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@ARTICLE{ElNahhas:288058,
      author       = {O. S. M. El Nahhas and C. M. L. Loeffler and Z. I. Carrero
                      and M. van Treeck and F. R. Kolbinger and K. J. Hewitt and
                      H. S. Muti and M. Graziani and Q. Zeng and J. Calderaro and
                      N. Ortiz-Brüchle and T. Yuan$^*$ and M. Hoffmeister$^*$ and
                      H. Brenner$^*$ and A. Brobeil and J. S. Reis-Filho and J. N.
                      Kather},
      title        = {{R}egression-based {D}eep-{L}earning predicts molecular
                      biomarkers from pathology slides.},
      journal      = {Nature Communications},
      volume       = {15},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {DKFZ-2024-00316},
      pages        = {1253},
      year         = {2024},
      abstract     = {Deep Learning (DL) can predict biomarkers from cancer
                      histopathology. Several clinically approved applications use
                      this technology. Most approaches, however, predict
                      categorical labels, whereas biomarkers are often continuous
                      measurements. We hypothesize that regression-based DL
                      outperforms classification-based DL. Therefore, we develop
                      and evaluate a self-supervised attention-based weakly
                      supervised regression method that predicts continuous
                      biomarkers directly from 11,671 images of patients across
                      nine cancer types. We test our method for multiple
                      clinically and biologically relevant biomarkers: homologous
                      recombination deficiency score, a clinically used pan-cancer
                      biomarker, as well as markers of key biological processes in
                      the tumor microenvironment. Using regression significantly
                      enhances the accuracy of biomarker prediction, while also
                      improving the predictions' correspondence to regions of
                      known clinical relevance over classification. In a large
                      cohort of colorectal cancer patients, regression-based
                      prediction scores provide a higher prognostic value than
                      classification-based scores. Our open-source regression
                      approach offers a promising alternative for continuous
                      biomarker analysis in computational pathology.},
      cin          = {C070 / C120 / HD01},
      ddc          = {500},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)HD01-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38341402},
      pmc          = {pmc:PMC10858881},
      doi          = {10.1038/s41467-024-45589-1},
      url          = {https://inrepo02.dkfz.de/record/288058},
}