Evidence map›Paper›PMID 26520725›Full record

ArticleMedical physics2015

Breast dose in mammography is about 30% lower when realistic heterogeneous glandular distributions are considered.

Andrew M Hernandez, J Anthony Seibert, John M Boone

Open access · bronzeAbstract read
In one paragraph

Article in Medical physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
5.0field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

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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

20 citing papers in PubMed, 76 citations in OpenAlex.

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  16. Monte Carlo Basics for Radiation Dose Assessment in Diagnostic Radiology.Journal of the American College of Radiology : JACR · 2017
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  17. Article
  18. Review
  19. Dosimetry in x-ray-based breast imaging.Physics in medicine and biology · 2016
    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

3 authors at 1 institution in 1 country.

Andrew M HernandezBiomedical Engineering Graduate Group, University of California Davis, Sacramento, California 95817.
J Anthony SeibertDepartments of Radiology and Biomedical Engineering, Biomedical Engineering Graduate Group, University of California Davis, Sacramento, California 95817.
John M BooneDepartments of Radiology and Biomedical Engineering, Biomedical Engineering Graduate Group, University of California Davis, Sacramento, California 95817.
University of California, Davis · US

Funding

Breast CT scanner for earlier cancer detectionR01EB002138 · NIBIB · UNIVERSITY OF CALIFORNIA DAVIS · PI BOONE, JOHN M · 2003 to 2012
$9.0M
Breast CT: Final Steps to TranslationR01CA181081 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BOONE, JOHN M · 2015 to 2019
$2.9M
NCI NIH HHS R01 CA181081NIBIB NIH HHS R01 EB002138
6 · The paper itself

Abstract

purposeCurrent dosimetry methods in mammography assume that the breast is comprised of a homogeneous mixture of glandular and adipose tissues. Three-dimensional (3D) dedicated breast CT (bCT) data sets were used previously to assess the complex anatomical structure within the breast, characterizing the statistical distribution of glandular tissue in the breast. The purpose of this work was to investigate the effect of bCT-derived heterogeneous glandular distributions on dosimetry in mammography.

methodsbCT-derived breast diameters, volumes, and 3D fibroglandular distributions were used to design realistic compressed breast models comprised of heterogeneous distributions of glandular tissue. The bCT-derived glandular distributions were fit to biGaussian functions and used as probability density maps to assign the density distributions within compressed breast models. The MCNPX 2.6.0 Monte Carlo code was used to estimate monoenergetic normalized mean glandular dose "DgN(E)" values in mammography geometry. The DgN(E) values were then weighted by typical mammography x-ray spectra to determine polyenergetic DgN (pDgN) coefficients for heterogeneous (pDgNhetero) and homogeneous (pDgNhomo) cases. The dependence of estimated pDgN values on phantom size, volumetric glandular fraction (VGF), x-ray technique factors, and location of the heterogeneous glandular distributions was investigated.

resultsThe pDgNhetero coefficients were on average 35.3% (SD, 4.1) and 24.2% (SD, 3.0) lower than the pDgNhomo coefficients for the Mo-Mo and W-Rh x-ray spectra, respectively, across all phantom sizes and VGFs when the glandular distributions were centered within the breast phantom in the coronal plane. At constant breast size, increasing VGF from 7.3% to 19.1% lead to a reduction in pDgNhetero relative to pDgNhomo of 23.6%-27.4% for a W-Rh spectrum. Displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width in the superior and inferior directions, resulted in a 37.3% and a -26.6% change in the pDgNhetero coefficient, respectively, relative to the centered distribution for the Mo-Mo spectrum. Lateral displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width, resulted in a 1.5% change in the pDgNhetero coefficient relative to the centered distribution for the W-Rh spectrum.

conclusionsIntroducing bCT-derived heterogeneous glandular distributions into mammography phantom design resulted in decreased glandular dose relative to the widely used homogeneous assumption. A homogeneous distribution overestimates the amount of glandular tissue near the entrant surface of the breast, where dose deposition is exponentially higher. While these findings are based on clinically measured distributions of glandular tissue using a large cohort of women, future work is required to improve the classification of glandular distributions based on breast size and overall glandular fraction.

Indexed as

Models, BiologicalAbsorption, RadiationAdultAgedAged, 80 and overBreastComputer SimulationFemaleHumansMammographyMiddle AgedModels, StatisticalRadiation DosageRadiation ExposureRadiation ProtectionRadiometry

Identifiers

PMID26520725
PMCPMC4600085
OpenAlexW1901193091

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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.