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@ARTICLE{Kernbach:917546,
      author       = {Kernbach, Julius M and Delev, Daniel and Neuloh, Georg and
                      Clusmann, Hans and Bzdok, Danilo and Eickhoff, Simon B and
                      Staartjes, Victor E and Vasella, Flavio and Weller, Michael
                      and Regli, Luca and Serra, Carlo and Krayenbühl, Niklaus
                      and Akeret, Kevin},
      title        = {{M}eta-topologies define distinct anatomical classes of
                      brain tumours linked to histology and survival},
      journal      = {Brain communications},
      volume       = {5},
      number       = {1},
      issn         = {2632-1297},
      address      = {[Großbritannien]},
      publisher    = {Guarantors of Brain},
      reportid     = {FZJ-2023-00749},
      pages        = {fcac336},
      year         = {2023},
      abstract     = {The current World Health Organization classification
                      integrates histological and molecular features of brain
                      tumours. The aim of this study was to identify generalizable
                      topological patterns with the potential to add an anatomical
                      dimension to the classification of brain tumours. We applied
                      non-negative matrix factorization as an unsupervised pattern
                      discovery strategy to the fine-grained topographic tumour
                      profiles of 936 patients with neuroepithelial tumours and
                      brain metastases. From the anatomical features alone, this
                      machine learning algorithm enabled the extraction of latent
                      topological tumour patterns, termed meta-topologies. The
                      optimal part-based representation was automatically
                      determined in 10 000 split-half iterations. We further
                      characterized each meta-topology's unique histopathologic
                      profile and survival probability, thus linking important
                      biological and clinical information to the underlying
                      anatomical patterns. In neuroepithelial tumours, six
                      meta-topologies were extracted, each detailing a
                      transpallial pattern with distinct parenchymal and
                      ventricular compositions. We identified one infratentorial,
                      one allopallial, three neopallial (parieto-occipital,
                      frontal, temporal) and one unisegmental meta-topology. Each
                      meta-topology mapped to distinct histopathologic and
                      molecular profiles. The unisegmental meta-topology showed
                      the strongest anatomical-clinical link demonstrating a
                      survival advantage in histologically identical tumours.
                      Brain metastases separated to an infra- and supratentorial
                      meta-topology with anatomical patterns highlighting their
                      affinity to the cortico-subcortical boundary of arterial
                      watershed areas.Using a novel data-driven approach, we
                      identified generalizable topological patterns in both
                      neuroepithelial tumours and brain metastases. Differences in
                      the histopathologic profiles and prognosis of these
                      anatomical tumour classes provide insights into the
                      heterogeneity of tumour biology and might add to
                      personalized clinical decision-making.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36632188},
      UT           = {WOS:000911103200004},
      doi          = {10.1093/braincomms/fcac336},
      url          = {https://juser.fz-juelich.de/record/917546},
}