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@ARTICLE{Eckardt:276101,
      author       = {J.-N. Eckardt and C. Röllig and K. Metzeler and P. Heisig
                      and S. Stasik and J.-A. Georgi and F. Kroschinsky and F.
                      Stölzel and U. Platzbecker and K. Spiekermann and U. Krug
                      and J. Braess and D. Görlich and C. Sauerland and B.
                      Woermann and T. Herold and W. Hiddemann and C.
                      Müller-Tidow$^*$ and H. Serve and C. D. Baldus and K.
                      Schäfer-Eckart and M. Kaufmann and S. W. Krause and M.
                      Hänel and W. E. Berdel and C. Schliemann and J. Mayer and
                      M. Hanoun and J. Schetelig and K. Wendt and M.
                      Bornhäuser$^*$ and C. Thiede and J. M. Middeke},
      title        = {{U}nsupervised meta-clustering identifies risk clusters in
                      acute myeloid leukemia based on clinical and genetic
                      profiles.},
      journal      = {Communications medicine},
      volume       = {3},
      number       = {1},
      issn         = {2730-664X},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2023-01001},
      pages        = {68},
      year         = {2023},
      abstract     = {Increasingly large and complex biomedical data sets
                      challenge conventional hypothesis-driven analytical
                      approaches, however, data-driven unsupervised learning can
                      detect inherent patterns in such data sets.While
                      unsupervised analysis in the medical literature commonly
                      only utilizes a single clustering algorithm for a given data
                      set, we developed a large-scale model with 605 different
                      combinations of target dimensionalities as well as
                      transformation and clustering algorithms and subsequent
                      meta-clustering of individual results. With this model, we
                      investigated a large cohort of 1383 patients from 59 centers
                      in Germany with newly diagnosed acute myeloid leukemia for
                      whom 212 clinical, laboratory, cytogenetic and molecular
                      genetic parameters were available.Unsupervised learning
                      identifies four distinct patient clusters, and statistical
                      analysis shows significant differences in rate of complete
                      remissions, event-free, relapse-free and overall survival
                      between the four clusters. In comparison to the
                      standard-of-care hypothesis-driven European Leukemia Net
                      (ELN2017) risk stratification model, we find all three
                      ELN2017 risk categories being represented in all four
                      clusters in varying proportions indicating unappreciated
                      complexity of AML biology in current established risk
                      stratification models. Further, by using assigned clusters
                      as labels we subsequently train a supervised model to
                      validate cluster assignments on a large external multicenter
                      cohort of 664 intensively treated AML patients.Dynamic
                      data-driven models are likely more suitable for risk
                      stratification in the context of increasingly complex
                      medical data than rigid hypothesis-driven models to allow
                      for a more personalized treatment allocation and gain novel
                      insights into disease biology.},
      subtyp        = {Editorial},
      cin          = {DD01 / HD01},
      cid          = {I:(DE-He78)DD01-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37198246},
      doi          = {10.1038/s43856-023-00298-6},
      url          = {https://inrepo02.dkfz.de/record/276101},
}