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@ARTICLE{Sachpekidis:295889,
      author       = {C. Sachpekidis$^*$ and H. Goldschmidt and L. Edenbrandt and
                      A. Dimitrakopoulou-Strauss$^*$},
      title        = {{R}adiomics and {A}rtificial {I}ntelligence {L}andscape for
                      [18{F}]{FDG} {PET}/{CT} in {M}ultiple {M}yeloma.},
      journal      = {Seminars in nuclear medicine},
      volume       = {55},
      number       = {3},
      issn         = {0001-2998},
      address      = {Duluth, Minn.},
      publisher    = {Saunders},
      reportid     = {DKFZ-2024-02704},
      pages        = {387-395},
      year         = {2025},
      note         = {#EA:E060#LA:E060# / 2025 May;55(3):387-395},
      abstract     = {[18F]FDG PET/CT is a powerful imaging modality of high
                      performance in multiple myeloma (MM) and is considered the
                      appropriate method for assessing treatment response in this
                      disease. On the other hand, due to the heterogeneous and
                      sometimes complex patterns of bone marrow infiltration in
                      MM, the interpretation of PET/CT can be particularly
                      challenging, hampering interobserver reproducibility and
                      limiting the diagnostic and prognostic ability of the
                      modality. Although many approaches have been developed to
                      address the issue of standardization, none can yet be
                      considered a standard method for interpretation or objective
                      quantification of PET/CT. Therefore, advanced diagnostic
                      quantification approaches are needed to support and
                      potentially guide the management of MM. In recent years,
                      radiomics has emerged as an innovative method for
                      high-throughput mining of image-derived features for
                      clinical decision making, which may be particularly helpful
                      in oncology. In addition, machine learning and deep
                      learning, both subfields of artificial intelligence (AI)
                      closely related to the radiomics process, have been
                      increasingly applied to automated image analysis, offering
                      new possibilities for a standardized evaluation of imaging
                      modalities such as CT, PET/CT and MRI in oncology. In line
                      with this, the initial but steadily growing literature on
                      the application of radiomics and AI-based methods in the
                      field of [18F]FDG PET/CT in MM has already yielded
                      encouraging results, offering a potentially reliable tool
                      towards optimization and standardization of interpretation
                      in this disease. The main results of these studies are
                      presented in this review.},
      subtyp        = {Review Article},
      cin          = {E060},
      ddc          = {610},
      cid          = {I:(DE-He78)E060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39674756},
      doi          = {10.1053/j.semnuclmed.2024.11.005},
      url          = {https://inrepo02.dkfz.de/record/295889},
}