Evidence map›Paper›PMID 33501657›Full record

ArticleMagnetic resonance in medicine2021

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck: Bayesian probability versus neural network.

Thomas Koopman, Roland Martens, Oliver J Gurney-Champion, Maqsood Yaqub, Cristina Lavini, Pim de Graaf, Jonas Castelijns, Ronald Boellaard, J Tim Marcus

Open access · hybridAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

17 citing papers in PubMed, 30 citations in OpenAlex.

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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

9 authors at 3 institutions in 1 country.

Thomas KoopmanDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0003-3123-5278
Roland MartensDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0002-8297-6802
Oliver J Gurney-ChampionDepartment of Radiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0003-1750-6617
Maqsood YaqubDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0003-2122-740X
Cristina LaviniDepartment of Radiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Pim de GraafDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0003-1938-0747
Jonas CastelijnsDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0001-9495-9797
Ronald BoellaardDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0002-0313-5686
J Tim MarcusDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0001-9948-6407
Amsterdam University Medical Centers · NLThe Netherlands Cancer Institute · NLUniversity Medical Center Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least-squares regression: Bayesian probability estimation, a recently introduced neural network approach, IVIM-NET, and a version of the neural network modified to increase consistency, IVIM-NET

methodsTen healthy volunteers underwent two imaging sessions of the neck, two weeks apart, with two DWI acquisitions per session. Model parameters (ADC, diffusion coefficient

resultsThe Bayesian and neural network approaches outperformed nonlinear regression in terms of wCV. Intersession wCV of

conclusionRepeatabilities from the Bayesian and neural network approaches are superior to that of nonlinear regression for estimating IVIM parameters in the head and neck.

Indexed as

Diffusion Magnetic Resonance ImagingNeural Networks, ComputerBayes TheoremBiomarkersHumansMotionReproducibility of ResultsBiomarkersdiffusion magnetic resonance imaginghead and neck neoplasmsrepeatability

Identifiers

PMID33501657
PMCPMC7986193
OpenAlexW3123518068

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.