% 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”.

@ARTICLE{Buchner:282682,
      author       = {J. A. Buchner and J. C. Peeken$^*$ and L. Etzel$^*$ and I.
                      Ezhov and M. Mayinger and S. M. Christ and T. B. Brunner and
                      A. Wittig and B. Menze and C. Zimmer and B. Meyer and M.
                      Guckenberger and N. Andratschke and R. A. El Shafie and J.
                      Debus and S. Rogers and O. Riesterer and K. Schulze and H.
                      J. Feldmann and O. Blanck and C. Zamboglou$^*$ and K.
                      Ferentinos and A. Bilger$^*$ and A. L. Grosu$^*$ and R.
                      Wolff and J. S. Kirschke and K. A. Eitz$^*$ and S. E.
                      Combs$^*$ and D. Bernhardt$^*$ and D. Rueckert and M. Piraud
                      and B. Wiestler and F. Kofler},
      title        = {{I}dentifying core {MRI} sequences for reliable automatic
                      brain metastasis segmentation.},
      journal      = {Radiotherapy and oncology},
      volume       = {188},
      issn         = {0167-8140},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2023-01828},
      pages        = {109901},
      year         = {2023},
      note         = {Volume 188, November 2023, 109901},
      abstract     = {Many automatic approaches to brain tumor segmentation
                      employ multiple magnetic resonance imaging (MRI) sequences.
                      The goal of this project was to compare different
                      combinations of input sequences to determine which MRI
                      sequences are needed for effective automated brain
                      metastasis (BM) segmentation.We analyzed preoperative
                      imaging (T1-weighted sequence ± contrast-enhancement
                      (T1/T1-CE), T2-weighted sequence (T2), and T2
                      fluid-attenuated inversion recovery (T2-FLAIR) sequence)
                      from 339 patients with BMs from seven centers. A baseline 3D
                      U-Net with all four sequences and six U-Nets with plausible
                      sequence combinations (T1-CE, T1, T2-FLAIR, T1-CE+T2-FLAIR,
                      T1-CE+T1+T2-FLAIR, T1-CE+T1) were trained on 239 patients
                      from two centers and subsequently tested on an external
                      cohort of 100 patients from five centers.The model based on
                      T1-CE alone achieved the best segmentation performance for
                      BM segmentation with a median Dice similarity coefficient
                      (DSC) of 0.96. Models trained without T1-CE performed worse
                      (T1-only: DSC = 0.70 and T2-FLAIR-only: DSC = 0.73). For
                      edema segmentation, models that included both T1-CE and
                      T2-FLAIR performed best (DSC = 0.93), while the remaining
                      four models without simultaneous inclusion of these both
                      sequences reached a median DSC of 0.81-0.89.A T1-CE-only
                      protocol suffices for the segmentation of BMs. The
                      combination of T1-CE and T2-FLAIR is important for edema
                      segmentation. Missing either T1-CE or T2-FLAIR decreases
                      performance. These findings may improve imaging routines by
                      omitting unnecessary sequences, thus allowing for faster
                      procedures in daily clinical practice while enabling optimal
                      neural network-based target definitions.},
      keywords     = {CNN (Other) / FLAIR (Other) / MRI sequences (Other) / T1
                      (Other) / U-net (Other) / brain metastases (Other) / deep
                      learning (Other) / segmentation (Other)},
      cin          = {MU01 / FR01},
      ddc          = {610},
      cid          = {I:(DE-He78)MU01-20160331 / I:(DE-He78)FR01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37678623},
      doi          = {10.1016/j.radonc.2023.109901},
      url          = {https://inrepo02.dkfz.de/record/282682},
}