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@ARTICLE{Scheffler:298913,
      author       = {P. Scheffler and J. Straehle and A. El Rahal and D. Erny
                      and B. Mizaikoff and I. Vasilikos and M. Prinz and V. A.
                      Coenen and J. Kühn and F. Scherer$^*$ and D. H. Heiland$^*$
                      and O. Schnell and R. Roelz and J. Beck and P. C. Reinacher
                      and N. Neidert$^*$},
      title        = {{I}ntraoperative classification of {CNS} lymphoma and
                      glioblastoma by {AI}-based analysis of {S}timulated {R}aman
                      {H}istology ({SRH})},
      journal      = {Brain and spine},
      volume       = {5},
      issn         = {2772-5294},
      address      = {[Amsterdam]},
      publisher    = {Elsevier B.V.},
      reportid     = {DKFZ-2025-00349},
      pages        = {104187},
      year         = {2025},
      abstract     = {Introduction: Early diagnosis is important to differentiate
                      central nervous system lymphomas (CNSL) from themain
                      differential diagnosis, glioblastoma (GBM), because of
                      different primary treatment modalities for theseentities.
                      Due to neurological deficits, diagnostic stereotactic
                      biopsies often need to be performed urgently. In thissetting
                      the availability of an intraoperative neuropathological
                      assessment is limited.Research question: This study uses
                      AI-based analysis of Stimulated Raman Histology (SRH) to
                      establish a classifierdistinguishing CNSL from glioblastoma
                      in an intraoperative setting.Material and methods: We
                      collected 126 intraoperative SRH images from 40 patients
                      diagnosed with CNSL. TheseSRH images were divided into
                      patches, measuring 224 x 224 pixels each. Additionally, we
                      used a comparativedataset of 87 SRH images from 31 patients
                      with GBM as a control group to train and validate a neural
                      networkbased on the CTransPath architecture. Two distinct
                      diagnostic categories were established: “Lymphoma”
                      and“Glioblastoma".Results: Our model demonstrated an
                      accuracy rate of $92.5\%$ in distinguishing between lymphoma
                      and glioblastoma. Analysis of our test dataset showed a
                      sensitivity of $84.2\%$ and a specificity of $100\%$ in the
                      detection ofCNSL, demonstrating performance comparable to
                      standard intraoperative histopathological
                      analysis.Discussion and conclusion: The use of AI-driven
                      analysis of SRH images holds promise for intraoperative
                      tissueexamination of stereotactic biopsies with suspected
                      CNSL en par with the current gold standard. This study
                      couldimprove the management of these cases especially in the
                      emergency setting when conventional
                      intraoperativeneuropathological evaluation is unavailable.},
      cin          = {FR01},
      ddc          = {610},
      cid          = {I:(DE-He78)FR01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.bas.2025.104187},
      url          = {https://inrepo02.dkfz.de/record/298913},
}