Dynamic Imaging Changes of COVID-19 Pneumonia at Different Stages.

INTRODUCTION: To investigate the Computed Tomography (CT) imaging characteristics and dynamic changes of COVID-19 pneumonia at different stages. METHODS: Forty-six patients infected with COVID-19 who had chest CT scans were enrolled, and CT scans were performed 4-6 times with an interval of 2-5 days. RESULTS: At the early stage (n=25), ground glass opacity was presented in 11 patients (11/25 or 44.0 %) and ground glass opacity mixed with consolidation in 13 (13/25 or 52.0 %) in the lung CT images. At the progressive stage (n=38), ground glass opacity was presented in only one patient (1/38 or 2.6 %) and ground glass opacity mixed with consolidation in 33 (33/38 or 86.8 %). In the early improvement stage (n=38), the imaging presentation was ground glass opacity alone in three patients (3/38 or 7.9 %) and ground glass opacity mixed with consolidation in 34 (34/38 or 89.5 %). In the late improvement (absorption) stage (n=33), the primary imaging presentation was ground glass presentation in eight patients (8/33 or 24.2 %) and ground glass opacity mixed with consolidation in 23 (23/33 or 69.7 %). The lesion reached the peak at 4-16 days after disease onset, and 26 (26/38 or 68.4 %) patients reached the disease peak within ten days. Starting from 6 to 20 days after onset, the disease began to be improved, with 30 (30/38 or 78.9 %) patients being improved within 15 days. CONCLUSION: COVID-19 pneumonia will progress to the peak stage at a mediate time of seven days and enter the improvement stage at twelve days. Computed tomography imaging of the pulmonary lesion has a common pattern from disease onset to improvement and recovery and provides important information for evaluation of the disease course and treatment effect.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Clinical and computed tomographic imaging features of novel coronavirus pneumonia caused by SARS-CoV-2.

PURPOSE: To investigate the clinical and imaging characteristics of computed tomography (CT) in novel coronavirus pneumonia (NCP) caused by SARS-CoV-2. MATERIALS AND METHODS: A retrospective analysis was performed on the imaging findings of patients confirmed with COVID-19 pneumonia who had chest CT scanning and treatment after disease onset. The clinical and imaging data were analyzed. RESULTS: Fifty patients were enrolled, including mild type in nine, common in 28, severe in 10 and critically severe in the rest three. Mild patients (29 years) were significantly (P<0.03) younger than either common (44.5 years) or severe (54.7) and critically severe (65.7 years) patients, and common patients were also significantly (P<0.03) younger than severe and critically severe patients. Mild patients had low to moderate fever (<39.1 °C), 49 (98%) patients had normal or slightly reduced leukocyte count, 14 (28%) had decreased counts of lymphocytes, and 26 (52%) patients had increased C-reactive protein. Nine mild patients were negative in CT imaging. For all the other types of NCP, the lesion was in the right upper lobe in 30 cases, right middle lobe in 22, right lower lobe in 39, left upper lobe in 33 and left lower lobe in 36. The lesion was primarily located in the peripheral area under the pleura with possible extension towards the pulmonary hilum. Symmetrical lesions were seen in 26 cases and asymmetrical in 15. The density of lesion was mostly uneven with ground glass opacity as the primary presentation accompanied by partial consolidation and fibrosis. CONCLUSION: CT imaging presentations of NCP are mostly patchy ground glass opacities in the peripheral areas under the pleura with partial consolidation which will be absorbed with formation of fibrotic stripes if improved. CT scanning provides important bases for early diagnosis and treatment of NCP.

Clinical and pathological investigation of severe COVID-19 patients

BACKGROUND: Severe acute respiratory coronavirus 2 (SARS-CoV-2) caused coronavirus disease 2019 (COVID-19) has become a pandemic. This study addressed the clinical and immunopathological characteristics of severe COVID-19. METHODS: Sixty-nine COVID-19 patients were classified into as severe and non-severe groups to analyze their clinical and laboratory characteristics. A panel of blood cytokines was quantified over time. Biopsy specimens from two deceased cases were obtained for immunopathological, ultrastructural, and in situ hybridization examinations. RESULTS: Circulating cytokines, including IL8, IL6, TNFα, IP10, MCP1, and RANTES, were significantly elevated in severe COVID-19 patients. Dynamic IL6 and IL8 were associated with disease progression. SARS-CoV-2 was demonstrated to infect type II, type I pneumocytes and endothelial cells, leading to severe lung damage through cell pyroptosis and apoptosis. In severe cases, lymphopenia, neutrophilia, depletion of CD4+ and CD8+ T lymphocytes, and massive macrophage and neutrophil infiltrates were observed in both blood and lung tissues. CONCLUSIONS: A panel of circulating cytokines could be used to predict disease deterioration and inform clinical interventions. Severe pulmonary damage was predominantly attributed to both SARS-CoV-2 caused cytopathy and immunopathologic damage. Strategies that encourage pulmonary recruitment and overactivation of inflammatory cells by suppressing cytokine storm might improve the outcomes of severe COVID-19 patients.