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.
Artículo
en Inglés
| 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.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Neumonía Viral
/
Interpretación de Imagen Radiográfica Asistida por Computador
/
Tomografía Computarizada por Rayos X
/
Gripe Humana
/
COVID-19
Tipo de estudio:
Estudios diagnósticos
/
Estudio observacional
/
Estudio pronóstico
Límite:
Adulto
/
Femenino
/
Humanos
/
Masculino
/
Middle aged
Idioma:
Inglés
Revista:
BMC Med Imaging
Asunto de la revista:
Diagnóstico por Imagen
Año:
2021
Tipo del documento:
Artículo
País de afiliación:
S12880-021-00564-w
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