. Porto, Portugal.
Universidade Federal do Rio de Janeiro. Carlos Chagas Filho Institute of Biophysics. Laboratory of Respiration Physiology. Rio de Janeiro, RJ, Brazil.
Complexo Hospitalar Universitário do Porto. Radiology Department. Porto, Portugal / Porto University. Instituto de Ciências Biomeìdicas Abel Salazar. Porto, Portugal.
Abstract
Background: COVID-19 pneumonia extension is assessed by computed tomography (CT) with the ratio between the volume of abnormal pulmonary opacities (PO) and CT-estimated lung volume (CTLV). CT-estimated lung weight (CTLW) also correlates with pneumonia severity. However, both CTLV and CTLW depend on demographic and anthropometric variables.
Purposes: To estimate the extent and severity of COVID-19 pneumonia adjusting the volume and weight of abnormal PO to the predicted CTLV (pCTLV) and CTLW (pCTLW), respectively, and to evaluate their possible association with clinical and radiological outcomes.
Methods: Chest CT from 103 COVID-19 and 86 healthy subjects were examined retrospectively. In controls, predictive equations for estimating pCTLV and pCTLW were assessed. COVID-19 pneumonia extent and severity were then defined as the ratio between the volume and the weight of abnormal PO expressed as a percentage of the pCTLV and pCTLW, respectively. A ROC analysis was used to test differential diagnosis ability of the proposed method in COVID-19 and controls. The degree of pneumonia extent and severity was assessed with Z-scores relative to the average volume and weight of PO in controls. Accordingly, COVID-19 patients were classified as with limited, moderate and diffuse pneumonia extent and as with mild, moderate and severe pneumonia severity.
Results: In controls, CTLV could be predicted by sex and height (adjusted R 2 = 0.57; P < 0.001) while CTLW by age, sex, and height (adjusted R 2 = 0.6; P < 0.001). The cutoff of 20% (AUC = 0.91, 95%CI 0.88-0.93) for pneumonia extent and of 50% (AUC = 0.91, 95%CI 0.89-0.92) for pneumonia severity were obtained. Pneumonia extent were better correlated when expressed as a percentage of the pCTLV and pCTLW (r = 0.85, P < 0.001), respectively. COVID-19 patients with diffuse and severe pneumonia at admission presented significantly higher CRP concentration, intra-hospital mortality, ICU stay and ventilatory support necessity, than those with moderate and limited/mild pneumonia. Moreover, pneumonia severity, but not extent, was positively and moderately correlated with age (r = 0.46) and CRP concentration (r = 0.44).
Conclusion: The proposed estimation of COVID-19 pneumonia extent and severity might be useful for clinical and radiological patient stratification.
Keywords
COVID-19CT-estimated lung volume
CT-estimated lung weight
Computed tomography
Deep learning
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