Evidence map›Paper›PMID 27300198›Full record

ArticleEuropean radiology2017

Local image variance of 7 Tesla SWI is a new technique for preoperative characterization of diffusely infiltrating gliomas: correlation with tumour grade and IDH1 mutational status.

Günther Grabner, Barbara Kiesel, Adelheid Wöhrer, Matthias Millesi, Aygül Wurzer, Sabine Göd, Ammar Mallouhi, Engelbert Knosp, Christine Marosi, Siegfried Trattnig and 3 more

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In one paragraph

Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 4 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

17 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

13 authors.

Günther GrabnerHigh Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Barbara KieselComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Adelheid WöhrerComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Matthias MillesiComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Aygül WurzerComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Sabine GödHigh Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Ammar MallouhiComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Engelbert KnospComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Christine MarosiComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Siegfried TrattnigHigh Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Stefan WolfsbergerComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Matthias PreusserComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria.
Georg WidhalmComprehensive Cancer Center, Central Nervous System Tumours Unit (CCC-CNS), Medical University of Vienna, Waehringer Guertel 18-20, 1097, Vienna, Austria. georg.widhalm@meduniwien.ac.at.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate the value of local image variance (LIV) as a new technique for quantification of hypointense microvascular susceptibility-weighted imaging (SWI) structures at 7 Tesla for preoperative glioma characterization.

methodsAdult patients with neuroradiologically suspected diffusely infiltrating gliomas were prospectively recruited and 7 Tesla SWI was performed in addition to standard imaging. After tumour segmentation, quantification of intratumoural SWI hypointensities was conducted by the SWI-LIV technique. Following surgery, the histopathological tumour grade and isocitrate dehydrogenase 1 (IDH1)-R132H mutational status was determined and SWI-LIV values were compared between low-grade gliomas (LGG) and high-grade gliomas (HGG), IDH1-R132H negative and positive tumours, as well as gliomas with significant and non-significant contrast-enhancement (CE) on MRI.

resultsIn 30 patients, 9 LGG and 21 HGG were diagnosed. The calculation of SWI-LIV values was feasible in all tumours. Significantly higher mean SWI-LIV values were found in HGG compared to LGG (92.7 versus 30.8; p < 0.0001), IDH1-R132H negative compared to IDH1-R132H positive gliomas (109.9 versus 38.3; p < 0.0001) and tumours with significant CE compared to non-significant CE (120.1 versus 39.0; p < 0.0001).

conclusionsOur data indicate that 7 Tesla SWI-LIV might improve preoperative characterization of diffusely infiltrating gliomas and thus optimize patient management by quantification of hypointense microvascular structures. KEY POINTS: • 7 Tesla local image variance helps to quantify hypointense susceptibility-weighted imaging structures. • SWI-LIV is significantly increased in high-grade and IDH1-R132H negative gliomas. • SWI-LIV is a promising technique for improved preoperative glioma characterization. • Preoperative management of diffusely infiltrating gliomas will be optimized.

Indexed as

MutationAdultAgedBrain NeoplasmsFemaleGliomaHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedNeoplasm GradingPreoperative CareProspective StudiesYoung Adult7 Tesla MRIDiffusely infiltrating gliomasGlioma characterizationLocal image varianceSusceptibility-weighted imaging

Identifiers

PMID27300198
PMCPMC5334387

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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.