% 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{Erdur:302814,
      author       = {A. C. Erdur and D. Scholz and Q. M. Nguyen and J. A.
                      Buchner and M. Mayinger and S. M. Christ and T. B. Brunner
                      and A. Wittig and C. Zimmer and B. Meyer and M. Guckenberger
                      and N. Andratschke and R. A. El Shafie and J. U. Debus and
                      S. Rogers and O. Riesterer and K. Schulze and H. J. Feldmann
                      and O. Blanck and C. Zamboglou$^*$ and A. Bilger-Z$^*$ and
                      A. L. Grosu$^*$ and R. Wolff and K. A. Eitz$^*$ and S. E.
                      Combs$^*$ and D. Bernhardt$^*$ and B. Wiestler and D.
                      Rueckert and J. C. Peeken$^*$},
      title        = {{I}mproving risk assessment of local failure in brain
                      metastases patients using vision transformers - {A}
                      multicentric development and validation study.},
      journal      = {Radiotherapy and oncology},
      volume       = {210},
      issn         = {0167-8140},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2025-01354},
      pages        = {111031},
      year         = {2025},
      abstract     = {This study investigates the use of Vision Transformers
                      (ViTs) to predict Freedom from Local Failure (FFLF) in
                      patients with brain metastases using pre-operative MRI
                      scans. The goal is to develop a model that enhances risk
                      stratification and informs personalized treatment
                      strategies.Within the AURORA retrospective trial, patients
                      (n = 352) who received surgical resection followed by
                      post-operative stereotactic radiotherapy (SRT) were
                      collected from seven hospitals. We trained our ViT for the
                      direct image-to-risk task on T1-CE and FLAIR sequences and
                      combined clinical features along the way. We employed
                      segmentation-guided image modifications, model adaptations,
                      and specialized patient sampling strategies during training.
                      The model was evaluated with five-fold cross-validation and
                      ensemble learning across all validation runs. An external,
                      international test cohort (n = 99) within the dataset was
                      used to assess the generalization capabilities of the model,
                      and saliency maps were generated for explainability
                      analysis.We achieved a competent C-Index score of 0.7982 on
                      the test cohort, surpassing all clinical, CNN-based, and
                      hybrid baselines. Kaplan-Meier analysis showed significant
                      FFLF risk stratification. Saliency maps focusing on the BM
                      core confirmed that model explanations aligned with expert
                      observations.Our ViT-based model offers a potential for
                      personalized treatment strategies and follow-up regimens in
                      patients with brain metastases. It provides an alternative
                      to radiomics as a robust, automated tool for clinical
                      workflows, capable of improving patient outcomes through
                      effective risk assessment and stratification.},
      keywords     = {Artificial Intelligence (Other) / Brain metastases (Other)
                      / Stereotactic radiotherapy (Other) / Vision Transformers
                      (Other)},
      cin          = {FR01 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)FR01-20160331 / I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40618900},
      doi          = {10.1016/j.radonc.2025.111031},
      url          = {https://inrepo02.dkfz.de/record/302814},
}