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@ARTICLE{Reis:307553,
      author       = {J. Reis and R. Stahl and K. J. Müller and P. Karschnia and
                      N. Teske and A. Neubauer and L. von Baumgarten$^*$ and N.
                      Thon and F. Ringel and T. Liebig and N. L. Albert$^*$ and P.
                      Harter$^*$ and R. Forbrig},
      title        = {{A} novel vascular model yields increased {MR} perfusion
                      metrics compared to conventional dynamic susceptibility
                      contrast algorithms in untreated glioblastoma.},
      journal      = {Neuro-oncology advances},
      volume       = {7},
      number       = {1},
      issn         = {2632-2498},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2026-00050},
      pages        = {vdaf212},
      year         = {2025},
      note         = {Published:30 September 2025},
      abstract     = {Malignant gliomas are heterogeneous brain tumors with
                      extensive neovascularization. Conventional gradient-echo
                      dynamic susceptibility contrast (GRE-DSC) perfusion MRI may
                      underestimate microvascular alterations. We hypothesized
                      that a novel vascular model (NVM), based on Bayesian
                      voxel-wise transit time distribution analysis, could yield
                      higher perfusion metrics in untreated isocitrate
                      dehydrogenase (IDH)-wild-type glioblastoma compared to
                      standard vendor GRE-DSC algorithms.In this retrospective,
                      single-center study, 89 patients with neuropathologically
                      confirmed glioblastoma underwent pretherapeutic GRE-DSC
                      perfusion MRI at 1.5 or 3.0 T. Perfusion maps were generated
                      using both the NVM and default vendor algorithms. Using
                      co-registered T1-post-contrast and T2/FLAIR images, two
                      neuroradiologists independently assessed perfusion
                      conspicuity of color-coded maps for each algorithm and
                      manually performed region-of-interest analyses within
                      visually identified tumor hotspots for quantification.
                      Relative values of cerebral blood flow (rCBF), cerebral
                      blood volume (rCBV), and mean transit time (rMTT) were
                      normalized to contralateral normal-appearing white matter.
                      Nonparametric tests evaluated group differences.The NVM
                      yielded enhanced hotspot delineation and significantly
                      higher median normalized perfusion values than vendor
                      algorithms (all P < .001), with excellent inter-rater
                      reliability (Cohen's κ and intraclass correlation
                      coefficients ≥0.86). At 3.0 T, NVM-derived rCBV was
                      significantly higher than at 1.5 T (P = .008).NVM
                      post-processing yielded higher normalized CBF, CBV, and MTT
                      values within tumor hotspots than vendor pipelines,
                      suggesting that Bayesian model-based perfusion analysis may
                      enhance the detection of microvascular changes in
                      glioblastoma. As validation against a gold standard is
                      missing, prospective multicenter studies are warranted to
                      confirm our findings, particularly with regard to treatment
                      monitoring and clinical decision-making.},
      keywords     = {Bayesian modeling (Other) / glioblastoma (Other) / gradient
                      echo dynamic susceptibility contrast perfusion (Other) /
                      magnet resonance imaging (Other) / neoangiogenesis (Other)},
      cin          = {MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41497452},
      pmc          = {pmc:PMC12768504},
      doi          = {10.1093/noajnl/vdaf212},
      url          = {https://inrepo02.dkfz.de/record/307553},
}