Evidence map›Paper›PMID 34105184›Full record

ArticleMagnetic resonance in medicine2021

Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients.

Misha P T Kaandorp, Sebastiano Barbieri, Remy Klaassen, Hanneke W M van Laarhoven, Hans Crezee, Peter T While, Aart J Nederveen, Oliver J Gurney-Champion

Abstract 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 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

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  12. Optimization of the Data Pattern and Analysis Algorithm for the T2-based Water Suppression Diffusion MRImaging (T2wsup-dMRI) Technique.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
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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

8 authors.

Misha P T KaandorpDepartment of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0002-7340-8256
Sebastiano BarbieriCentre for Big Data Research in Health, UNSW, Sydney, Australia.ORCID 0000-0002-5919-372X
Remy KlaassenDepartment of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Hanneke W M van LaarhovenDepartment of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0003-3546-9709
Hans CrezeeDepartment of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Peter T WhileDepartment of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.ORCID 0000-0003-2602-0758
Aart J NederveenDepartment of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
Oliver J Gurney-ChampionDepartment of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.ORCID 0000-0003-1750-6617

Funding

KWF Kankerbestrijding KWF-UVA 18410KWF Kankerbestrijding KWF-UVA 2013.5932Norges Forskningsråd FRIPRO Researcher Project 302624
6 · The paper itself

Abstract

purposeEarlier work showed that IVIM-NET

methodIn simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CV

resultsIn simulations (SNR = 20), IVIM-NET

conclusionIVIM-NET

Indexed as

Deep LearningPancreatic NeoplasmsAlgorithmsBayes TheoremDiffusion Magnetic Resonance ImagingHumansMotionPhysicsReproducibility of Resultsdeep neural networkdiffusion-weighted magnetic resonance imagingintravoxel incoherent motionIVIMpancreatic cancerunsupervised physics-informed deep learning

Identifiers

PMID34105184
PMCPMC8362093

What OpenQuestion holds

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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.