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Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides

Author

Listed:
  • I. Garberis

    (Paris-Saclay University)

  • V. Gaury

    (Owkin)

  • C. Saillard

    (Owkin)

  • D. Drubay

    (Université Paris-Saclay
    labeled Ligue Contre le Cancer)

  • K. Elgui

    (Owkin)

  • B. Schmauch

    (Owkin)

  • A. Jaeger

    (Owkin)

  • L. Herpin

    (Owkin)

  • J. Linhart

    (Owkin)

  • M. Sapateiro

    (Paris-Saclay University)

  • F. Bernigole

    (Paris-Saclay University)

  • A. Kamoun

    (Owkin)

  • A. Filiot

    (Owkin)

  • O. Tchita

    (Owkin)

  • R. Dubois

    (Owkin)

  • M. Auffret

    (Owkin)

  • L. Guillou

    (Owkin)

  • I. Bousaid

    (Gustave Roussy)

  • M. Azoulay

    (Gustave Roussy)

  • J. Lemonnier

    (Unicancer)

  • M. Sefta

    (Owkin)

  • S. Everhard

    (Unicancer)

  • A. Sarrazin

    (Owkin)

  • J-F Reboud

    (Owkin)

  • F. Brulport

    (Owkin)

  • J. Dachary

    (Owkin)

  • B. Pistilli

    (Paris-Saclay University)

  • S. Delaloge

    (Paris-Saclay University)

  • P. Courtiol

    (Owkin)

  • F. André

    (Paris-Saclay University
    Paris-Saclay University)

  • V. Aubert

    (Owkin)

  • M. Lacroix-Triki

    (Paris-Saclay University)

Abstract

Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients with estrogen receptor-positive, HER2-negative (ER + /HER2 − ) early breast cancer (EBC). Our deep learning model, RlapsRisk BC, independently predicts MFS and provides significant prognostic value beyond traditional clinico-pathological variables (C-index 0.81 vs 0.76, p

Suggested Citation

  • I. Garberis & V. Gaury & C. Saillard & D. Drubay & K. Elgui & B. Schmauch & A. Jaeger & L. Herpin & J. Linhart & M. Sapateiro & F. Bernigole & A. Kamoun & A. Filiot & O. Tchita & R. Dubois & M. Auffre, 2025. "Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60824-z
    DOI: 10.1038/s41467-025-60824-z
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    References listed on IDEAS

    as
    1. Rumiko Tashima & Reiki Nishimura & Tomofumi Osako & Yasuyuki Nishiyama & Yasuhiro Okumura & Masahiro Nakano & Mamiko Fujisue & Yasuo Toyozumi & Nobuyuki Arima, 2015. "Evaluation of an Optimal Cut-Off Point for the Ki-67 Index as a Prognostic Factor in Primary Breast Cancer: A Retrospective Study," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-10, July.
    2. Kevin M. Boehm & Omar S. M. El Nahhas & Antonio Marra & Michele Waters & Justin Jee & Lior Braunstein & Nikolaus Schultz & Pier Selenica & Hannah Y. Wen & Britta Weigelt & Evan D. Paul & Pavol Cekan &, 2025. "Multimodal histopathologic models stratify hormone receptor-positive early breast cancer," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    3. Uno, Hajime & Cai, Tianxi & Tian, Lu & Wei, L.J., 2007. "Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 527-537, June.
    4. Mélanie Roschewitz & Galvin Khara & Joe Yearsley & Nisha Sharma & Jonathan J. James & Éva Ambrózay & Adam Heroux & Peter Kecskemethy & Tobias Rijken & Ben Glocker, 2023. "Automatic correction of performance drift under acquisition shift in medical image classification," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
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