Evidence map›Paper›PMID 30167373›Full record

ArticleNeuroImage. Clinical2018

Quantification and visualization of lipid landscape in glioma using in -and opposed-phase imaging.

Pohchoo Seow, Vairavan Narayanan, Aditya Tri Hernowo, Jeannie Hsiu Ding Wong, Norlisah Ramli

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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

4 citing papers in PubMed.

  1. Observational
  2. Review
  3. Neurophotonics · 2020
    Article
  4. 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

5 authors.

Pohchoo SeowDepartment of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Vairavan NarayananDivision of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Aditya Tri HernowoDivision of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Jeannie Hsiu Ding WongDepartment of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Norlisah RamliDepartment of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia. Electronic address: norlisah@ummc.edu.my.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study maps the lipid distributions based on magnetic resonance imaging (MRI) in-and opposed-phase (IOP) sequence and correlates the findings generated from lipid map to histological grading of glioma. Methods: Forty histologically proven glioma patients underwent a standard MRI tumour protocol with the addition of IOP sequence. The regions of tumour (solid enhancing, solid non-enhancing, and cystic regions) were delineated using snake model (ITK-SNAP) with reference to structural and diffusion MRI images. The lipid distribution map was constructed based on signal loss ratio (SLR) obtained from the IOP imaging. The mean SLR values of the regions were computed and compared across the different glioma grades. Results: The solid enhancing region of glioma had the highest SLR for both Grade II and III. The mean SLR of solid non-enhancing region of tumour demonstrated statistically significant difference between the WHO grades (grades II, III & IV) (mean SLR Conclusion: Lipid quantification via lipid map provides useful information on lipid landscape in tumour heterogeneity characterisation of glioma. This technique adds to the surgical diagnostic yield by identifying biopsy targets. It can also be used as an adjunct grading tool for glioma as well as to provide information about lipidomics landscape in glioma development.

Indexed as

Lipid MetabolismLipidsAdolescentAdultAgedBrain NeoplasmsChildFemaleGliomaHumansMagnetic Resonance ImagingMaleMiddle AgedProspective StudiesRetrospective StudiesYoung AdultLipidsGliomaGradingIn- and opposed phaseLipid distributionLipid mapSignal loss ratio

Identifiers

PMID30167373
PMCPMC6111041

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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.