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@ARTICLE{Reitsam:310011,
      author       = {N. G. Reitsam and X. Jiang and J. Liang and B. Grosser and
                      V. Grozdanov and C. M. Loeffler and M. Gustav and T. Lenz
                      and H. S. Muti and Z. I. Carrero and N. P. West and P.
                      Quirke and S. Foersch and M. Jesinghaus and W. Müller and
                      T. Yuan$^*$ and M. Hoffmeister$^*$ and H. Brenner$^*$ and J.
                      Jonnagaddala and N. J. Hawkins and R. L. Ward and H. I.
                      Grabsch and B. Märkl and J. N. Kather},
      title        = {{D}eep learning-based ${H}\&{E}-derived$ risk scores in
                      colorectal cancer: associations with tumour morphology,
                      biology, and predicted drug response.},
      journal      = {The journal of pathology},
      volume       = {nn},
      issn         = {0368-3494},
      address      = {Bognor Regis [u.a.]},
      publisher    = {Wiley},
      reportid     = {DKFZ-2026-00424},
      pages        = {nn},
      year         = {2026},
      note         = {epub},
      abstract     = {Over recent years, several deep learning (DL) models have
                      been presented to predict colorectal cancer (CRC) patient
                      survival directly from haematoxylin and eosin
                      $(H\&E)-stained$ routine whole-slide images (WSIs). Unlike
                      traditional studies that rely on manually defined
                      histopathological features, weakly supervised DL allows
                      training directly on clinical endpoints without prior
                      specification of the model's focus. This offers a unique
                      opportunity to study the tissue morphology underlying these
                      predictions, improving our understanding of disease biology.
                      Here, we present a comprehensive analysis of the
                      clinicopathological features, tumour morphology and biology,
                      as well as gene expression-based predicted drug response of
                      over 4,000 CRC patients derived from four different
                      international cohorts with available $H\&E-inferred$
                      DL-based risk scores (low- versus high-risk as well as
                      absolute risk scores). The results from our study suggest
                      that conventional clinicopathological risk factors, such as
                      grade of differentiation, presence of lymph node metastasis,
                      tumour budding, and percentage of tumour necrosis, are
                      positively associated with DL-based risk scores. Moreover,
                      CRCs with direct tumour-adipocyte interactions are enriched
                      in the DL-based high-risk group. Through detailed
                      morphologic review, we provide comprehensive evidence that
                      direct tumour-adipocyte interaction, a high degree of tumour
                      budding, and poorly differentiated morphology are linked to
                      high DL-based risk scores. Transcriptomic and genetic
                      subgroups show only limited association with $H\&E-derived$
                      DL-based risk scores. Moreover, we present data suggesting
                      that DL-based low- versus high-risk CRCs may be
                      characterised by differential drug sensitivity. Our study
                      highlights that DL-based risk scores derived from $H\&E$
                      WSIs not only align with established clinicopathological
                      features but also highlight morphological features, such as
                      tumour-adipocyte interaction, that are not routinely
                      captured by established clinicopathological scoring systems.
                      Moreover, DL-based risk groups may be associated with a
                      differential treatment response, underlining their potential
                      to guide patient stratification in routine clinical
                      practice. © 2026 The Author(s). The Journal of Pathology
                      published by John Wiley $\&$ Sons Ltd on behalf of The
                      Pathological Society of Great Britain and Ireland.},
      keywords     = {biomarker (Other) / colorectal cancer (Other) /
                      computational pathology (Other) / deep learning (Other) /
                      drug response (Other) / gene expression (Other) /
                      histopathology (Other) / pathology (Other) / whole‐slide
                      image (Other)},
      cin          = {C070 / M320},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)M320-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41716034},
      doi          = {10.1002/path.70039},
      url          = {https://inrepo02.dkfz.de/record/310011},
}