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@ARTICLE{Schwenck:276369,
      author       = {J. Schwenck and D. Sonanini and J. M. Cotton and H.-G.
                      Rammensee$^*$ and C. la Fougère$^*$ and L. Zender$^*$ and
                      B. Pichler$^*$},
      title        = {{A}dvances in {PET} imaging of cancer.},
      journal      = {Nature reviews / Cancer},
      volume       = {23},
      number       = {7},
      issn         = {1474-175X},
      address      = {London [u.a.]},
      publisher    = {Nature Publ. Group},
      reportid     = {DKFZ-2023-01078},
      pages        = {474-490},
      year         = {2023},
      note         = {2023 Jul;23(7):474-490},
      abstract     = {Molecular imaging has experienced enormous advancements in
                      the areas of imaging technology, imaging probe and contrast
                      development, and data quality, as well as machine
                      learning-based data analysis. Positron emission tomography
                      (PET) and its combination with computed tomography (CT) or
                      magnetic resonance imaging (MRI) as a multimodality PET-CT
                      or PET-MRI system offer a wealth of molecular, functional
                      and morphological data with a single patient scan. Despite
                      the recent technical advances and the availability of dozens
                      of disease-specific contrast and imaging probes, only a few
                      parameters, such as tumour size or the mean tracer uptake,
                      are used for the evaluation of images in clinical practice.
                      Multiparametric in vivo imaging data not only are highly
                      quantitative but also can provide invaluable information
                      about pathophysiology, receptor expression, metabolism, or
                      morphological and functional features of tumours, such as
                      pH, oxygenation or tissue density, as well as
                      pharmacodynamic properties of drugs, to measure drug
                      response with a contrast agent. It can further
                      quantitatively map and spatially resolve the intertumoural
                      and intratumoural heterogeneity, providing insights into
                      tumour vulnerabilities for target-specific therapeutic
                      interventions. Failure to exploit and integrate the full
                      potential of such powerful imaging data may lead to a lost
                      opportunity in which patients do not receive the best
                      possible care. With the desire to implement personalized
                      medicine in the cancer clinic, the full comprehensive
                      diagnostic power of multiplexed imaging should be utilized.},
      subtyp        = {Review Article},
      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:37258875},
      doi          = {10.1038/s41568-023-00576-4},
      url          = {https://inrepo02.dkfz.de/record/276369},
}