Your browser doesn't support javascript.
loading
Deep learning based tissue analysis predicts outcome in colorectal cancer.
Bychkov, Dmitrii; Linder, Nina; Turkki, Riku; Nordling, Stig; Kovanen, Panu E; Verrill, Clare; Walliander, Margarita; Lundin, Mikael; Haglund, Caj; Lundin, Johan.
Afiliación
  • Bychkov D; Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki, Helsinki, Finland. dmitrii.bychkov@helsinki.fi.
  • Linder N; Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki, Helsinki, Finland.
  • Turkki R; Department of Women's and Children's Health, International Maternal and Child Health (IMCH), Uppsala University, Uppsala, Sweden.
  • Nordling S; Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki, Helsinki, Finland.
  • Kovanen PE; Department of Pathology, Medicum, University of Helsinki, Helsinki, Finland.
  • Verrill C; Department of Pathology, University of Helsinki and HUSLAB, Helsinki University Hospital, Helsinki, Finland.
  • Walliander M; Nuffield Department of Surgical Sciences, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
  • Lundin M; Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki, Helsinki, Finland.
  • Haglund C; Institute for Molecular Medicine Finland FIMM, Helsinki Institute for Life Science HiLIFE, University of Helsinki, Helsinki, Finland.
  • Lundin J; Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Sci Rep ; 8(1): 3395, 2018 02 21.
Article en En | MEDLINE | ID: mdl-29467373
Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2018 Tipo del documento: Article País de afiliación: Finlandia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Año: 2018 Tipo del documento: Article País de afiliación: Finlandia Pais de publicación: Reino Unido