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@ARTICLE{Scheffler:307512,
      author       = {P. Scheffler and N. Neidert$^*$ and J. Straehle and D. Erny
                      and M. Prinz and U. Hubbe and R. Rölz and D. H. Heiland and
                      J. Beck and A. El Rahal},
      title        = {{A}rtificial intelligence-based analysis and diagnosis of
                      intradural extramedullary spinal tumors by stimulated
                      {R}aman histology.},
      journal      = {Neuro-oncology advances},
      volume       = {7},
      number       = {1},
      issn         = {2632-2498},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2026-00017},
      pages        = {vdaf211},
      year         = {2025},
      abstract     = {Intraoperative Stimulated Raman Histology (SRH) has been
                      reported to be fast and accurate in the assessment of
                      neuro-oncological lesions. However, its application to
                      spinal tumors, especially intradural extramedullary tumors
                      (IDEM), remains underexplored. IDEM primarily include
                      meningiomas and schwannomas, as well as less common entities
                      such as metastases or ependymomas. Given that surgical
                      resection is the primary treatment modality, rapid,
                      artificial intelligence (AI)-driven intraoperative tumor
                      classification based on SRH could enhance surgical
                      decision-making and subsequent management.We acquired 232
                      SRH images from patients with IDEM using the NIO Laser
                      Imaging System (Invenio Imaging Inc.). We categorized images
                      into three diagnostic classes: 'Meningioma,' 'Schwannoma,'
                      and 'Other.' Images were divided into 224 × 224 pixel
                      patches and used to train and test AI-based image
                      classifiers employing CTransPath, ResNet, and Vision
                      Transformer architectures.Our best-performing model,
                      utilizing the CTransPath architecture, achieved a
                      classification accuracy of $94.3\%$ on the test dataset.
                      Vision Transformer-based models also performed well,
                      exceeding $90\%$ accuracy, while ResNet models attained
                      slightly lower accuracies $(79.6\%-88.8\%).$ Qualitative
                      analysis indicates that the top-performing model primarily
                      relies on cellular morphology for classification.Our
                      findings confirm the feasibility and effectiveness of
                      AI-assisted SRH analysis for distinguishing IDEM tumor
                      types. This approach may complement conventional
                      intraoperative neuropathology by providing rapid, reliable,
                      and clinically actionable diagnostic information.},
      keywords     = {artificial intelligence (Other) / intradural extramedullary
                      tumors (Other) / stimulated raman histology (Other)},
      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},
      pubmed       = {pmid:41473747},
      pmc          = {pmc:PMC12746601},
      doi          = {10.1093/noajnl/vdaf211},
      url          = {https://inrepo02.dkfz.de/record/307512},
}