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@ARTICLE{Lohmann:1018477,
      author       = {Lohmann, Philipp and Bundschuh, Ralph Alexander and
                      Miederer, Isabelle and Mottaghy, Felix M. and Langen, Karl
                      Josef and Galldiks, Norbert},
      title        = {{C}linical {A}pplications of {R}adiomics in {N}uclear
                      {M}edicine},
      journal      = {Nuklearmedizin},
      volume       = {62},
      number       = {06},
      issn         = {0029-5566},
      address      = {Stuttgart},
      publisher    = {Thieme},
      reportid     = {FZJ-2023-04836},
      pages        = {354 - 360},
      year         = {2023},
      abstract     = {Radiomics is an emerging field of artificial intelligence
                      that focuses on the extraction and analysis of quantitative
                      features such as intensity, shape, texture and spatial
                      relationships from medical images. These features, often
                      imperceptible to the human eye, can reveal complex patterns
                      and biological insights. They can also be combined with
                      clinical data to create predictive models using machine
                      learning to improve disease characterization in nuclear
                      medicine. This review article examines the current state of
                      radiomics in nuclear medicine and shows its potential to
                      improve patient care. Selected clinical applications for
                      diseases such as cancer, neurodegenerative diseases,
                      cardiovascular problems and thyroid diseases are examined.
                      The article concludes with a brief classification in terms
                      of future perspectives and strategies for linking research
                      findings to clinical practice.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37935406},
      UT           = {WOS:001100844500001},
      doi          = {10.1055/a-2191-3271},
      url          = {https://juser.fz-juelich.de/record/1018477},
}