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@ARTICLE{Katiyar:276425,
      author       = {P. Katiyar and J. Schwenck and L. Frauenfeld and M. R.
                      Divine and V. Agrawal and U. Kohlhofer and S. Gatidis and R.
                      Kontermann and A. Königsrainer and L. Quintanilla-Martinez
                      and C. la Fougère$^*$ and B. Schölkopf and B. Pichler$^*$
                      and J. A. Disselhorst},
      title        = {{Q}uantification of intratumoural heterogeneity in mice and
                      patients via machine-learning models trained on {PET}-{MRI}
                      data.},
      journal      = {Nature biomedical engineering},
      volume       = {7},
      number       = {8},
      issn         = {2157-846X},
      address      = {Tokyo},
      publisher    = {Nature Research},
      reportid     = {DKFZ-2023-01108},
      pages        = {1014-1027},
      year         = {2023},
      note         = {2023 Aug;7(8):1014-1027},
      abstract     = {In oncology, intratumoural heterogeneity is closely linked
                      with the efficacy of therapy, and can be partially
                      characterized via tumour biopsies. Here we show that
                      intratumoural heterogeneity can be characterized spatially
                      via phenotype-specific, multi-view learning classifiers
                      trained with data from dynamic positron emission tomography
                      (PET) and multiparametric magnetic resonance imaging (MRI).
                      Classifiers trained with PET-MRI data from mice with
                      subcutaneous colon cancer quantified phenotypic changes
                      resulting from an apoptosis-inducing targeted therapeutic
                      and provided biologically relevant probability maps of
                      tumour-tissue subtypes. When applied to retrospective
                      PET-MRI data of patients with liver metastases from
                      colorectal cancer, the trained classifiers characterized
                      intratumoural tissue subregions in agreement with tumour
                      histology. The spatial characterization of intratumoural
                      heterogeneity in mice and patients via multimodal,
                      multiparametric imaging aided by machine-learning may
                      facilitate applications in precision oncology.},
      cin          = {TU01},
      ddc          = {610},
      cid          = {I:(DE-He78)TU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37277483},
      doi          = {10.1038/s41551-023-01047-9},
      url          = {https://inrepo02.dkfz.de/record/276425},
}