Evidence map›Paper›PMID 29115690›Full record

ArticleMagnetic resonance in medicine2018

The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains.

Ryan T Woodall, Stephanie L Barnes, David A Hormuth, Anna G Sorace, C Chad Quarles, Thomas E Yankeelov

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Modeling of factors affecting late gadolinium enhancement kinetics in MRI of cardiac amyloid.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023
    Article
  3. Review
  4. Article
  5. 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

6 authors.

Ryan T WoodallDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA.
Stephanie L BarnesCenter for Computational Oncology, Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas, USA.
David A HormuthCenter for Computational Oncology, Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, Texas, USA.
Anna G SoraceDepartment of Internal Medicine, The University of Texas at Austin, Austin, Texas, USA.
C Chad QuarlesBarrow Neurological Institute, Phoenix, Arizona, USA.
Thomas E YankeelovDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA.

Funding

Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant TherapyU01CA142565 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI KARCZMAR, GREGORY S., NANDA, RITA · 2010 to 2019
$4.5M
MRI Assessment of Tumor Perfusion, Permeability and CellularityR01CA158079 · NCI · VANDERBILT UNIVERSITY · PI QUARLES, CHRISTOPHER CHAD · 2011 to 2021
$3.5M
Quantitative Multiscale Imaging to Optimize Cancer Treatment StrategiesR01CA186193 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, RERICHA, ERIN C · 2014 to 2019
$3.1M
Comprehensive Training Program in Imaging Science and InformaticsT32EB007507 · NIBIB · UNIVERSITY OF TEXAS AT AUSTIN · PI MARKEY, MIA K, RYLANDER, HENRY GRADY · 2009 to 2024
$2.7M
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast CancerU01CA174706 · NCI · VANDERBILT UNIVERSITY · PI QUARANTA, VITO, YANKEELOV, THOMAS E · 2013 to 2018
$2.5M
Evaluation and Validation of Imaging Biomarkers of Tumor Response to TreatmentR01CA138599 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI MCINTYRE, JAMES OLIVER · 2010 to 2015
$2.2M
NCI NIH HHS R01 CA138599NCI NIH HHS R01 CA158079NCI NIH HHS R01 CA186193NCI NIH HHS U01 CA142565NCI NIH HHS U01 CA174706NIBIB NIH HHS T32 EB007507
6 · The paper itself

Abstract

purposeQuantitative evaluation of dynamic contrast enhanced MRI (DCE-MRI) allows for estimating perfusion, vessel permeability, and tissue volume fractions by fitting signal intensity curves to pharmacokinetic models. These compart mental models assume rapid equilibration of contrast agent within each voxel. However, there is increasing evidence that this assumption is violated for small molecular weight gadolinium chelates. To evaluate the error introduced by this invalid assumption, we simulated DCE-MRI experiments with volume fractions computed from entire histological tumor cross-sections obtained from murine studies.

methodsA 2D finite element model of a diffusion-compensated Tofts-Kety model was developed to simulate dynamic T

resultsResults indicated parameterization errors of -19.1% ± 10.6% in K

conclusionThese results indicate a need for revising the standard model of DCE-MRI to incorporate a correction for slow diffusion of contrast agent. Magn Reson Med 80:330-340, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Indexed as

Magnetic Resonance ImagingAnimalsChelating AgentsComputer SimulationContrast MediaDiffusionFemaleFinite Element AnalysisGadoliniumGadolinium DTPAImage EnhancementImage Processing, Computer-AssistedMethodsMiceMice, NudeNeoplasmsChelating AgentsContrast MediaGadoliniumGadolinium DTPAcancerDCE-MRIdiffusionkineticsmodelingtumor

Identifiers

PMID29115690
PMCPMC5876107

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.