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@ARTICLE{Bounias:302012,
      author       = {D. Bounias$^*$ and T. Führes and L. Brock and J. Graber
                      and L. A. Kapsner and A. Liebert and H. Schreiter and J.
                      Eberle and D. Hadler and D. Skwierawska and R. O. Floca$^*$
                      and P. Neher$^*$ and B. Kovacs$^*$ and E. Wenkel and S.
                      Ohlmeyer and M. Uder and K. Maier-Hein$^*$ and S.
                      Bickelhaupt},
      title        = {{AI}-{B}ased screening for thoracic aortic aneurysms in
                      routine breast {MRI}.},
      journal      = {Nature Communications},
      volume       = {16},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2025-01210},
      pages        = {5299},
      year         = {2025},
      note         = {#EA:E230#},
      abstract     = {Prognosis for thoracic aortic aneurysms is significantly
                      worse for women than men, with a higher mortality rate
                      observed among female patients. The increasing use of
                      magnetic resonance breast imaging (MRI) offers a unique
                      opportunity for simultaneous detection of both breast cancer
                      and thoracic aortic aneurysms. We retrospectively validate a
                      fully-automated artificial neural network (ANN) pipeline on
                      5057 breast MRI examinations from public (Duke University
                      Hospital/EA1141 trial) and in-house (Erlangen University
                      Hospital) data. The ANN, benchmarked against 3D-ground-truth
                      segmentations, clinical reports, and a multireader panel,
                      demonstrates high technical robustness (dice/clDice
                      0.88-0.91/0.97-0.99) across different vendors and field
                      strengths. The ANN improves aneurysm detection rates by
                      3.5-fold compared with routine clinical readings,
                      highlighting its potential to improve early diagnosis and
                      patient outcomes. Notably, a higher odds ratio (OR = 2.29,
                      CI: [0.55,9.61]) for thoracic aortic aneurysms is observed
                      in women with breast cancer or breast cancer history,
                      suggesting potential further benefits from integrated
                      simultaneous assessment for cancer and aortic aneurysms.},
      keywords     = {Humans / Female / Magnetic Resonance Imaging: methods /
                      Aortic Aneurysm, Thoracic: diagnostic imaging / Aortic
                      Aneurysm, Thoracic: diagnosis / Breast Neoplasms: diagnostic
                      imaging / Middle Aged / Neural Networks, Computer /
                      Retrospective Studies / Breast: diagnostic imaging / Aged /
                      Male / Mass Screening: methods / Adult},
      cin          = {E230 / HD01},
      ddc          = {500},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40506431},
      doi          = {10.1038/s41467-025-59694-2},
      url          = {https://inrepo02.dkfz.de/record/302012},
}