% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@MISC{Lohmann:908195,
      author       = {Lohmann, P. and Lerche, C. and Stoffels, G. and Filss, C.
                      P. and Stegmayr, C. and Neumaier, B. and Shah, N. J. and
                      Langen, K. and Galldiks, N.},
      title        = {{P}09.26 {FET} {PET} radiomics - diagnosis of
                      pseudoprogression in glioblastoma patients based on textural
                      features},
      issn         = {1523-5866},
      reportid     = {FZJ-2022-02448},
      year         = {2017},
      abstract     = {AbstractIntroduction: The differentiation of
                      pseudoprogression (PsP) from tumor progression (TP) in
                      glioblastoma patients is difficult on the basis of standard
                      MRI alone. Textural feature analysis as part of the concept
                      of radiomics offers a quantitative method to describe tumor
                      heterogeneity and gains increasing interest in the field of
                      neuro-oncology. In our study, we investigated the potential
                      of textural features of O-(2-[18F]fluoroethyl)-L-tyrosine
                      (FET) PET to discriminate between PsP and TP in glioblastoma
                      patients. Materials and Methods: Twenty-three newly
                      diagnosed glioblastoma patients with MRI findings suspicious
                      for TP within 12 weeks after completion of chemoradiation
                      with temozolomide underwent an additional dynamic FET PET
                      scan. Volumes-of-interest were defined on summed images from
                      20-40 min post-injection (p.i.) by a 3-dimensional
                      auto-contouring process using a tumor-to-brain ratio (TBR)
                      of 1.6 or more. For each lesion, TBRs and the time-activity
                      curves (TACs) of the FET uptake were determined. The TACs
                      were used to evaluate the dynamic FET PET parameters
                      time-to-peak (TTP), slope (slope of linear regression line
                      20-50 min p.i.) and intercept (intercept of linear
                      regression line with y-axis). Additionally, 39 textural
                      parameters were calculated using the software LifeX
                      (lifexsoft.org). The diagnostic accuracy of TBRs, TTP,
                      slope, intercept, and textural parameters to discriminate
                      between PsP and TP was evaluated using ROC analyses. In
                      order to further increase the diagnostic accuracy,
                      parameters were combined using linear logistic regression
                      for classification of PsP and TP. Results: Fourteen patients
                      had a clinico-radiological diagnosis of TP and nine patients
                      had PsP. The FET PET parameters TBRmean, TBRmax, TTP and
                      intercept yielded a diagnostic accuracy to discriminate
                      between PsP and TP of $79\%,$ $79\%,$ $58\%,$ $63\%,$
                      respectively. The dynamic FET PET parameter slope yielded
                      the highest diagnostic accuracy of $83\%$ to discriminate
                      between PsP and TP. Two textural features showed a
                      comparable accuracy of $79\%.$ The diagnostic accuracy could
                      not be increased by combination of parameters. Conclusions:
                      Textural features yielded a comparable diagnostic accuracy
                      for diagnosis of pseudoprogression in glioblastoma patients
                      in comparison with static and dynamic FET PET parameters.
                      Textural features might yield additional valuable
                      information for this highly relevant clinical problem and
                      should be further evaluated in larger cohort prospective
                      studies.},
      cin          = {INM-4 / INM-11 / JARA-BRAIN / INM-5},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)VDB1046 / I:(DE-Juel1)INM-5-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)4},
      doi          = {10.1093/neuonc/nox036.282},
      url          = {https://juser.fz-juelich.de/record/908195},
}