Evidence map›Paper›PMID 29630755›Full record

Trial reportJournal of magnetic resonance imaging : JMRI2018

Reproducible automated breast density measure with no ionizing radiation using fat-water decomposition MRI.

Jie Ding, Alison T Stopeck, Yi Gao, Marilyn T Marron, Betsy C Wertheim, Maria I Altbach, Jean-Philippe Galons, Denise J Roe, Fang Wang, Gertraud Maskarinec and 3 more

Open access · greenAbstract readRandomized Controlled Trial
In one paragraph

Trial report 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 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.4field-weighted citation impact, top 11% 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, 27 citations in OpenAlex.

  1. Trial
  2. Sulindac, a Nonselective NSAID, Reduces Breast Density in Postmenopausal Women with Breast Cancer Treated with Aromatase Inhibitors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021
    Trial
  3. Concurrent water TMagnetic resonance imaging · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Preoperative prediction of lymph node metastasis using deep learning-based features.Visual computing for industry, biomedicine, and art · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Background parenchymal enhancement on breast MRI: A comprehensive review.Journal of magnetic resonance imaging : JMRI · 2020
    Review
  15. Article
  16. Review
  17. 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

13 authors at 5 institutions in 2 countries.

Jie DingDepartment of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.
Alison T StopeckDepartment of Hematology and Oncology, Stony Brook Medicine, Stony Brook, New York, USA.ORCID 0000-0003-4762-1392
Yi GaoSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.ORCID 0000-0003-0094-8136
Marilyn T MarronUniversity of Arizona Cancer Center, Tucson, Arizona, USA.
Betsy C WertheimUniversity of Arizona Cancer Center, Tucson, Arizona, USA.
Maria I AltbachUniversity of Arizona Cancer Center, Tucson, Arizona, USA.
Jean-Philippe GalonsUniversity of Arizona Cancer Center, Tucson, Arizona, USA.
Denise J RoeUniversity of Arizona Cancer Center, Tucson, Arizona, USA.
Fang WangStony Brook University Cancer Center, Stony Brook, New York, USA.
Gertraud MaskarinecUniversity of Hawaii Cancer Center, Honolulu, Hawaii, USA.
Cynthia A ThomsonUniversity of Arizona Cancer Center, Tucson, Arizona, USA.
Patricia A ThompsonStony Brook University Cancer Center, Stony Brook, New York, USA.
Chuan HuangDepartment of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.ORCID 0000-0001-6052-0663
University of Arizona · USStony Brook Medicine · USStony Brook University · USShenzhen University Health Science Center · CNUniversity of Hawaiʻi at Mānoa · US

Funding

VITAMIN AP30CA023074 · NCI · UNIVERSITY OF ARIZONA · PI Teri L Bankhead · 1985 to 2026
$110.2M
Evaluation of Di-indolylmethane supplementation to modulate tamoxifen efficacy inR01CA149417 · NCI · UNIVERSITY OF ARIZONA · PI THOMSON, CYNTHIA A. · 2010 to 2014
$2.5M
Nsaid Effects on Clinical and Imaging Breast BiomarkersR01CA161534 · NCI · UNIVERSITY OF ARIZONA · PI STOPECK, ALISON T., THOMPSON, PATRICIA ANN · 2012 to 2016
$2.4M
Early Treatment Response Detection Using Highly Accurate and Reliable Breast Density Measure Derived From MRIR03CA223052 · NCI · STATE UNIVERSITY NEW YORK STONY BROOK · PI HUANG, CHUAN · 2018 to 2019
$157k
Carol M. Baldwin Breast Cancer Research AwardsNational Institutes of Health (NIH) R01CA149417National Institutes of Health (NIH) R01CA161534National Institutes of Health (NIH) R03CA223052National Natural Science Foundation of China 61601302NCI NIH HHS P30 CA023074NCI NIH HHS R01 CA149417NCI NIH HHS R01 CA161534NCI NIH HHS R03 CA223052
6 · The paper itself

Abstract

backgroundIncreased breast density is a significant independent risk factor for breast cancer, and recent studies show that this risk is modifiable. Hence, breast density measures sensitive to small changes are desired. PURPOSE: Utilizing fat-water decomposition MRI, we propose an automated, reproducible breast density measurement, which is nonionizing and directly comparable to mammographic density (MD). STUDY TYPE: Retrospective study. POPULATION: The study included two sample sets of breast cancer patients enrolled in a clinical trial, for concordance analysis with MD (40 patients) and reproducibility analysis (10 patients). FIELD STRENGTH/SEQUENCE: The majority of MRI scans (59 scans) were performed with a 1.5T GE Signa scanner using radial IDEAL-GRASE sequence, while the remaining (seven scans) were performed with a 3T Siemens Skyra using 3D Cartesian 6-echo GRE sequence with a similar fat-water separation technique. ASSESSMENT: After automated breast segmentation, breast density was calculated using FraGW, a new measure developed to reliably reflect the amount of fibroglandular tissue and total water content in the entire breast. Based on its concordance with MD, FraGW was calibrated to MR-based breast density (MRD) to be comparable to MD. A previous breast density measurement, Fra80-the ratio of breast voxels with <80% fat fraction-was also calculated for comparison with FraGW. STATISTICAL TESTS: Pearson correlation was performed between MD (reference standard) and FraGW (and Fra80). Test-retest reproducibility of MRD was evaluated using the difference between test-retest measures (Δ

resultsBoth FraGW and Fra80 were strongly correlated with MD (Pearson ρ: 0.96 vs. 0.90, both P < 0.0001). MRD converted from FraGW showed higher test-retest reproducibility (Δ DATA

conclusionThe proposed MRD is directly comparable with MD and highly reproducible, which enables the early detection of small breast density changes and treatment response. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:971-981.

Indexed as

Breast DensityMagnetic Resonance ImagingRadiation, IonizingAdipose TissueBreastBreast NeoplasmsFemaleHumansImage Processing, Computer-AssistedMammographyPattern Recognition, AutomatedReproducibility of ResultsRetrospective StudiesRisk FactorsTamoxifenWaterTamoxifenWaterbreast cancerbreast cancer preventionbreast densityfat-water decomposition MRIrisk biomarker

Identifiers

PMID29630755
PMCPMC6173993
OpenAlexW2796928390

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.