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@MISC{Lohmann:908215,
      author       = {Lohmann, Philipp and Meissner, Anna-Katharina and Werner,
                      Jan-Michael and Stoffels, Gabriele and Kocher, Martin and
                      Bauer, Elena and Fink, Gereon and Shah, Nadim and Langen,
                      Karl-Josef and Galldiks, Norbert},
      title        = {{NIMG}-38. {NON}-{INVASIVE} {PREDICTION} {OF} {MGMT}
                      {PROMOTER} {METHYLATION} {USING} {COMBINED} {FET}
                      {PET}/{MRI} {RADIOMICS}},
      reportid     = {FZJ-2022-02466},
      year         = {2020},
      abstract     = {BACKGROUNDRecently, the Response Assessment in
                      Neuro-Oncology (RANO) Working Group emphasized the
                      additional diagnostic value of amino acid PET in addition to
                      MRI. However, the number of studies using amino acid PET/MRI
                      radiomics is still low. We investigated the potential of
                      combined O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET/MRI
                      radiomics for the non-invasive prediction of the
                      O6-methylguanine-DNA methyl-transferase (MGMT) promoter
                      methylation status in glioma patients.METHODSSeventy-one
                      patients with newly diagnosed glioma (predominantly WHO
                      grade III and IV glioma, $82\%)$ underwent a hybrid FET
                      PET/MRI scan. Forty-six patients $(65\%)$ had a methylated
                      MGMT promoter. The tumor and tumor subregions were manually
                      segmented on conventional MRI. In total, 199 standardized
                      features were obtained from FET PET, contrast-enhanced
                      T1-weighted, T2-weighted, and fluid attenuated inversion
                      recovery (FLAIR) MRI. After feature extraction and data
                      normalization, patients were randomly assigned to a training
                      and a test dataset for final model evaluation in a ratio of
                      70/30, with a balanced distribution of the MGMT promoter
                      methylation status. Feature selection was performed by
                      recursive feature elimination using random forest
                      regressors. For the final model generation, the number of
                      features was limited to seven to avoid data overfitting.
                      Different algorithms for model generation were compared, and
                      the model performance in the training data was assessed by
                      5-fold cross-validation. Finally, the best performing models
                      were applied to the test dataset to evaluate the robustness
                      of the models.RESULTSIn the test dataset, the best radiomics
                      signatures obtained from MRI or FET PET alone achieved
                      diagnostic accuracies for the prediction of the MGMT
                      promoter methylation of $64\%$ and $70\%,$ respectively. In
                      contrast, the highest diagnostic accuracy of $83\%$ was
                      obtained by combining FET PET and MRI
                      features.CONCLUSIONCombined FET PET/MRI radiomics allows the
                      non-invasive prediction of the MGMT promoter methylation
                      status in patients with gliomas, providing more diagnostic
                      information than either modality alone.},
      cin          = {INM-11 / INM-4 / JARA-BRAIN / INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-11-20170113 / I:(DE-Juel1)INM-4-20090406 /
                      I:(DE-Juel1)VDB1046 / I:(DE-Juel1)INM-3-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)4},
      url          = {https://juser.fz-juelich.de/record/908215},
}