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Xinping Shen
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Background: Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis.
Methods : Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19).
Results : Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p<0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score>4, a sensitivity of 100% and a specificity of 23.26% for a score>0, and a sensitivity of 86.67% and a specificity of 67.44% for a score>2.
Conclusions : With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.
Keywords
Coronavirus infections, Pneumonia, Tomography, x-ray computed, Lung diseases
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CURRENT STATUS: Published
View the published article at BMC Pulmonary Medicine.
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Editorial decision: Accept
On 27 Apr, 2020
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On 02 Apr, 2020
Version 2
Posted 29 Apr, 2020
No comments provided
Editorial decision: Minor revision
On 24 Apr, 2020
Editor assigned
On 20 Apr, 2020
Submission checks complete
On 19 Apr, 2020
Editor invited
On 19 Apr, 2020
No comments provided
Reviewer #2 agreed
On 06 Apr, 2020
Review #2 received
Received 06 Apr, 2020
Editorial decision: Major revision
On 06 Apr, 2020
Review #1 received
Received 05 Apr, 2020
Editor assigned
On 04 Apr, 2020
Reviewers invited
Invitations sent on 04 Apr, 2020
Reviewer #1 agreed
On 04 Apr, 2020
Submission checks complete
On 03 Apr, 2020
Editor invited
On 03 Apr, 2020
First submitted
On 30 Mar, 2020
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See the published version of this preprint at BMC Pulmonary Medicine.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Xinping Shen
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Pulmonary Medicine.
No community comments so far
Editorial decision: Accept
On 27 Apr, 2020
Submission checks complete
On 02 Apr, 2020
Version 2
Posted 29 Apr, 2020
No comments provided
Editorial decision: Minor revision
On 24 Apr, 2020
Editor assigned
On 20 Apr, 2020
Submission checks complete
On 19 Apr, 2020
Editor invited
On 19 Apr, 2020
No comments provided
Reviewer #2 agreed
On 06 Apr, 2020
Review #2 received
Received 06 Apr, 2020
Editorial decision: Major revision
On 06 Apr, 2020
Review #1 received
Received 05 Apr, 2020
Editor assigned
On 04 Apr, 2020
Reviewers invited
Invitations sent on 04 Apr, 2020
Reviewer #1 agreed
On 04 Apr, 2020
Submission checks complete
On 03 Apr, 2020
Editor invited
On 03 Apr, 2020
First submitted
On 30 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Pulmonary Medicine.
Background: Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis.
Methods : Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19).
Results : Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p<0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score>4, a sensitivity of 100% and a specificity of 23.26% for a score>0, and a sensitivity of 86.67% and a specificity of 67.44% for a score>2.
Conclusions : With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.
Figure 1
Figure 2
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