Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.
Eur Radiol
; 32(8): 5319-5329, 2022 Aug.
Article
em En
| MEDLINE
| ID: mdl-35201409
ABSTRACT
OBJECTIVES:
Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD.METHODS:
A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients.RESULTS:
The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI 0.903, 0.961) on the internal test set and 0.866 (95% CI 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale.CONCLUSIONS:
The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging. KEY POINTS ⢠Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries. ⢠The proposed chest computed tomography (CT)-based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale. ⢠The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Doença Pulmonar Obstrutiva Crônica
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Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
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Screening_studies
Limite:
Humans
Idioma:
En
Revista:
Eur Radiol
Assunto da revista:
RADIOLOGIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China