Evidence map›Paper›PMID 33208767›Full record

ArticleScientific reports2020

Phased-array combination of 2D MRS for lipid composition quantification in patients with breast cancer.

Vasiliki Mallikourti, Sai Man Cheung, Tanja Gagliardi, Nicholas Senn, Yazan Masannat, Trevor McGoldrick, Ravi Sharma, Steven D Heys, Jiabao He

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 87% 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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. 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

9 authors at 2 institutions in 1 country.

Vasiliki MallikourtiInstitute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, UK. r02vm16@abdn.ac.uk.
Sai Man CheungInstitute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, UK.
Tanja GagliardiInstitute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, UK.
Nicholas SennInstitute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, UK.
Yazan MasannatBreast Unit, Aberdeen Royal Infirmary, Aberdeen, UK.
Trevor McGoldrickDepartment of Oncology, Aberdeen Royal Infirmary, Aberdeen, UK.
Ravi SharmaDepartment of Oncology, Aberdeen Royal Infirmary, Aberdeen, UK.
Steven D HeysInstitute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, UK.
Jiabao HeInstitute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, UK.
University of Aberdeen · GBAberdeen Royal Infirmary · GB

Funding

Cancer Research UK 28312
6 · The paper itself

Abstract

Lipid composition in breast cancer, a central marker of disease progression, can be non-invasively quantified using 2D MRS method of double quantum filtered correlation spectroscopy (DQF-COSY). The low signal to noise ratio (SNR), arising from signal retention of only 25% and depleted lipids within tumour, demands improvement approaches beyond signal averaging for clinically viable applications. We therefore adapted and examined combination algorithms, designed for 1D MRS, for 2D MRS with both internal and external references. Lipid composition spectra were acquired from 17 breast tumour specimens, 15 healthy female volunteers and 25 patients with breast cancer on a clinical 3 T MRI scanner. Whitened singular value decomposition (WSVD) with internal reference yielded maximal SNR with an improvement of 53.3% (40.3-106.9%) in specimens, 84.4 ± 40.6% in volunteers, 96.9 ± 54.2% in peritumoural adipose tissue and 52.4% (25.1-108.0%) in tumours in vivo. Non-uniformity, as variance of improvement across peaks, was low at 21.1% (13.7-28.1%) in specimens, 5.5% (4.2-7.2%) in volunteers, 6.1% (5.0-9.0%) in peritumoural tissue, and 20.7% (17.4-31.7%) in tumours in vivo. The bias (slope) in improvement ranged from - 1.08 to 0.21%/ppm along the diagonal directions. WSVD is therefore the optimal algorithm for lipid composition spectra with highest SNR uniformly across peaks, reducing acquisition time by up to 70% in patients, enabling clinical applications.

Indexed as

AlgorithmsComputer SimulationSignal-To-Noise RatioAdultAgedBreast NeoplasmsCase-Control StudiesFemaleHumansLipidsMagnetic Resonance SpectroscopyMiddle AgedLipids

Identifiers

PMID33208767
PMCPMC7676263
OpenAlexW3080214911

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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.