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@ARTICLE{Kaiser:270296,
      author       = {Kaiser, Lena and Quach, S. and Zounek, A. J. and Wiestler,
                      B. and Zatcepin, A. and Holzgreve, A. and Bollenbacher, A.
                      and Bartos, L. M. and Ruf, V. C. and Böning, G. and Thon,
                      N. and Herms, J. and Riemenschneider, M. J. and Stöcklein,
                      S. and Brendel, M. and Rupprecht, R. and Tonn, J. C. and
                      Bartenstein, P. and von Baumgarten, L. and Ziegler, S. and
                      Albert, N. L.},
      title        = {{E}nhancing predictability of {IDH} mutation status in
                      glioma patients at initial diagnosis: a comparative analysis
                      of radiomics from {MRI}, [18{F}]{FET} {PET}, and {TSPO}
                      {PET}.},
      journal      = {European journal of nuclear medicine and molecular imaging},
      volume       = {51},
      number       = {8},
      issn         = {1619-7070},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer-Verl.},
      reportid     = {DZNE-2024-00768},
      pages        = {2371 - 2381},
      year         = {2024},
      abstract     = {According to the World Health Organization classification
                      for tumors of the central nervous system, mutation status of
                      the isocitrate dehydrogenase (IDH) genes has become a major
                      diagnostic discriminator for gliomas. Therefore,
                      imaging-based prediction of IDH mutation status is of high
                      interest for individual patient management. We compared and
                      evaluated the diagnostic value of radiomics derived from
                      dual positron emission tomography (PET) and magnetic
                      resonance imaging (MRI) data to predict the IDH mutation
                      status non-invasively.Eighty-seven glioma patients at
                      initial diagnosis who underwent PET targeting the
                      translocator protein (TSPO) using [18F]GE-180, dynamic amino
                      acid PET using [18F]FET, and T1-/T2-weighted MRI scans were
                      examined. In addition to calculating tumor-to-background
                      ratio (TBR) images for all modalities, parametric images
                      quantifying dynamic [18F]FET PET information were generated.
                      Radiomic features were extracted from TBR and parametric
                      images. The area under the receiver operating characteristic
                      curve (AUC) was employed to assess the performance of
                      logistic regression (LR) classifiers. To report robust
                      estimates, nested cross-validation with five folds and 50
                      repeats was applied.TBRGE-180 features extracted from
                      TSPO-positive volumes had the highest predictive power among
                      TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic
                      [18F]FET PET reached a similarly high performance (0.94,
                      with age 0.96). The highest LR coefficients in multimodal
                      analyses included TBRGE-180 features, parameters from
                      kinetic and early static [18F]FET PET images, age, and the
                      features from TBRT2 images such as the kurtosis (0.97).The
                      findings suggest that incorporating TBRGE-180 features along
                      with kinetic information from dynamic [18F]FET PET, kurtosis
                      from TBRT2, and age can yield very high predictability of
                      IDH mutation status, thus potentially improving early
                      patient management.},
      keywords     = {Humans / Female / Receptors, GABA: genetics / Receptors,
                      GABA: metabolism / Male / Middle Aged / Isocitrate
                      Dehydrogenase: genetics / Mutation / Positron-Emission
                      Tomography: methods / Glioma: diagnostic imaging / Glioma:
                      genetics / Magnetic Resonance Imaging / Adult / Brain
                      Neoplasms: diagnostic imaging / Brain Neoplasms: genetics /
                      Aged / Tyrosine: analogs $\&$ derivatives / Image
                      Processing, Computer-Assisted / Radiomics / IDH mutation
                      status (Other) / BraTS (Other) / FET PET (Other) / Glioma
                      (Other) / Radiomics (Other) / TSPO PET (Other) / Receptors,
                      GABA (NLM Chemicals) / Isocitrate Dehydrogenase (NLM
                      Chemicals) / TSPO protein, human (NLM Chemicals) /
                      (18F)fluoroethyltyrosine (NLM Chemicals) / Tyrosine (NLM
                      Chemicals)},
      cin          = {AG Haass},
      ddc          = {610},
      cid          = {I:(DE-2719)1110007},
      pnm          = {352 - Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-352},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38396261},
      pmc          = {pmc:PMC11178656},
      doi          = {10.1007/s00259-024-06654-5},
      url          = {https://pub.dzne.de/record/270296},
}