Any unique image biomarkers associated with COVID-19?
Eur Radiol
; 30(11): 6221-6227, 2020 Nov.
Article
en En
| MEDLINE
| ID: mdl-32462445
ABSTRACT
OBJECTIVE:
To define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers.METHODS:
We retrospectively collected chest CT exams including n = 498 on 151 unique patients RT-PCR positive for COVID-19 and n = 497 unique patients with community-acquired pneumonia (CAP). Both COVID-19 and CAP image sets were partitioned into three groups for training, validation, and testing respectively. In an attempt to discriminate COVID-19 from CAP, we developed several classifiers based on three-dimensional (3D) convolutional neural networks (CNNs). We also asked two experienced radiologists to visually interpret the testing set and discriminate COVID-19 from CAP. The classification performance of the computer algorithms and the radiologists was assessed using the receiver operating characteristic (ROC) analysis, and the nonparametric approaches with multiplicity adjustments when necessary.RESULTS:
One of the considered models showed non-trivial, but moderate diagnostic ability overall (AUC of 0.70 with 99% CI 0.56-0.85). This model allowed for the identification of 8-50% of CAP patients with only 2% of COVID-19 patients.CONCLUSIONS:
Professional or automated interpretation of CT exams has a moderately low ability to distinguish between COVID-19 and CAP cases. However, the automated image analysis is promising for targeted decision-making due to being able to accurately identify a sizable subsect of non-COVID-19 cases. KEY POINTS ⢠Both human experts and artificial intelligent models were used to classify the CT scans. ⢠ROC analysis and the nonparametric approaches were used to analyze the performance of the radiologists and computer algorithms. ⢠Unique image features or patterns may not exist for reliably distinguishing all COVID-19 from CAP; however, there may be imaging markers that can identify a sizable subset of non-COVID-19 cases.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neumonía Viral
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Interpretación de Imagen Asistida por Computador
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Tomografía Computarizada por Rayos X
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Infecciones por Coronavirus
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Betacoronavirus
Tipo de estudio:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
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Female
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Humans
/
Male
Idioma:
En
Revista:
Eur Radiol
Asunto de la revista:
RADIOLOGIA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos