Evidence map›Paper›PMID 37686690›Full record

ArticleCancers2023

Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas.

Burak Han Akkurt, Dorothee Cäcilia Spille, Susanne Peetz-Dienhart, Nora Maren Kiolbassa, Christian Mawrin, Manfred Musigmann, Walter Leonhard Heindel, Werner Paulus, Walter Stummer, Manoj Mannil and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Advancements in the application of MRI radiomics in meningioma.Radiation oncology (London, England) · 2025
    Review
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

11 authors at 2 institutions in 1 country.

Burak Han AkkurtDepartment of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany.ORCID 0000-0002-9305-2824
Dorothee Cäcilia SpilleDepartment of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany.ORCID 0000-0001-8358-1848
Susanne Peetz-DienhartInstitute of Neuropathology, University Hospital Muenster, DE-48149 Muenster, Germany.
Nora Maren KiolbassaDepartment of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany.
Christian MawrinDepartment of Neuropathology, University Hospital Magdeburg, 39120 Magdeburg, Germany.
Manfred MusigmannDepartment of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany.
Walter Leonhard HeindelDepartment of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany.
Werner PaulusInstitute of Neuropathology, University Hospital Muenster, DE-48149 Muenster, Germany.
Walter StummerDepartment of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany.
Manoj MannilDepartment of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany.
Benjamin BrokinkelDepartment of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany.
University Hospital Münster · DEUniversity Hospital Magdeburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIn meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment.

methodsSemiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting.

resultsA total of 117 image sets including training (

conclusionsRadiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.

Indexed as

human telomerase reverse transcriptasemagnetic resonance imagingmeningiomasradiomicsTERT

Identifiers

PMID37686690
PMCPMC10486806
OpenAlexW4386416268

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.