CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia.
BMC Med Imaging
; 21(1): 31, 2021 02 17.
Article
in English
| MEDLINE | ID: covidwho-1088584
ABSTRACT
BACKGROUND:
In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia.METHODS:
A total of 154 patients with confirmed viral pneumonia (COVID-19 89 cases, influenza pneumonia 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis.RESULTS:
The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%).CONCLUSIONS:
CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Radiographic Image Interpretation, Computer-Assisted
/
Tomography, X-Ray Computed
/
Influenza, Human
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
BMC Med Imaging
Journal subject:
Diagnostic Imaging
Year:
2021
Document Type:
Article
Affiliation country:
S12880-021-00564-w
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