Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study

By Yuhui Wang, C. Dong, Yue Hu, Chungao Li, Qianqian Ren, Xin Zhang, Heshui Shi, Min Zhou. Radiology, 2020

885 citations recorded by the source corpus.

Background CT may play a central role in the diagnosis and management of COVID-19 pneumonia. Purpose To perform a longitudinal study to analyze the serial CT findings over time in patients with COVID-19 pneumonia. Materials and Methods During January 16 to February 17, 2020, 90 patients (male:female, 33:57; mean age, 45 years) with COVID-19 pneumonia were prospectively enrolled and followed up until they were discharged or died, or until the end of the study. A total of 366 CT scans were acquired and reviewed by 2 groups of radiologists for the patterns and distribution of lung abnormalities, total CT scores and number of zones involved. Those features were analyzed for temporal change. Results CT scores and number of zones involved progressed rapidly, peaked during illness days 6-11 (median: 5 and 5), and followed by persistence of high levels. The predominant pattern of abnormalities after symptom onset was ground-glass opacity (35/78 [45%] to 49/79 [62%] in different periods). The percentage of mixed pattern peaked (30/78 [38%]) on illness days 12-17, and became the second most predominant pattern thereafter. Pure ground-glass opacity was the most prevalent sub-type of ground-glass opacity after symptom onset (20/50 [40%] to 20/28 [71%]). The percentage of ground-glass opacity with irregular linear opacity peaked on illness days 6-11 (14/50 [28%)]) and became the second most prevalent subtype thereafter. The distribution of lesions was predominantly bilateral and subpleural. 66/70 (94%) patients discharged had residual disease on final CT scans (median CT scores and zones involved: 4 and 4), with ground-glass opacity (42/70 [60%]) and pure ground-glass opacity (31/42 [74%]) the most common pattern and subtype. Conclusion The extent of lung abnormalities on CT peaked during illness days 6-11. The temporal changes of the diverse CT manifestations followed a specific pattern, which might indicate the progression and recovery of the illness.

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

View Paper: https://www.semanticscholar.org/paper/8153042ea92b90b92ed7b4d768ae8ee0a0e11f54

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Citations in the corpus

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation (2020, methodology): “[16] found that the COVID-19 disease will be severe during 6-11 days from the infection, based on a study on 366 CT scans of 90 patients.”
Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach (2020, neutral): “The second most predominant pattern of lung opacity abnormalities peak during days 12-17 of the illness [12].”
The Role of Imaging in the Detection and Management of COVID-19: A Review (2020, background): “Wang et al. [67] investigated temporal changes of chest CT characteristics, and divided the stages as < 0 days, 0-5 days, 6-11 days, 12-17 days, 18-23 days and ≥ 24 days (Fig.”
CO-RADS – A categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation (2020, background): “CO-RADS 5 requires the presence of at least one confirmatory pattern, which aligns with the temporal evolution of the disease (15).”
CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection (2020, methodology): “In a research published in the journal Radiology [17], chest radiography outperformed laboratory testing in the detection of 2019 novel coronavirus disease.”
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches (2020, background): “The objective of this study was to define disease progression and recovery of the illness and found that the peak period during illness were days 6-11 [11].”