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@MISC{Lohmann:908211,
      author       = {Lohmann, P. and Elahmadawy, M. A. and Werner, J. and Rapp,
                      M. and Ceccon, G. and Fink, G. R. and Shah, N. J. and
                      Langen, K. and Galldiks, N.},
      title        = {{OS}9.6 {D}iagnosis of pseudoprogression using {FET} {PET}
                      radiomics},
      issn         = {1523-5866},
      reportid     = {FZJ-2022-02462},
      year         = {2019},
      abstract     = {AbstractBACKGROUNDRadiomics derived from different imaging
                      modalities is gaining increasing interest in the field of
                      neuro-oncology. Besides MRI, amino acid PET radiomics may
                      also improve the to date challenging, clinically relevant
                      diagnostic problem of differentiating pseudoprogression
                      (PsP) from tumor progression (TP). To this end, we here
                      explored the potential of O-(2-[18F]fluoroethyl)-L-tyrosine
                      (FET) PET radiomics to discriminate between PsP and
                      TP.MATERIAL AND METHODSThirty-five newly diagnosed
                      IDH-wildtype glioblastoma patients with MRI findings
                      suspicious for TP within 12 weeks after completion of
                      chemoradiation with temozolomide underwent an additional
                      dynamic FET PET scan. FET PET tumor volumes were segmented
                      using a tumor-to-brain ratio (TBR) ≥ 1.6. The static PET
                      parameters TBRmax and TBRmean, as well as the dynamic
                      parameter time-to-peak (TTP), were calculated. For radiomics
                      analysis, the number of datasets for model generation was
                      increased using data augmentation techniques. Subsequently,
                      70 datasets were available for model generation. Prior to
                      further processing, patients were randomly assigned to a
                      discovery and a validation dataset in a ratio of 70/30, with
                      balanced distribution of PsP and TP diagnoses. Forty-two
                      radiomics features (4 shape-based, 6 first- and 32
                      second-order features) were obtained using the software
                      LifeX (lifexsoft.org). Afterwards, a z-score transformation
                      was performed for data normalization. For feature selection,
                      recursive feature elimination using random forest regressors
                      was performed. For the final model generation, the number of
                      parameters was limited to three to avoid data overfitting.
                      Different algorithms for model calculation were compared,
                      and the diagnostic accuracy was assessed using leave-one-out
                      cross-validation. Finally, the resulting models were applied
                      to the validation dataset to evaluate model
                      robustness.RESULTSEighteen patients were diagnosed with TP,
                      and 17 patients had PsP. Diagnoses were based on a
                      neuropathological confirmation or clinicoradiological
                      follow-up $(26\%$ and $74\%,$ respectively). The diagnostic
                      accuracy of the best single FET PET parameter was $75\%$
                      (TBRmax). Combining TBRmax and TTP increased the diagnostic
                      accuracy to $83\%.$ Other combinations of static and dynamic
                      FET PET parameters, however, did not further increase the
                      accuracy. The highest diagnostic accuracy of $92\%$ was
                      achieved by a three-parameter model combining the FET PET
                      parameter TTP with two radiomics features. The model
                      demonstrated its robustness in the validation dataset with a
                      diagnostic accuracy of $86\%.CONCLUSIONThe$ results suggest
                      that FET PET radiomics improves the diagnostic accuracy for
                      discerning PsP and TP considerably. Given the clinical
                      significance of differentiating PSP and TP, prospective
                      multicenter studies are warranted.FUNDINGWilhelm-Sander
                      Stiftung and the DAAD GERSS Program, Germany},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)VDB1046},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)4},
      UT           = {WOS:000493085900066},
      doi          = {10.1093/neuonc/noz126.064},
      url          = {https://juser.fz-juelich.de/record/908211},
}