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@ARTICLE{Haase:279447,
      author       = {Haase, Robert and Pinetz, Thomas and Kobler, Erich and
                      Bendella, Zeynep and Zülow, Stefan and Schievelkamp,
                      Arndt-Hendrik and Schmeel, Frederic Carsten and Panahabadi,
                      Sarah and Stylianou, Anna Magdalena and Paech, Daniel and
                      Foltyn-Dumitru, Martha and Wagner, Verena and Schlamp, Kai
                      and Heussel, Gudula and Holtkamp, Mathias and Heussel, Claus
                      Peter and Vahlensieck, Martin and Luetkens, Julian A and
                      Schlemmer, Heinz-Peter and Haubold, Johannes and Radbruch,
                      Alexander and Effland, Alexander and Deuschl, Cornelius and
                      Deike-Hofmann, Katerina},
      title        = {{D}eep {L}earning-{B}ased {S}ignal {A}mplification of
                      {T}1-{W}eighted {S}ingle-{D}ose {I}mages {I}mproves
                      {M}etastasis {D}etection in {B}rain {MRI}.},
      journal      = {Investigative radiology},
      volume       = {60},
      number       = {8},
      issn         = {0020-9996},
      address      = {Philadelphia, Pa.},
      publisher    = {Lippincott Williams $\&$ Wilkins},
      reportid     = {DZNE-2025-00778},
      pages        = {543 - 551},
      year         = {2025},
      abstract     = {Double-dose contrast-enhanced brain imaging improves tumor
                      delineation and detection of occult metastases but is
                      limited by concerns about gadolinium-based contrast agents'
                      effects on patients and the environment. The purpose of this
                      study was to test the benefit of a deep learning-based
                      contrast signal amplification in true single-dose
                      T1-weighted (T-SD) images creating artificial double-dose
                      (A-DD) images for metastasis detection in brain magnetic
                      resonance imaging.In this prospective, multicenter study, a
                      deep learning-based method originally trained on
                      noncontrast, low-dose, and T-SD brain images was applied to
                      T-SD images of 30 participants (mean age ± SD, 58.5 ± 11.8
                      years; 23 women) acquired externally between November 2022
                      and June 2023. Four readers with different levels of
                      experience independently reviewed T-SD and A-DD images for
                      metastases with 4 weeks between readings. A reference reader
                      reviewed additionally acquired true double-dose images to
                      determine any metastases present. Performances were compared
                      using Mid-p McNemar tests for sensitivity and Wilcoxon
                      signed rank tests for false-positive findings.All readers
                      found more metastases using A-DD images. The 2 experienced
                      neuroradiologists achieved the same level of sensitivity
                      using T-SD images (62 of 91 metastases, $68.1\%).$ While the
                      increase in sensitivity using A-DD images was only
                      descriptive for 1 of them (A-DD: 65 of 91 metastases,
                      $+3.3\%,$ P = 0.424), the second neuroradiologist benefited
                      significantly with a sensitivity increase of $12.1\%$ (73 of
                      91 metastases, P = 0.008). The 2 less experienced readers (1
                      resident and 1 fellow) both found significantly more
                      metastases on A-DD images (resident, T-SD: $61.5\%,$ A-DD:
                      $68.1\%,$ P = 0.039; fellow, T-SD: $58.2\%,$ A-DD: $70.3\%,$
                      P = 0.008). They were therefore able to use A-DD images to
                      increase their sensitivity to the neuroradiologists' initial
                      level on regular T-SD images. False-positive findings did
                      not differ significantly between sequences. However, readers
                      showed descriptively more false-positive findings on A-DD
                      images. The benefit in sensitivity particularly applied to
                      metastases ≤5 mm $(5.7\%-17.3\%$ increase in
                      sensitivity).A-DD images can improve the detectability of
                      brain metastases without a significant loss of precision and
                      could therefore represent a potentially valuable addition to
                      regular single-dose brain imaging.},
      keywords     = {Humans / Deep Learning / Brain Neoplasms: diagnostic
                      imaging / Brain Neoplasms: secondary / Female / Middle Aged
                      / Male / Magnetic Resonance Imaging: methods / Prospective
                      Studies / Contrast Media: administration $\&$ dosage /
                      Sensitivity and Specificity / Aged / Image Interpretation,
                      Computer-Assisted: methods / Image Enhancement: methods /
                      artificial double-dose (Other) / brain metastasis (Other) /
                      contrast maximization (Other) / convolutional neural network
                      (Other) / deep learning (Other) / gadolinium-based contrast
                      agent (Other) / magnetic resonance imaging (Other) /
                      metastasis detection (Other) / virtual contrast (Other) /
                      Contrast Media (NLM Chemicals)},
      cin          = {AG Radbruch},
      ddc          = {610},
      cid          = {I:(DE-2719)5000075},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39961132},
      doi          = {10.1097/RLI.0000000000001166},
      url          = {https://pub.dzne.de/record/279447},
}