Evidence map›Paper›PMID 39843975›Full record

ArticleScientific reports2025

Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors.

Laetitia Lebrun, Nathalie Gilis, Manon Dausort, Chloé Gillard, Stefan Rusu, Karim Slimani, Olivier De Witte, Fabienne Escande, Florence Lefranc, Nicky D'Haene and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Laetitia LebrunDepartment of Pathology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University Hospital, Rue Meylemeersch 90, 1070, Brussels, Belgium. Laetitia.lebrun@hubruxelles.be.
Nathalie GilisDepartment of Neurosurgery, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University Hospital, 1070, Brussels, Belgium.
Manon DausortInstitute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-La-Neuve, Belgium.
Chloé GillardDIAPath, Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles (ULB), 6041, Gosselies, Belgium.
Stefan RusuDepartment of Pathology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University Hospital, Rue Meylemeersch 90, 1070, Brussels, Belgium.
Karim SlimaniDepartment of Pathology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University Hospital, Rue Meylemeersch 90, 1070, Brussels, Belgium.
Olivier De WitteDepartment of Neurosurgery, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University Hospital, 1070, Brussels, Belgium.
Fabienne EscandeService de Biochimie et Biologie Moléculaire, Pole Pathologie Biologie, CHU Lille, Lille, France.
Florence LefrancDepartment of Neurosurgery, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University Hospital, 1070, Brussels, Belgium.
Nicky D'HaeneDepartment of Pathology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University Hospital, Rue Meylemeersch 90, 1070, Brussels, Belgium.
Claude Alain MaurageUFR3S - Laboratoire d'Histologie, Univ. Lille, 59000, Lille, France.
Isabelle SalmonDIAPath, Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles (ULB), 6041, Gosselies, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past decade, neuropathological diagnosis has undergone significant changes, integrating morphological features with molecular biomarkers. The molecular era has successfully refined neuropathological diagnostic accuracy; however, a substantial number of CNS tumor diagnoses remain challenging, particularly in children. DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors. The aim of this study is to share our experience using DNA methylation classification in daily routine practice, illustrated through clinical cases. We employed a classification system to evaluate discrepancies between histo-molecular and DNA methylation diagnoses, with a specific focus on adult versus pediatric CNS tumors. In our study, we observed that 40% of cases fell into Class I, 47% into Class II, and 13% into Class III among the "matched cases" (≥ 0.84). In other words, DNA methylation classification confirmed morphological diagnoses in 63% of adult and 23% of pediatric cases. Refinement of diagnosis was particularly evident in the pediatric population (65% vs. 21% for the adult population, p = 0.006). Additionally, we discussed cases classified with low calibrated scores. In conclusion, our study confirms that DNA methylation classification provides significant added-value for CNS tumors diagnosis, particularly in pediatric cases.

Indexed as

Central Nervous System NeoplasmsDNA MethylationAdolescentAdultAgedBiomarkers, TumorChildChild, PreschoolFemaleHumansInfantMachine LearningMaleMiddle AgedYoung AdultBiomarkers, TumorCNS tumorDKFZ classifierDNA methylationNeuropathologyNext generation sequencingPediatric CNS tumors

Identifiers

PMID39843975
PMCPMC11754448

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.