Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease...

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Detalles Bibliográficos
Autores principales: Sirinukunwattana, Korsuk, Domingo, Enric, Richman, Susan D, Redmond, Keara L, Blake, Andrew, Verrill, Clare, Leedham, Simon J, Chatzipli, Aikaterini, Hardy, Claire, Whalley, Celina M, Wu, Chieh-hsi, Beggs, Andrew D, McDermott, Ultan, Dunne, Philip D, Meade, Angela, Walker, Steven M, Murray, Graeme I, Samuel, Leslie, Seymour, Matthew, Tomlinson, Ian, Quirke, Phil, Maughan, Timothy, Rittscher, Jens, Koelzer, Viktor H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Artificial Intelligence/Machine Learning
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873419/
https://www.ncbi.nlm.nih.gov/pubmed/32690604
http://dx.doi.org/10.1136/gutjnl-2019-319866

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873419/
https://www.ncbi.nlm.nih.gov/pubmed/32690604
http://dx.doi.org/10.1136/gutjnl-2019-319866

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