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@ARTICLE{Hielscher:182607,
author = {T. Hielscher$^*$ and M. Sill$^*$ and P. Sievers$^*$ and D.
Stichel$^*$ and S. Brandner and D. Jones$^*$ and A. von
Deimling$^*$ and F. Sahm$^*$ and S. L. N. Maas},
title = {{C}linical implementation of integrated
molecular-morphologic risk prediction for meningioma.},
journal = {Brain pathology},
volume = {33},
number = {3},
issn = {1015-6305},
address = {Oxford},
publisher = {Wiley-Blackwell},
reportid = {DKFZ-2022-02788},
pages = {e13132},
year = {2023},
note = {#EA:C060#LA:B300# / 2023 May;33(3):e13132},
abstract = {Risk prediction for meningioma tumors was until recently
almost exclusively based on morphological features of the
tumor. To improve risk prediction, multiple models have been
established that incorporate morphological and molecular
features for an integrated risk prediction score. One such
model is the integrated molecular-morphologic meningioma
integrated score (IntS), which allocates points to the
histological grade, epigenetic methylation family and
specific copy-number variations. After publication of the
IntS, questions arose in the neuropathological community
about the practical and clinical implementation of the IntS,
specifically regarding the calling of CNVs, the
applicability of the newly available version (v12.5) of the
brain tumor classifier and the need for incorporation of
TERT-promoter and CDKN2A/B status analysis in the IntS
calculation. To investigate and validate these questions
additional analyses of the discovery (n = 514),
retrospective validation (n = 184) and prospective
validation (n = 287) cohorts used for IntS discovery and
validation were performed. Our findings suggest that any
loss over $5\%$ of the chromosomal arm suffices for the
calling of a CNV, that input from the v12.5 classifier is as
good or better than the dedicated meningioma classifier
(v2.4) and that there is most likely no need for additional
testing for TERT-promoter mutations and/or homozygous losses
of CDKN2A/B when defining the IntS for an individual
patient. The findings from this study help facilitate the
clinical implementation of IntS-based risk prediction for
meningioma patients.},
keywords = {brain tumors (Other) / meningioma (Other) / molecular
biomarkers (Other) / risk prediction (Other) / tumor
classification (Other)},
cin = {C060 / B062 / B300 / HD01 / B360},
ddc = {610},
cid = {I:(DE-He78)C060-20160331 / I:(DE-He78)B062-20160331 /
I:(DE-He78)B300-20160331 / I:(DE-He78)HD01-20160331 /
I:(DE-He78)B360-20160331},
pnm = {312 - Funktionelle und strukturelle Genomforschung
(POF4-312)},
pid = {G:(DE-HGF)POF4-312},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:36377252},
doi = {10.1111/bpa.13132},
url = {https://inrepo02.dkfz.de/record/182607},
}