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@ARTICLE{Lohmann:851404,
      author       = {Lohmann, Philipp and Kocher, Martin and Ceccon, Garry and
                      Bauer, Elena K. and Stoffels, Gabriele and Viswanathan,
                      Shivakumar and Ruge, Maximilian I. and Neumaier, Bernd and
                      Shah, Nadim J. and Fink, Gereon R. and Langen, Karl-Josef
                      and Galldiks, Norbert},
      title        = {{C}ombined {FET} {PET}/{MRI} radiomics differentiates
                      radiation injury from recurrent brain metastasis},
      journal      = {NeuroImage: Clinical},
      volume       = {20},
      issn         = {2213-1582},
      address      = {[Amsterdam u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2018-05053},
      pages        = {537 - 542},
      year         = {2018},
      abstract     = {Background<br>The aim of this study was to investigate the
                      potential of combined textural feature analysis of
                      contrast-enhanced MRI (CE-MRI) and static
                      O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for the
                      differentiation between local recurrent brain metastasis and
                      radiation injury since CE-MRI often remains
                      inconclusive.<br>Methods<br>Fifty-two patients with new or
                      progressive contrast-enhancing brain lesions on MRI after
                      radiotherapy (predominantly stereotactic radiosurgery) of
                      brain metastases were additionally investigated using FET
                      PET. Based on histology (n = 19) or clinicoradiological
                      follow-up (n = 33), local recurrent brain metastases
                      were diagnosed in 21 patients $(40\%)$ and radiation injury
                      in 31 patients $(60\%).$ Forty-two textural features were
                      calculated on both unfiltered and filtered CE-MRI and summed
                      FET PET images (20–40 min p.i.), using the software
                      LIFEx. After feature selection, logistic regression models
                      using a maximum of five features to avoid overfitting were
                      calculated for each imaging modality separately and for the
                      combined FET PET/MRI features. The resulting models were
                      validated using cross-validation. Diagnostic accuracies were
                      calculated for each imaging modality separately as well as
                      for the combined model.<br>Results<br>For the
                      differentiation between radiation injury and recurrence of
                      brain metastasis, textural features extracted from CE-MRI
                      had a diagnostic accuracy of $81\%$ (sensitivity, $67\%;$
                      specificity, $90\%).$ FET PET textural features revealed a
                      slightly higher diagnostic accuracy of $83\%$ (sensitivity,
                      $88\%;$ specificity, $75\%).$ However, the highest
                      diagnostic accuracy was obtained when combining CE-MRI and
                      FET PET features (accuracy, $89\%;$ sensitivity, $85\%;$
                      specificity, $96\%).<br>Conclusions<br>Our$ findings suggest
                      that combined FET PET/CE-MRI radiomics using textural
                      feature analysis offers a great potential to contribute
                      significantly to the management of patients with brain
                      metastases.},
      cin          = {INM-3 / INM-4 / INM-5},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-4-20090406 /
                      I:(DE-Juel1)INM-5-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30175040},
      UT           = {WOS:000450799000058},
      doi          = {10.1016/j.nicl.2018.08.024},
      url          = {https://juser.fz-juelich.de/record/851404},
}