Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts.

Journal: The Lancet. Digital health
PMID: 36496303

Abstract

BACKGROUND: Endometrial cancer can be molecularly classified into POLE, mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole-slide-image-based prediction of the four molecular classes in endometrial cancer (im4MEC), to identify morpho-molecular correlates, and to refine prognostication.

Authors

  • Sarah Fremond
    Department of Pathology, Leiden University Medical Center, Leiden, Netherlands.
  • Sonali Andani
    Department of Computer Science, ETH Zurich, Zurich, Switzerland; Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
  • Jurriaan Barkey Wolf
    Department of Pathology, Leiden University Medical Center, Leiden, Netherlands.
  • Jouke Dijkstra
    Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands.
  • SinĂ©ad Melsbach
    Department of Pathology, Leiden University Medical Center, Leiden, Netherlands.
  • Jan J Jobsen
    Department of Radiation Oncology, Medisch Spectrum Twente, Enschede, Netherlands.
  • Mariel Brinkhuis
    Department of Pathology, LabPON, Hengelo, Netherlands.
  • Suzan Roothaan
    Department of Pathology, LabPON, Hengelo, Netherlands.
  • Ina Jurgenliemk-Schulz
    Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands.
  • Ludy C H W Lutgens
    Department of Radiation Oncology, Maastricht University Medical Center+, Maastricht, Netherlands.
  • Remi A Nout
    Department of Radiation Oncology, Erasmus University Medical Center, Rotterdam, Netherlands.
  • Elzbieta M van der Steen-Banasik
    Department of Radiation Oncology, Radiotherapiegroep, Arnhem, Netherlands.
  • Stephanie M de Boer
    Department of Radiation Oncology, Leiden University Medical Center, Leiden, Netherlands.
  • Melanie E Powell
    Department of Clinical Oncology, Barts Health NHS Trust, London, UK.
  • Naveena Singh
    Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
  • Linda R Mileshkin
    Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, VIC, Australia.
  • Helen J Mackay
    Department of Medical Oncology and Hematology, Odette Cancer Center Sunnybrook Health Sciences Center, Toronto, ON, Canada.
  • Alexandra Leary
    Medical Oncology Department, Gustave Roussy Institute, Villejuif, France.
  • Hans W Nijman
    Department of Obstetrics and Gynecology, University Medical Center Groningen, Groningen, Netherlands.
  • Vincent T H B M Smit
    Department of Pathology, Leiden University Medical Centre, Leiden, The Netherlands.
  • Carien L Creutzberg
    Department of Radiation Oncology, Leiden University Medical Center, Leiden, Netherlands.
  • Nanda Horeweg
    Department of Radiation Oncology, Leiden University Medical Center, Leiden, Netherlands.
  • Viktor H Koelzer
    Institute of Cancer and Genomic Science, University of Birmingham, 6 Mindelsohn Way, Birmingham, B15 2SY, UK. vkoelzer@well.ox.ac.uk.
  • Tjalling Bosse
    Department of Pathology, Leiden University Medical Center, Leiden, Netherlands. Electronic address: t.bosse@lumc.nl.

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