Evidence map›Paper›PMID 32977210›Full record

ArticleNeuroImage. Clinical2020

High-resolution metabolic imaging of high-grade gliomas using 7T-CRT-FID-MRSI.

Gilbert Hangel, Cornelius Cadrien, Philipp Lazen, Julia Furtner, Alexandra Lipka, Eva Hečková, Lukas Hingerl, Stanislav Motyka, Stephan Gruber, Bernhard Strasser and 9 more

Open access · goldAbstract read
In one paragraph

Article in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
3.6field-weighted citation impact, top 5% 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

37 citing papers in PubMed, 76 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Monitoring Pharmacological Treatment of Breast Cancer with MRI.Current issues in molecular biology · 2025
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Ultrahigh-field MRI: where it really makes a difference.Radiologie (Heidelberg, Germany) · 2024
    Review
  15. Article
  16. Article
  17. 7 Tesla magnetic resonance spectroscopic imaging predicting IDH status and glioma grading.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Article
  18. Article
  19. Article
  20. Article
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

19 authors at 3 institutions in 2 countries.

Gilbert HangelHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria. Electronic address: gilbert.hangel@meduniwien.ac.at.
Cornelius CadrienHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Philipp LazenHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Julia FurtnerDivision of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Alexandra LipkaHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria.
Eva HečkováHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Lukas HingerlHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Stanislav MotykaHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Stephan GruberHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Bernhard StrasserHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Barbara KieselDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Mario MischkulnigDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Matthias PreusserDivision of Oncology, Department of Inner Medicine I, Medical University of Vienna, Vienna, Austria.
Thomas RoetzerDivision of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria.
Adelheid WöhrerDivision of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria.
Georg WidhalmDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Karl RösslerDepartment of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Siegfried TrattnigHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria.
Wolfgang BognerHigh-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Medical University of Vienna · ATChristian Doppler Laboratory for Thermoelectricity · ATAthinoula A. Martinos Center for Biomedical Imaging · US

Funding

Austrian Science Fund FWF KLI 646
6 · The paper itself

Abstract

objectivesSuccessful neurosurgical intervention in gliomas depends on the precision of the preoperative definition of the tumor and its margins since a safe maximum resection translates into a better patient outcome. Metabolic high-resolution imaging might result in improved presurgical tumor characterization, and thus optimized glioma resection. To this end, we validated the performance of a fast high-resolution whole-brain 3D-magnetic resonance spectroscopic imaging (MRSI) method at 7T in a patient cohort of 23 high-grade gliomas (HGG). MATERIALS AND

methodsWe preoperatively measured 23 patients with histologically verified HGGs (17 male, 8 female, age 53 ± 15) with an MRSI sequence based on concentric ring trajectories with a 64 × 64 × 39 measurement matrix, and a 3.4 × 3.4 × 3.4 mm

resultsEighteen of the patient measurements were considered usable. In these patients, ten metabolites were quantified with acceptable quality. Gln, Gly, and tCho were increased and NAA and tCr decreased in nearly all tumor regions, with other metabolites such as serine, showing mixed trends. Overall, there was a reliable characterization of metabolic tumor areas. We also found heterogeneity in the metabolic images often continued into the peritumoral region. While 2HG could not be satisfyingly quantified, we found an increase of Glu in the contrast-enhancing region of IDH-wildtype HGGs and a decrease of Glu in IDH1-mutant HGGs.

conclusionsWe successfully demonstrated high-resolution 7T 3D-MRSI in HGG patients, showing metabolic differences between tumor regions and peritumoral tissue for multiple metabolites. Increases of tCho, Gln (related to tumor metabolism), Gly (related to tumor proliferation), as well as decreases in NAA, tCr, and others, corresponded very well to clinical tumor segmentation, but were more heterogeneous and often extended into the peritumoral region.

Indexed as

Brain NeoplasmsGliomaAdultAgedBrainFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedReproducibility of Results7 TeslaConcentric circle trajectoriesGlycineHigh-grade gliomaMagnetic resonance spectroscopic imagingMetabolic imaging

Identifiers

PMID32977210
PMCPMC7511769
OpenAlexW3089309785

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.