Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.

Journal: The Lancet. Digital health
PMID: 34686474

Abstract

BACKGROUND: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as an alternative to current tests.

Authors

  • Mohsin Bilal
    Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK.
  • Shan E Ahmed Raza
    Division of Molecular Pathology, The Institute of Cancer Research, UK; Department of Computer Science, University of Warwick, UK; Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. Electronic address: shan.raza@icr.ac.uk.
  • Ayesha Azam
    Department of Computer Science, University of Warwick, UK; University Hospitals Coventry and Warwickshire, Coventry, UK.
  • Simon Graham
    Mathematics for Real World Systems Centre for Doctoral Training, University of Warwick, Coventry, CV4 7AL, UK; Department of Computer Science, University of Warwick, UK. Electronic address: s.graham.1@warwick.ac.uk.
  • Mohammad Ilyas
    Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK.
  • Ian A Cree
  • David Snead
    Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK.
  • Fayyaz Minhas
    Department of Computer Science, University of Warwick, Coventry, UK.
  • Nasir M Rajpoot

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