Evidence map›Paper›PMID 31392385›Full record

ArticleNeuroradiology2019

Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models.

Marianna Inglese, Katherine L Ordidge, Lesley Honeyfield, Tara D Barwick, Eric O Aboagye, Adam D Waldman, Matthew Grech-Sollars

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

  1. Pooled it
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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

7 authors.

Marianna IngleseDepartment of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK. marianna.inglese17@imperial.ac.uk.ORCID http://orcid.org/0000-0002-9154-8260
Katherine L OrdidgeDepartment of Imaging, Imperial College Healthcare NHS Trust, London, UK.
Lesley HoneyfieldDepartment of Imaging, Imperial College Healthcare NHS Trust, London, UK.
Tara D BarwickDepartment of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.
Eric O AboagyeDepartment of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.
Adam D WaldmanDepartment of Medicine, Imperial College London, London, UK.
Matthew Grech-SollarsDepartment of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test.

methodsDCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV).

resultsThe model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of K

conclusionsThe majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.

Indexed as

AdultAgedBrain NeoplasmsContrast MediaFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedReproducibility of ResultsContrast MediaDCE-MRIGliomaPrimary brain tumourShutter speed modelTofts model

Identifiers

PMID31392385
PMCPMC6848046

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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.