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@ARTICLE{Loeffler:303650,
      author       = {C. M. L. Loeffler and H. Bando and S. Sainath and H. S.
                      Muti and X. Jiang and M. van Treeck and N. G. Reitsam and Z.
                      I. Carrero and A. R. Meneghetti and T. Nishikawa and T.
                      Misumi and S. Mishima and D. Kotani and H. Taniguchi and I.
                      Takemasa and T. Kato and E. Oki and Y. Tanwei$^*$ and W.
                      Durgesh$^*$ and S. Foersch and H. Brenner$^*$ and M.
                      Hoffmeister$^*$ and Y. Nakamura and T. Yoshino and J. N.
                      Kather},
      title        = {{HIBRID}: histology-based risk-stratification with deep
                      learning and ct{DNA} in colorectal cancer.},
      journal      = {Nature Communications},
      volume       = {16},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2025-01714},
      pages        = {7561},
      year         = {2025},
      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 < 0.005).
                      Combining DL scores with MRD status improves prognostic
                      stratification in both MRD-positive (HR 1.58; p < 0.005) and
                      MRD-negative groups (HR 2.1; p < 0.005). Notably,
                      MRD-negative patients predicted as DL high-risk benefit from
                      adjuvant chemotherapy (HR 0.49; p = 0.01) vs. DL low-risk
                      (HR = 0.92; p = 0.64). Combining ctDNA with DL-based
                      histology analysis significantly improves risk
                      stratification, with the potential to improve follow-up and
                      personalized adjuvant therapy decisions.},
      cin          = {C070},
      ddc          = {500},
      cid          = {I:(DE-He78)C070-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40813777},
      pmc          = {pmc:PMC12354865},
      doi          = {10.1038/s41467-025-62910-8},
      url          = {https://inrepo02.dkfz.de/record/303650},
}