Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning.

Jang, Hyun-Jong; Lee, Ahwon; Kang, J; Song, In Hye; Lee, Sung Hak

Jang, Hyun-Jong; Lee, Ahwon; Kang, J; Song, In Hye; Lee, Sung Hak.

Afiliação

Jang HJ; Department of Physiology, Department of Biomedicine and Health Sciences, Catholic Neuroscience Institute, The Catholic University of Korea, Seoul 06591, South Korea.
Lee A; Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea.
Kang J; Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea.
Song IH; Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea.
Lee SH; Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea. hakjjang@catholic.ac.kr.

World J Gastroenterol ; 26(40): 6207-6223, 2020 Oct 28.

Article em En | MEDLINE | ID: mdl-33177794

Assuntos

Neoplasias do Colo; Neoplasias Colorretais; Aprendizado Profundo; Neoplasias do Colo/genética; Neoplasias Colorretais/diagnóstico por imagem; Neoplasias Colorretais/genética; Genes p53; Humanos; Mutação

Palavras-chave

Colorectal cancer; Computational pathology; Computer-aided diagnosis; Deep learning; Digital pathology; Mutation

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Neoplasias do Colo / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: World J Gastroenterol Assunto da revista: GASTROENTEROLOGIA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Coréia do Sul País de publicação: Estados Unidos

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