Time Course of Lung Changes On Chest CT During Recovery From 2019 Novel Coronavirus (COVID-19) Pneumonia

By F. Pan, Tianhe Ye, Peng Sun, Shan Gui, Bo Liang, Lingli Li, D. Zheng, Jiazheng Wang, Richard L. Hesketh, Lian Yang and 1 more. Radiology, 2020

2,104 citations recorded by the source corpus.

Background Chest CT is used to assess the severity of lung involvement in COVID-19 pneumonia. Purpose To determine the change in chest CT findings associated with COVID-19 pneumonia from initial diagnosis until patient recovery. Materials and Methods This retrospective review included patients with RT-PCR confirmed COVID-19 infection presenting between 12 January 2020 to 6 February 2020. Patients with severe respiratory distress and/ or oxygen requirement at any time during the disease course were excluded. Repeat Chest CT was obtained at approximately 4 day intervals. The total CT score was the sum of lung involvement (5 lobes, score 1-5 for each lobe, range, 0 none, 25 maximum) was determined. Results Twenty one patients (6 males and 15 females, age 25-63 years) with confirmed COVID-19 pneumonia were evaluated. These patients under went a total of 82 pulmonary CT scans with a mean interval of 4±1 days (range: 1-8 days). All patients were discharged after a mean hospitalized period of 17±4 days (range: 11-26 days). Maximum lung involved peaked at approximately 10 days (with the calculated total CT score of 6) from the onset of initial symptoms (R2=0.25), p<0.001). Based on quartiles of patients from day 0 to day 26 involvement, 4 stages of lung CT were defined: Stage 1 (0-4 days): ground glass opacities (GGO) in 18/24 (75%) patients with the total CT score of 2±2; (2)Stage-2 (5-8d days): increased crazy-paving pattern 9/17 patients (53%) with a increase in total CT score (6±4, p=0.002); (3) Stage-3 (9-13days): consolidation 19/21 (91%) patients with the peak of total CT score (7±4); (4) Stage-4 (≥14 days): gradual resolution of consolidation 15/20 (75%) patients with a decreased total CT score (6±4) without crazy-paving pattern. Conclusion In patients recovering from COVID-19 pneumonia (without severe respiratory distress during the disease course), lung abnormalities on chest CT showed greatest severity approximately 10 days after initial onset of symptoms.

Subfields: Infectious Diseases, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e955775ba634c0e445b028cc1fddeb499a46e1f3

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In Infectious Diseases

1. Nima Rezaei
2. Melika Lotfi
3. Michael R Hamblin

In Oncology

1. Nima Rezaei
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In Radiology, Nuclear Medicine and Imaging

1. Dinggang Shen
2. Qian Wang
3. Michael R Hamblin

Citations in the corpus

• COVID-19 and Multiorgan Response (2020, neutral): “Crazy paving patterns have also been observed.(27) A staging system for using computed topography images has been reported(33) and is summarized in Table 4.”
• Chest Radiographic and CT Findings of the 2019 Novel Coronavirus Disease (COVID-19): Analysis of Nine Patients Treated in Korea (2020, background): “As of February 16, 2020, COVID-19 infections had been confirmed in 51857 patients globally, including 51174 patients in China and 683 patients outside China (3).”
• Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis (2020, methodology): “In all cases, a semi-quantitative CT severity scoring proposed by Pan et al [15] was calculated per each of the 5 lobes considering the extent of anatomic involvement, as follows: 0, no involvement; 1, < 5% involvement; 2, 5–25% involvement; 3, 26–50% involvement; 4, 51–75% involvement; and 5, > 75%”
• Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. (2020, positive): “After day 14, improvement in imaging findings was reported in 75% of the patients, including decreased number of involved lobes and resolution of crazy paving pattern and consolidative opacities [25].”
• Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study (2020, result): “Also other studies confirm the relevance of these features and their association with the COVID-19 positivity [8, 30, 32, 44], compared to other kinds of pneumonia [43].”
• The Role of Imaging in the Detection and Management of COVID-19: A Review (2020, background): “Pan et al. [66] divided patients into Stage-1 (0-4 days), Stage-2 (5-8 days), Stage-3 (9-13 days), and Stage-4 ( ≥ 14 days).”
• COVID-19: Transmission, prevention, and potential therapeutic opportunities (2020, positive): “The current discharge criteria are as follows: (1) Two consecutive RT-PCR negative results in at least a 24 h interval; (2) Complete resolution of the patient's acute exudative pulmonary lesions on chest Computed Tomography (CT) examination; (3) Normalization of temperature for over 72 h; and (4) Re”
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19 (2020, background): “Dynamic radiological patterns in chest CT images of COVID19 have been reported and summarized as 4 stages [80].”
• Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study. (2020, background): “Exploring the CT changes and comparing them with the clinical parameters may help us monitor and predict outcome [28] and support clinical decision making.”