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@ARTICLE{Keyl:298360,
      author       = {J. Keyl and P. Keyl and G. Montavon and R. Hosch and A.
                      Brehmer and L. Mochmann and P. Jurmeister and G. Dernbach
                      and M. Kim and S. Koitka and S. Bauer$^*$ and N.
                      Bechrakis$^*$ and M. Forsting$^*$ and D. Führer-Sakel and
                      M. Glas$^*$ and V. Grünwald$^*$ and B. Hadaschik$^*$ and J.
                      Haubold and K. Herrmann$^*$ and S. Kasper$^*$ and R. Kimmig
                      and S. Lang and T. Rassaf and A. Roesch$^*$ and D.
                      Schadendorf$^*$ and J. Siveke$^*$ and M. Stuschke$^*$ and U.
                      Sure$^*$ and M. Totzeck and A. Welt and M. Wiesweg$^*$ and
                      H. A. Baba and F. Nensa$^*$ and J. Egger and K.-R. Müller
                      and M. Schuler$^*$ and F. Klauschen$^*$ and J. Kleesiek$^*$},
      title        = {{D}ecoding pan-cancer treatment outcomes using multimodal
                      real-world data and explainable artificial intelligence.},
      journal      = {Nature cancer},
      volume       = {6},
      number       = {2},
      issn         = {2662-1347},
      address      = {London},
      publisher    = {Nature Research},
      reportid     = {DKFZ-2025-00258},
      pages        = {307-322},
      year         = {2025},
      note         = {2025 Feb;6(2):307-322},
      abstract     = {Despite advances in precision oncology, clinical
                      decision-making still relies on limited variables and expert
                      knowledge. To address this limitation, we combined
                      multimodal real-world data and explainable artificial
                      intelligence (xAI) to introduce AI-derived (AID) markers for
                      clinical decision support. We used xAI to decode the outcome
                      of 15,726 patients across 38 solid cancer entities based on
                      350 markers, including clinical records, image-derived body
                      compositions, and mutational tumor profiles. xAI determined
                      the prognostic contribution of each clinical marker at the
                      patient level and identified 114 key markers that accounted
                      for $90\%$ of the neural network's decision process.
                      Moreover, xAI enabled us to uncover 1,373 prognostic
                      interactions between markers. Our approach was validated in
                      an independent cohort of 3,288 patients with lung cancer
                      from a US nationwide electronic health record-derived
                      database. These results show the potential of xAI to
                      transform the assessment of clinical variables and enable
                      personalized, data-driven cancer care.},
      cin          = {ED01 / BE01 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)ED01-20160331 / I:(DE-He78)BE01-20160331 /
                      I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39885364},
      doi          = {10.1038/s43018-024-00891-1},
      url          = {https://inrepo02.dkfz.de/record/298360},
}