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@ARTICLE{Wald:298593,
      author       = {T. Wald$^*$ and B. Hamm$^*$ and J. Holzschuh$^*$ and R. El
                      Shafie and A. Kudak$^*$ and B. Kovacs$^*$ and I. Pflüger
                      and B. von Nettelbladt and C. Ulrich$^*$ and M.
                      Baumgartner$^*$ and P. Vollmuth and J. Debus$^*$ and K.
                      Maier-Hein$^*$ and T. Welzel$^*$},
      title        = {{E}nhancing deep learning methods for brain metastasis
                      detection through cross-technique annotations on {SPACE}
                      {MRI}.},
      journal      = {European radiology experimental},
      volume       = {9},
      number       = {1},
      issn         = {2509-9280},
      address      = {[Cham]},
      publisher    = {Springer International Publishing},
      reportid     = {DKFZ-2025-00303},
      pages        = {15},
      year         = {2025},
      note         = {#EA:E230#},
      abstract     = {Gadolinium-enhanced 'sampling perfection with
                      application-optimized contrasts using different flip angle
                      evolution' (SPACE) sequence allows better visualization of
                      brain metastases (BMs) compared to 'magnetization-prepared
                      rapid acquisition gradient echo' (MPRAGE). We hypothesize
                      that this better conspicuity leads to high-quality
                      annotation (HAQ), enhancing deep learning (DL) algorithm
                      detection of BMs on MPRAGE images.Retrospective
                      contrast-enhanced (gadobutrol 0.1 mmol/kg) SPACE and MPRAGE
                      data of 157 patients with BM were used, either annotated on
                      MPRAGE resulting in normal annotation quality (NAQ) or on
                      coregistered SPACE resulting in HAQ. Multiple DL methods
                      were developed with NAQ or HAQ using either SPACE or MRPAGE
                      images and evaluated on their detection performance using
                      positive predictive value (PPV), sensitivity, and F1 score
                      and on their delineation performance using volumetric Dice
                      similarity coefficient, PPV, and sensitivity on one internal
                      and four additional test datasets (660 patients).The
                      SPACE-HAQ model reached 0.978 PPV, 0.882 sensitivity, and
                      0.916 F1-score. The MPRAGE-HAQ reached 0.867, 0.839, and
                      0.840, the MPRAGE NAQ 0.964, 0.667, and 0.798, respectively
                      (p ≥ 0.157). Relative to MPRAGE-NAQ, the MPRAGE-HAQ
                      F1-score detection increased on all additional test datasets
                      by 2.5-9.6 points (p < 0.016) and sensitivity improved on
                      three datasets by 4.6-8.5 points (p < 0.001). Moreover,
                      volumetric instance sensitivity improved by 3.6-7.6 points
                      (p < 0.001).HAQ improves DL methods without specialized
                      imaging during application time. HAQ alone achieves about
                      $40\%$ of the performance improvements seen with SPACE
                      images as input, allowing for fast and accurate, fully
                      automated detection of small (< 1 cm) BMs.Training with
                      higher-quality annotations, created using the SPACE
                      sequence, improves the detection and delineation sensitivity
                      of DL methods for the detection of brain metastases (BMs)on
                      MPRAGE images. This MRI cross-technique transfer learning is
                      a promising way to increase diagnostic
                      performance.Delineating small BMs on SPACE MRI sequence
                      results in higher quality annotations than on MPRAGE
                      sequence due to enhanced conspicuity. Leveraging
                      cross-technique ground truth annotations during training
                      improved the accuracy of DL models in detecting and
                      segmenting BMs. Cross-technique annotation may enhance DL
                      models by integrating benefits from specialized,
                      time-intensive MRI sequences while not relying on them.
                      Further validation in prospective studies is needed.},
      keywords     = {Humans / Brain Neoplasms: diagnostic imaging / Brain
                      Neoplasms: secondary / Deep Learning / Magnetic Resonance
                      Imaging: methods / Retrospective Studies / Male / Female /
                      Middle Aged / Contrast Media / Aged / Organometallic
                      Compounds / Adult / Brain neoplasms (Other) / Deep learning
                      (Other) / Image interpretation (computer-assisted) (Other) /
                      Image processing (computer-assisted) (Other) / Magnetic
                      resonance imaging (Other) / Contrast Media (NLM Chemicals) /
                      gadobutrol (NLM Chemicals) / Organometallic Compounds (NLM
                      Chemicals)},
      cin          = {E230 / E010 / E050 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)E010-20160331 /
                      I:(DE-He78)E050-20160331 / 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:39913077},
      doi          = {10.1186/s41747-025-00554-5},
      url          = {https://inrepo02.dkfz.de/record/298593},
}