Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review.
Postgrad Med J
; 98(1161): e20, 2022 Jul.
Article
em En
| MEDLINE
| ID: mdl-33688072
ABSTRACT
CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
/
Neoplasias
Tipo de estudo:
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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Systematic_reviews
Limite:
Humans
Idioma:
En
Revista:
Postgrad Med J
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Reino Unido