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@ARTICLE{Ritter:298356,
      author       = {M. Ritter$^*$ and C. Blume$^*$ and Y. Tang$^*$ and A. J.
                      Patel$^*$ and B. Patel$^*$ and N. Berghaus$^*$ and J. Kada
                      Benotmane and J. Kueckelhaus and Y. Yabo and J. Zhang and E.
                      Grabis and G. Villa and D. N. Zimmer and A. Khriesh and P.
                      Sievers$^*$ and Z. Seferbekova$^*$ and F. Hinz$^*$ and V. M.
                      Ravi and M. Seiz-Rosenhagen and M. Ratliff and C.
                      Herold-Mende and O. Schnell$^*$ and J. Beck and W. Wick$^*$
                      and A. von Deimling$^*$ and M. Gerstung$^*$ and D. H.
                      Heiland$^*$ and F. Sahm$^*$},
      title        = {{S}patially resolved transcriptomics and graph-based deep
                      learning improve accuracy of routine {CNS} tumor
                      diagnostics.},
      journal      = {Nature cancer},
      volume       = {6},
      number       = {2},
      issn         = {2662-1347},
      address      = {London},
      publisher    = {Nature Research},
      reportid     = {DKFZ-2025-00257},
      pages        = {292-306},
      year         = {2025},
      note         = {#EA:B320#LA:B320# / 2025 Feb;6(2):292-306},
      abstract     = {The diagnostic landscape of brain tumors integrates
                      comprehensive molecular markers alongside traditional
                      histopathological evaluation. DNA methylation and
                      next-generation sequencing (NGS) have become a cornerstone
                      in central nervous system (CNS) tumor classification. A
                      limiting requirement for NGS and methylation profiling is
                      sufficient DNA quality and quantity, which restrict its
                      feasibility. Here we demonstrate NePSTA (neuropathology
                      spatial transcriptomic analysis) for comprehensive
                      morphological and molecular neuropathological diagnostics
                      from single 5-µm tissue sections. NePSTA uses spatial
                      transcriptomics with graph neural networks for automated
                      histological and molecular evaluations. Trained and
                      evaluated across 130 participants with CNS malignancies and
                      healthy donors across four medical centers, NePSTA predicts
                      tissue histology and methylation-based subclasses with high
                      accuracy. We demonstrate the ability to reconstruct
                      immunohistochemistry and genotype profiling on tissue with
                      minimal requirements, inadequate for conventional molecular
                      diagnostics, demonstrating the potential to enhance tumor
                      subtype identification with implications for fast and
                      precise diagnostic workup.},
      cin          = {B320 / HD01 / B450 / B062 / FR01 / B300},
      ddc          = {610},
      cid          = {I:(DE-He78)B320-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)B450-20160331 / I:(DE-He78)B062-20160331 /
                      I:(DE-He78)FR01-20160331 / I:(DE-He78)B300-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39880907},
      doi          = {10.1038/s43018-024-00904-z},
      url          = {https://inrepo02.dkfz.de/record/298356},
}