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

Journal: Gut
Published Date: Jul 20, 2020

Abstract

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 progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning.

Authors

  • Korsuk Sirinukunwattana
  • Enric Domingo
    Department of Oncology, University of Oxford, Oxford, UK.
  • Susan D Richman
    Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Keara L Redmond
    Centre for Cancer Research and Cell Biology, Faculty of Medicine, Health and Life Sciences, Queen's University Belfast, Belfast, UK.
  • Andrew Blake
    Department of Oncology, University of Oxford, Oxford, UK.
  • Clare Verrill
    Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK; Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK. Electronic address: Clare.Verrill@ouh.nhs.uk.
  • Simon J Leedham
    Gastrointestinal Stem-cell Biology Laboratory, Oxford Centre for Cancer Gene Research, Wellcome Trust Centre for Human Genetics, Oxford, UK.
  • Aikaterini Chatzipli
    Wellcome Trust Sanger Institute, Hinxton, UK.
  • Claire Hardy
    Wellcome Trust Sanger Institute, Hinxton, UK.
  • Celina M Whalley
    Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.
  • Chieh-Hsi Wu
    Department of Statistics, University of Oxford, Oxford, UK.
  • Andrew D Beggs
    School of Cancer Sciences, University of Birmingham, Birmingham, UK.
  • Ultan McDermott
    Wellcome Trust Sanger Institute, Hinxton, UK.
  • Philip D Dunne
    Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK.
  • Angela Meade
    MRC Clinical Trials Unit at University College London, London, UK.
  • Steven M Walker
    Almac Diagnostics Ltd, Craigavon, UK.
  • Graeme I Murray
    Department of Pathology, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.
  • Leslie Samuel
    Department of Clinical Oncology, Aberdeen Royal Infirmary, Aberdeen, UK.
  • Matthew Seymour
    Department of Oncology, Leeds Institute of Cancer and Pathology, Leeds, UK.
  • Ian Tomlinson
    Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Edinburgh Cancer Research Centre, University of Edinburgh, Edinburgh, UK.
  • Phil Quirke
    Department of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, Leeds, UK.
  • Timothy Maughan
    CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK.
  • Jens Rittscher
    Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
  • Viktor H Koelzer
    Institute of Cancer and Genomic Science, University of Birmingham, 6 Mindelsohn Way, Birmingham, B15 2SY, UK. vkoelzer@well.ox.ac.uk.

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