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@ARTICLE{Ruiz:301290,
      author       = {F. Ruiz and R. Rispoli and Z. Jaunmuktane and A. Merve and
                      L. D'Antona and M. Dutt and F. Sahm$^*$ and S. Brandner},
      title        = {{T}esting {M}eningiomas {W}ith {M}ethylation {A}rrays:
                      {I}nsights and {R}ecommendations {F}rom a {L}arge
                      {S}ingle-{C}entre {S}tudy.},
      journal      = {Neuropathology $\&$ applied neurobiology},
      volume       = {51},
      number       = {3},
      issn         = {0305-1846},
      address      = {Oxford [u.a.]},
      publisher    = {Wiley-Blackwell},
      reportid     = {DKFZ-2025-00974},
      pages        = {e70018},
      year         = {2025},
      abstract     = {Meningiomas are common primary CNS tumours, and their
                      morphological diagnosis is usually straightforward. Their
                      histological grading according to CNS WHO criteria alone
                      provides limited information on recurrence risk. Risk
                      stratification of meningiomas combining WHO grade,
                      methylation class and copy number profile improves
                      prediction of the risk of early recurrence. Because of the
                      frequency of meningiomas in diagnostic practice, applying
                      this prediction algorithm to all meningiomas is financially
                      not viable in most healthcare systems.We analysed a
                      retrospective dataset of over 1000 meningiomas from a single
                      centre with methylation arrays to provide guidance on which
                      meningiomas to prioritise for integrated molecular testing
                      and to understand how WHO grades resolve into risk
                      strata.Approximately $90\%$ of CNS WHO Grade 1 meningiomas
                      were allocated into the methylation class 'benign' and also
                      into a low-risk group. Grade 2 meningiomas were allocated
                      almost equally to either the low-risk $(39\%)$ or
                      intermediate-risk groups $(46\%)$ but occasionally also to
                      the high-risk group $(15\%).$ All grading criteria for CNS
                      WHO Grade 2 meningiomas (brain invasion, mitotic count,
                      cytoarchitectural atypia and histological type) showed a
                      similar risk score distribution as the entire group. Grade 3
                      meningiomas were allocated to intermediate- $(26\%)$ or
                      high-risk groups $(74\%).Our$ data suggest that Grade 2 and
                      3 meningiomas should be prioritised for methylation
                      profiling. A small proportion of Grade 1 meningiomas may
                      also benefit from integrated molecular analysis, and further
                      research is needed to explore if those histologically benign
                      meningiomas with a predicted increased recurrence risk are
                      associated with distinct demographic or histological
                      characteristics.},
      keywords     = {Humans / Meningioma: genetics / Meningioma: pathology /
                      Meningioma: diagnosis / Meningeal Neoplasms: genetics /
                      Meningeal Neoplasms: pathology / Meningeal Neoplasms:
                      diagnosis / Female / Male / DNA Methylation / Retrospective
                      Studies / Middle Aged / Aged / Adult / Neoplasm Grading /
                      Aged, 80 and over / CNS WHO grading (Other) / meningioma
                      (Other) / methylation array (Other) / model score (Other) /
                      prognostication (Other)},
      cin          = {B300 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)B300-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40356449},
      doi          = {10.1111/nan.70018},
      url          = {https://inrepo02.dkfz.de/record/301290},
}