Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice.
Clin Radiol
; 75(1): 38-45, 2020 01.
Article
en En
| MEDLINE
| ID: mdl-31521323
ABSTRACT
AIM:
To test the diagnostic performance of a deep learning-based system for the detection of clinically significant pulmonary nodules/masses on chest radiographs. MATERIALS ANDMETHODS:
Using a retrospective study of 100 patients (47 with clinically significant pulmonary nodules/masses and 53 control subjects without pulmonary nodules), two radiologists verified clinically significantly pulmonary nodules/masses according to chest computed tomography (CT) findings. A computer-aided diagnosis (CAD) software using a deep-learning approach was used to detect pulmonary nodules/masses to determine the diagnostic performance in four algorithms (heat map, abnormal probability, nodule probability, and mass probability).RESULTS:
A total of 100 cases were included in the analysis. Among the four algorithms, mass algorithm could achieve a 76.6% sensitivity (36/47, 11 false negative) and 88.68% specificity (47/53, six false-positive) in the detection of pulmonary nodules/masses at the optimal probability score cut-off of 0.2884. Compared to the other three algorithms, mass probability algorithm had best predictive ability for pulmonary nodule/mass detection at the optimal probability score cut-off of 0.2884 (AUCMass 0.916 versus AUCHeat map 0.682, p<0.001; AUCMass 0.916 versus AUCAbnormal 0.810, p=0.002; AUCMass 0.916 versus AUCNodule 0.813, p=0.014).CONCLUSION:
In conclusion, the deep-learning based computer-aided diagnosis system will likely play a vital role in the early detection and diagnosis of pulmonary nodules/masses on chest radiographs. In future applications, these algorithms could support triage workflow via double reading to improve sensitivity and specificity during the diagnostic process.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Radiografía Torácica
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Tomografía Computarizada por Rayos X
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Nódulo Pulmonar Solitario
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Nódulos Pulmonares Múltiples
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Aprendizaje Profundo
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Neoplasias Pulmonares
Tipo de estudio:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
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Screening_studies
Límite:
Female
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Humans
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Male
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Middle aged
País/Región como asunto:
Asia
Idioma:
En
Año:
2020
Tipo del documento:
Article