Evidence map›Paper›PMID 29570895›Full record

ArticleJournal of magnetic resonance imaging : JMRI2018

Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting.

Anna G Sorace, Chengyue Wu, Stephanie L Barnes, Angela M Jarrett, Sarah Avery, Debra Patt, Boone Goodgame, Jeffery J Luci, Hakmook Kang, Richard G Abramson and 2 more

Abstract read
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
4.8field-weighted citation impact, top 4% 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

33 citing papers in PubMed, 59 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Quantitative [Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Bias, Repeatability and Reproducibility of Liver TJournal of magnetic resonance imaging : JMRI · 2022
    Observational
  13. Article
  14. Article
  15. Article
  16. Review
  17. Impact of AlternateTomography (Ann Arbor, Mich.) · 2022
    Article
  18. Review
  19. Article
  20. 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

12 authors at 6 institutions in 1 country.

Anna G SoraceDepartment of Diagnostic Medicine, University of Texas at Austin, Austin, Texas, USA.ORCID http://orcid.org/0000-0002-3640-3966
Chengyue WuDepartment of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA.
Stephanie L BarnesDepartment of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA.
Angela M JarrettInstitute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA.
Sarah AveryAustin Radiological Association, Austin, Texas, USA.
Debra PattTexas Oncology, Austin, Texas, USA.
Boone GoodgameSeton Hospital, Austin, Texas, USA.
Jeffery J LuciDepartment of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA.
Hakmook KangDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Richard G AbramsonDepartment of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Thomas E YankeelovDepartment of Diagnostic Medicine, University of Texas at Austin, Austin, Texas, USA.
John VirostkoDepartment of Diagnostic Medicine, University of Texas at Austin, Austin, Texas, USA.
The University of Texas at Austin · USLivestrong Foundation · USVanderbilt University Medical Center · USSeton Healthcare Family · USTexas Medical Association · USTexas Oncology · US

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
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
NCI NIH HHS U01 CA142565NCI NIH HHS U01 CA174706
6 · The paper itself

Abstract

backgroundQuantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer. PURPOSE/HYPOTHESIS: To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and T STUDY TYPE: Prospective. SUBJECTS/PHANTOM: Ice-water DW-MRI and T FIELD STRENGTH/SEQUENCE: 3T Siemens Skyra MRI quantitative DW-MRI and T ASSESSMENT: Quantitative DW-MRI and T STATISTICAL TESTS: Average values of breast tissue were quantified and Bland-Altman analysis was performed to assess the repeatability of the MRI techniques, while the Friedman test assessed reproducibility.

resultsADC measurements were reproducible across sites, with an average difference of 1.6% in an ice-water phantom and 7.0% in breast fibroglandular tissue. T DATA

conclusionCommunity radiology practices can perform repeatable, reproducible, and accurate quantitative T LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Indexed as

breast cancerdiffusion-weighted MRIdynamic contrast-enhanced MRIT1-mapping

Identifiers

PMID29570895
PMCPMC6151298
OpenAlexW2791561970

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.