Evidence map›Paper›PMID 31811342›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2020

Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric

Johannes Haubold, Aydin Demircioglu, Marcel Gratz, Martin Glas, Karsten Wrede, Ulrich Sure, Gerald Antoch, Kathy Keyvani, Mathias Nittka, Stephan Kannengiesser and 6 more

Abstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 8 pooled it
–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

78 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  6. Accuracy of Radiomics in PredictingRadiology. Artificial intelligence · 2024
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  15. Prognostic value of [European journal of nuclear medicine and molecular imaging · 2026
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  17. Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025
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18 more citing papers are in PubMed but not listed here.

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

16 authors.

Johannes HauboldDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany. johannes.haubold@uk-essen.de.ORCID 0000-0003-4843-5911
Aydin DemirciogluDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
Marcel GratzErwin L Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany.
Martin GlasDivision of Clinical Neurooncology, Department of Neurology, University Hospital Essen, Essen, Germany.
Karsten WredeDepartment of Neurosurgery, University Hospital Essen, Essen, Germany.
Ulrich SureDepartment of Neurosurgery, University Hospital Essen, Essen, Germany.
Gerald AntochDepartment of Diagnostic and Interventional Radiology, University Dusseldorf, Medical Faculty, 40225, Dusseldorf, Germany.
Kathy KeyvaniDepartment of Neuropathology, University Hospital Essen, Essen, Germany.
Mathias NittkaSiemens Healthcare, Erlangen, Germany.
Stephan KannengiesserSiemens Healthcare, Erlangen, Germany.
Vikas GulaniCase Western Reserve University, Cleveland, USA.
Mark GriswoldCase Western Reserve University, Cleveland, USA.
Ken HerrmannClinic of Nuclearmedicine, University Hospital Essen, Essen, Germany.
Michael ForstingDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
Felix NensaDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
Lale UmutluDepartment of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe introduction of the 2016 WHO classification of CNS tumors has made the combined molecular and histopathological characterization of tumors a pivotal part of glioma patient management. Recent publications on radiogenomics-based prediction of the mutational status have demonstrated the predictive potential of imaging-based, non-invasive tissue characterization algorithms. Hence, the aim of this study was to assess the potential of multiparametric MATERIALS AND

methods42 patients with suspected primary brain tumor without prior surgical or systemic treatment or biopsy underwent an

resultsThe 5-fold cross-validated area under the curve in predicting the ATRX mutation was 85.1%, MGMT mutation was 75.7%, IDH1 was 88.7%, and 1p19q was 97.8%. The area under the curve of differentiating low-grade glioma vs. high-grade glioma was 85.2%.

conclusion

Indexed as

Brain NeoplasmsGliomaHumansMagnetic Resonance ImagingPositron-Emission TomographyTyrosineTyrosine1p19qATRXFET PETGliomaIDH1MR fingerprintingPET-MRIRadiomics

Identifiers

PMID31811342

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.