Evidence map›Paper›PMID 26526945›Full record

ReviewBrain pathology (Zurich, Switzerland)2015

Magnetic Resonance (MR) Metabolic Imaging in Glioma.

Myriam M Chaumeil, Janine M Lupo, Sabrina M Ronen

Registry-linked trialOpen access · greenAbstract readReview
In one paragraph

Review in Brain pathology (Zurich, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04407039 (Establishment and Evaluation of Multimodal Image Recognition System of Glioma Based on Deep Learning), which is not on this map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
4.9field-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.

NCT04407039 unknown statusnot on this mapstarted 2021, after this paper: background citation

Establishment and Evaluation of Multimodal Image Recognition System of Glioma Based on Deep Learning

TypeobservationalSponsorTao XinRan2021 to 2022Enrolled350ConditionsGliomaArmsPET/CT, H-MRS and MRI
3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 43 citations in OpenAlex.

  1. Article
  2. Noninvasive Prediction ofDiagnostics (Basel, Switzerland) · 2026
    Article
  3. Article
  4. Single-Voxel MR Spectroscopy of Gliomas with s-LASER at 7T.Diagnostics (Basel, Switzerland) · 2023
    Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. HyperpolarizedScientific reports · 2021
    Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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

3 authors at 1 institution in 1 country.

Myriam M ChaumeilDepartment of Radiology and Biomedical Imaging, Mission Bay Campus.
Janine M LupoDepartment of Radiology and Biomedical Imaging, Mission Bay Campus.
Sabrina M RonenDepartment of Radiology and Biomedical Imaging, Mission Bay Campus.
Neurological Surgery · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review is focused on describing the use of magnetic resonance (MR) spectroscopy for metabolic imaging of brain tumors. We will first review the MR metabolic imaging findings generated from preclinical models, focusing primarily on in vivo studies, and will then describe the use of metabolic imaging in the clinical setting. We will address relatively well-established (1) H MRS approaches, as well as (31) P MRS, (13) C MRS and emerging hyperpolarized (13) C MRS methodologies, and will describe the use of metabolic imaging for understanding the basic biology of glioma as well as for improving the characterization and monitoring of brain tumors in the clinic.

Indexed as

AnimalsBrain NeoplasmsGliomaHumansMagnetic Resonance Spectroscopybrain tumorsgliomamagnetic resonance imagingmetabolic imaging

Identifiers

PMID26526945
PMCPMC8029127
OpenAlexW2162813222

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.