Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Mais filtros

Base de dados
Ano de publicação
Tipo de documento
País de afiliação
Intervalo de ano de publicação
1.
Sci Rep ; 13(1): 7720, 2023 05 12.
Artigo em Inglês | MEDLINE | ID: mdl-37173327

RESUMO

Computer-assisted diagnosis (CAD) algorithms have shown its usefulness for the identification of pulmonary nodules in chest x-rays, but its capability to diagnose lung cancer (LC) is unknown. A CAD algorithm for the identification of pulmonary nodules was created and used on a retrospective cohort of patients with x-rays performed in 2008 and not examined by a radiologist when obtained. X-rays were sorted according to the probability of pulmonary nodule, read by a radiologist and the evolution for the following three years was assessed. The CAD algorithm sorted 20,303 x-rays and defined four subgroups with 250 images each (percentiles ≥ 98, 66, 33 and 0). Fifty-eight pulmonary nodules were identified in the ≥ 98 percentile (23,2%), while only 64 were found in lower percentiles (8,5%) (p < 0.001). A pulmonary nodule was confirmed by the radiologist in 39 out of 173 patients in the high-probability group who had follow-up information (22.5%), and in 5 of them a LC was diagnosed with a delay of 11 months (12.8%). In one quarter of the chest x-rays considered as high-probability for pulmonary nodule by a CAD algorithm, the finding is confirmed and corresponds to an undiagnosed LC in one tenth of the cases.


Assuntos
Neoplasias Pulmonares , Nódulos Pulmonares Múltiplos , Nódulo Pulmonar Solitário , Humanos , Raios X , Tomografia Computadorizada por Raios X/métodos , Estudos Retrospectivos , Nódulo Pulmonar Solitário/diagnóstico por imagem , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Sensibilidade e Especificidade , Neoplasias Pulmonares/diagnóstico por imagem , Diagnóstico por Computador/métodos , Nódulos Pulmonares Múltiplos/diagnóstico por imagem
SELEÇÃO DE REFERÊNCIAS
Detalhe da pesquisa