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AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

Author

Listed:
  • Amirali Darbandsari

    (University of British Columbia)

  • Hossein Farahani

    (University of British Columbia
    University of British Columbia)

  • Maryam Asadi

    (University of British Columbia)

  • Matthew Wiens

    (University of British Columbia)

  • Dawn Cochrane

    (British Columbia Cancer Research Institute)

  • Ali Khajegili Mirabadi

    (University of British Columbia)

  • Amy Jamieson

    (University of British Columbia)

  • David Farnell

    (University of British Columbia
    Vancouver General Hospital)

  • Pouya Ahmadvand

    (University of British Columbia)

  • Maxwell Douglas

    (British Columbia Cancer Research Institute)

  • Samuel Leung

    (British Columbia Cancer Research Institute)

  • Purang Abolmaesumi

    (University of British Columbia)

  • Steven J. M. Jones

    (British Columbia Cancer Research Center)

  • Aline Talhouk

    (University of British Columbia)

  • Stefan Kommoss

    (Tübingen University Hospital)

  • C. Blake Gilks

    (University of British Columbia
    Vancouver General Hospital)

  • David G. Huntsman

    (University of British Columbia
    British Columbia Cancer Research Institute)

  • Naveena Singh

    (University of British Columbia
    Vancouver General Hospital)

  • Jessica N. McAlpine

    (University of British Columbia)

  • Ali Bashashati

    (University of British Columbia
    University of British Columbia)

Abstract

Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed ‘p53abn-like NSMP’), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the ‘p53abn-like NSMP’ group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study’s findings are applicable exclusively to females.

Suggested Citation

  • Amirali Darbandsari & Hossein Farahani & Maryam Asadi & Matthew Wiens & Dawn Cochrane & Ali Khajegili Mirabadi & Amy Jamieson & David Farnell & Pouya Ahmadvand & Maxwell Douglas & Samuel Leung & Puran, 2024. "AI-based histopathology image analysis reveals a distinct subset of endometrial cancers," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49017-2
    DOI: 10.1038/s41467-024-49017-2
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    References listed on IDEAS

    as
    1. Douglas A. Levine, 2013. "Integrated genomic characterization of endometrial carcinoma," Nature, Nature, vol. 497(7447), pages 67-73, May.
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