Evidence map›Paper›PMID 37935168›Full record

ReviewJournal of Korean medical science2023

Brain Tumor Classification by Methylation Profile.

Jin Woo Park, Kwanghoon Lee, Eric Eunshik Kim, Seong-Ik Kim, Sung-Hye Park

Abstract readReview
In one paragraph

Review in Journal of Korean medical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
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  7. Review
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  11. Review
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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

5 authors.

Jin Woo Park *Department of Pathology, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-0801-8208
Kwanghoon Lee *Department of Pathology, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-9000-5902
Eric Eunshik KimDepartment of Pathology, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-5020-9150
Seong-Ik KimDepartment of Pathology, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-7713-3782
Sung-Hye ParkDepartment of Pathology, Seoul National University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-8681-1597

Funding

Institute for Information & Communications Technology Promotion 2019-0567Korea Health Industry Development Institute HI14C1277
6 · The paper itself

Abstract

The goal of the methylation classifier in brain tumor classification is to accurately classify tumors based on their methylation profiles. Accurate brain tumor diagnosis is the first step for healthcare professionals to predict tumor prognosis and establish personalized treatment plans for patients. The methylation classifier can be used to perform classification on tumor samples with diagnostic difficulties due to ambiguous histology or mismatch between histopathology and molecular signatures, i.e., not otherwise specified (NOS) cases or not elsewhere classified (NEC) cases, aiding in pathological decision-making. Here, the authors elucidate upon the application of a methylation classifier as a tool to mitigate the inherent complexities associated with the pathological evaluation of brain tumors, even when pathologists are experts in histopathological diagnosis and have access to enough molecular genetic information. Also, it should be emphasized that methylome cannot classify all types of brain tumors, and it often produces erroneous matches even with high matching scores, so, excessive trust is prohibited. The primary issue is the considerable difficulty in obtaining reference data regarding the methylation profile of each type of brain tumor. This challenge is further amplified when dealing with recently identified novel types or subtypes of brain tumors, as such data are not readily accessible through open databases or authors of publications. An additional obstacle arises from the fact that methylation classifiers are primarily research-based, leading to the unavailability of charging patients. It is important to note that the application of methylation classifiers may require specialized laboratory techniques and expertise in DNA methylation analysis.

Indexed as

Brain NeoplasmsDNA MethylationDatabases, FactualHumansPrognosisBioinformaticsBrain TumorClassificationsMethylation

Identifiers

PMID37935168
PMCPMC10627723

What OpenQuestion holds

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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.