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HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer

Author

Listed:
  • Chiara M. L. Loeffler

    (Technical University Dresden
    Technische Universität Dresden
    National Center for Tumor Diseases Dresden (NCT/UCC))

  • Hideaki Bando

    (National Cancer Center Hospital East
    National Cancer Center Hospital East
    National Cancer Center Hospital East)

  • Srividhya Sainath

    (Technical University Dresden)

  • Hannah Sophie Muti

    (Technical University Dresden
    Technische Universität Dresden
    Technische Universität Dresden)

  • Xiaofeng Jiang

    (Technical University Dresden)

  • Marko Treeck

    (Technical University Dresden)

  • Nic Gabriel Reitsam

    (Technical University Dresden
    University of Augsburg
    Bavarian Cancer Research Center (BZKF))

  • Zunamys I. Carrero

    (Technical University Dresden)

  • Asier Rabasco Meneghetti

    (Technical University Dresden)

  • Tomomi Nishikawa

    (National Cancer Center Hospital East)

  • Toshihiro Misumi

    (National Cancer Center Hospital East)

  • Saori Mishima

    (National Cancer Center Hospital East)

  • Daisuke Kotani

    (National Cancer Center Hospital East)

  • Hiroya Taniguchi

    (Aichi Cancer Center Hospital)

  • Ichiro Takemasa

    (Sapporo Medical University)

  • Takeshi Kato

    (NHO Osaka National Hospital)

  • Eiji Oki

    (Kyushu University)

  • Yuan Tanwei

    (German Cancer Research Center (DKFZ))

  • Wankhede Durgesh

    (German Cancer Research Center (DKFZ))

  • Sebastian Foersch

    (University Medical Center Mainz)

  • Hermann Brenner

    (German Cancer Research Center (DKFZ)
    University Hospital Heidelberg)

  • Michael Hoffmeister

    (German Cancer Research Center (DKFZ))

  • Yoshiaki Nakamura

    (National Cancer Center Hospital East
    National Cancer Center Hospital East)

  • Takayuki Yoshino

    (National Cancer Center Hospital East
    National Cancer Center Hospital East
    National Cancer Center Hospital East)

  • Jakob Nikolas Kather

    (Technical University Dresden
    Technische Universität Dresden
    National Center for Tumor Diseases Dresden (NCT/UCC)
    University Hospital Heidelberg)

Abstract

Although surgical resection is the standard therapy for stage II/III colorectal cancer, recurrence rates exceed 30%. Circulating tumor DNA (ctNDA) detects molecular residual disease (MRD), but lacks spatial and tumor microenvironment information. Here, we develop a deep learning (DL) model to predict disease-free survival from hematoxylin & eosin stained whole slide images in stage II-IV colorectal cancer. The model is trained on the DACHS cohort (n = 1766) and validated on the GALAXY cohort (n = 1404). In GALAXY, the DL model categorizes 304 patients as DL high-risk and 1100 as low-risk (HR 2.31; p

Suggested Citation

  • Chiara M. L. Loeffler & Hideaki Bando & Srividhya Sainath & Hannah Sophie Muti & Xiaofeng Jiang & Marko Treeck & Nic Gabriel Reitsam & Zunamys I. Carrero & Asier Rabasco Meneghetti & Tomomi Nishikawa , 2025. "HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62910-8
    DOI: 10.1038/s41467-025-62910-8
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