Evidence map›Paper›PMID 35449515›Full record

SynthesisFrontiers in neurology2022

Ultra-High-Field MRI in the Diagnosis and Management of Gliomas: A Systematic Review.

Annabelle Shaffer, Susanna S Kwok, Anant Naik, Aaron T Anderson, Fan Lam, Tracey Wszalek, Paul M Arnold, Wael Hassaneen

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024
    Pooled it
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Exploring theFrontiers in neuroscience · 2025
    Article
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
  19. Imaging Findings ofNuclear medicine and molecular imaging · 2022
    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

8 authors.

Annabelle ShafferCarle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Susanna S KwokCarle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Anant NaikCarle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Aaron T AndersonBeckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Fan LamBeckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Tracey WszalekBeckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Paul M ArnoldCarle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Wael HassaneenCarle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, United States.

Funding

Towards In Vivo Imaging of Tissue MetabolomicsR35GM142969 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Fan Lam · 2021 to 2026
$2.4M
NIGMS NIH HHS R35 GM142969
6 · The paper itself

Abstract

Importance: Gliomas, tumors of the central nervous system, are classically diagnosed through invasive surgical biopsy and subsequent histopathological study. Innovations in ultra-high field (UHF) imaging, namely 7-Tesla magnetic resonance imaging (7T MRI) are advancing preoperative tumor grading, visualization of intratumoral structures, and appreciation of small brain structures and lesions. Objective: Summarize current innovative uses of UHF imaging techniques in glioma diagnostics and treatment. Methods: A systematic review in accordance with PRISMA guidelines was performed utilizing PubMed. Case reports and series, observational clinical trials, and randomized clinical trials written in English were included. After removing unrelated studies and those with non-human subjects, only those related to 7T MRI were independently reviewed and summarized for data extraction. Some preclinical animal models are briefly described to demonstrate future usages of ultra-high-field imaging. Results: We reviewed 46 studies (43 human and 3 animal models) which reported clinical usages of UHF MRI in the diagnosis and management of gliomas. Current literature generally supports greater resolution imaging from 7T compared to 1.5T or 3T MRI, improving visualization of cerebral microbleeds and white and gray matter, and providing more precise localization for radiotherapy targeting. Additionally, studies found that diffusion or susceptibility-weighted imaging techniques applied to 7T MRI, may be used to predict tumor grade, reveal intratumoral structures such as neovasculature and microstructures like axons, and indicate isocitrate dehydrogenase 1 mutation status in preoperative imaging. Similarly, newer imaging techniques such as magnetic resonance spectroscopy and chemical exchange saturation transfer imaging can be performed on 7T MRI to predict tumor grading and treatment efficacy. Geometrical distortion, a known challenge of 7T MRI, was at a tolerable level in all included studies. Conclusion: UHF imaging has the potential to preoperatively and non-invasively grade gliomas, provide precise therapy target areas, and visualize lesions not seen on conventional MRI.

Indexed as

3-Tesla MRI7-Tesla MRIbrain tumorsglioblastomagliomasultra-high field imagingultra-high field MRI

Identifiers

PMID35449515
PMCPMC9016277

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.