End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study.

Journal: The Lancet. Digital health
Published Date:

Abstract

BACKGROUND: Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine.

Authors

  • Xiaofeng Jiang
    Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Michael Hoffmeister
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Hermann Brenner
    German Cancer Consortium (DKTK), Heidelberg, Germany.
  • Hannah Sophie Muti
    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
  • Tanwei Yuan
    Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Sebastian Foersch
    Institute of Pathology, University Medical Center Mainz, Mainz, Germany. Electronic address: sebastian.foersch@unimedizin-mainz.de.
  • Nicholas P West
    Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Alexander Brobeil
    Institute of Pathology, University of Heidelberg, Heidelberg, Germany; Tissue Bank of the National Center for Tumor Diseases (NCT), Heidelberg, Germany.
  • Jitendra Jonnagaddala
    School of Public Health and Community Medicine, University of New South Wales, Australia; Asia-Pacific Ubiquitous Healthcare Research Centre, University of New South Wales, Australia; Prince of Wales Clinical School, University of New South Wales, Australia.
  • Nicholas Hawkins
    School of Medical Sciences, Faculty of Medicine and Health, University of New South Wales Sydney, Kensington, NSW, Australia.
  • Robyn L Ward
    School of Medical Sciences, Faculty of Medicine and Health, University of New South Wales Sydney, Kensington, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia.
  • Titus J Brinker
    National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Oliver Lester Saldanha
    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
  • Jia Ke
    Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Wolfram Müller
    Gemeinschaftspraxis Pathologie, Starnberg, Germany.
  • Heike I Grabsch
    Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands.
  • Philip Quirke
    Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Daniel Truhn
    Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).
  • Jakob Nikolas Kather
    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.