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@ARTICLE{Gutsche:1010425,
      author       = {Gutsche, Robin and Lowis, Carsten and Ziemons, Karl and
                      Kocher, Martin and Ceccon, Garry and Brambilla, Cláudia
                      Régio and Shah, Nadim J and Langen, Karl-Josef and
                      Galldiks, Norbert and Isensee, Fabian and Lohmann, Philipp},
      title        = {{A}utomated {B}rain {T}umor {D}etection and {S}egmentation
                      for {T}reatment {R}esponse {A}ssessment {U}sing {A}mino
                      {A}cid {PET}.},
      journal      = {Journal of nuclear medicine},
      volume       = {64},
      number       = {10},
      issn         = {0097-9058},
      address      = {New York, NY},
      publisher    = {Soc.},
      reportid     = {FZJ-2023-03049},
      pages        = {-},
      year         = {2023},
      abstract     = {Evaluation of metabolic tumor volume (MTV) changes using
                      amino acid PET has become an important tool for response
                      assessment in brain tumor patients. MTV is usually
                      determined by manual or semiautomatic delineation, which is
                      laborious and may be prone to intra- and interobserver
                      variability. The goal of our study was to develop a method
                      for automated MTV segmentation and to evaluate its
                      performance for response assessment in patients with
                      gliomas. Methods: In total, 699 amino acid PET scans using
                      the tracer O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) from
                      555 brain tumor patients at initial diagnosis or during
                      follow-up were retrospectively evaluated (mainly glioma
                      patients, $76\%).$ 18F-FET PET MTVs were segmented
                      semiautomatically by experienced readers. An artificial
                      neural network (no new U-Net) was configured on 476 scans
                      from 399 patients, and the network performance was evaluated
                      on a test dataset including 223 scans from 156 patients.
                      Surface and volumetric Dice similarity coefficients (DSCs)
                      were used to evaluate segmentation quality. Finally, the
                      network was applied to a recently published 18F-FET PET
                      study on response assessment in glioblastoma patients
                      treated with adjuvant temozolomide chemotherapy for a fully
                      automated response assessment in comparison to an
                      experienced physician. Results: In the test dataset, $92\%$
                      of lesions with increased uptake (n = 189) and $85\%$ of
                      lesions with iso- or hypometabolic uptake (n = 33) were
                      correctly identified (F1 score, $92\%).$ Single lesions with
                      a contiguous uptake had the highest DSC, followed by lesions
                      with heterogeneous, noncontiguous uptake and multifocal
                      lesions (surface DSC: 0.96, 0.93, and 0.81 respectively;
                      volume DSC: 0.83, 0.77, and 0.67, respectively). Change in
                      MTV, as detected by the automated segmentation, was a
                      significant determinant of disease-free and overall
                      survival, in agreement with the physician's assessment.
                      Conclusion: Our deep learning-based 18F-FET PET segmentation
                      allows reliable, robust, and fully automated evaluation of
                      MTV in brain tumor patients and demonstrates clinical value
                      for automated response assessment.},
      keywords     = {AI (Other) / FET PET (Other) / artificial intelligence
                      (Other) / machine learning (Other) / neurooncology (Other) /
                      volumetry (Other)},
      cin          = {INM-4 / INM-11 / INM-3 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)INM-3-20090406 / $I:(DE-82)080010_20140620$},
      pnm          = {5253 - Neuroimaging (POF4-525) / DFG project 428090865 -
                      Radiomics basierend auf MRT und Aminosäure PET in der
                      Neuroonkologie (428090865) / DFG project 491111487 -
                      Open-Access-Publikationskosten / 2022 - 2024 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5253 / G:(GEPRIS)428090865 /
                      G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37562802},
      doi          = {10.2967/jnumed.123.265725},
      url          = {https://juser.fz-juelich.de/record/1010425},
}