Evidence map›Paper›PMID 25139935›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2014

Comparison of mammographic density assessed as volumes and areas among women undergoing diagnostic image-guided breast biopsy.

Gretchen L Gierach, Berta M Geller, John A Shepherd, Deesha A Patel, Pamela M Vacek, Donald L Weaver, Rachael E Chicoine, Ruth M Pfeiffer, Bo Fan, Amir Pasha Mahmoudzadeh and 5 more

Registry-linked trialAbstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00475761 (Breast Radiology Evaluation and Study of Tissues), which is not on this map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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.

NCT00475761 completednot on this map

Breast Radiology Evaluation and Study of Tissues (Breast) Stamp Project

TypeobservationalSponsorNational Cancer Institute (NCI)Ran2007Enrolled466ConditionsBreast Neoplasms
3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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  9. Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2018
    Article
  10. Adiposity, breast density, and breast cancer risk: epidemiological and biological considerations.European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP) · 2017
    Review
  11. Article
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  18. The effect of change in body mass index on volumetric measures of mammographic density.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2015
    Article
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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

15 authors.

Gretchen L GierachHormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland. gierachg@mail.nih.gov.
Berta M GellerUniversity of Vermont, Burlington, Vermont.
John A ShepherdUniversity of California, San Francisco, San Francisco, California.
Deesha A PatelHormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland.
Pamela M VacekUniversity of Vermont, Burlington, Vermont.
Donald L WeaverUniversity of Vermont, Burlington, Vermont.
Rachael E ChicoineUniversity of Vermont, Burlington, Vermont.
Ruth M PfeifferBiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland.
Bo FanUniversity of California, San Francisco, San Francisco, California.
Amir Pasha MahmoudzadehUniversity of California, San Francisco, San Francisco, California.
Jeff WangUniversity of California, San Francisco, San Francisco, California.
Jason M JohnsonUniversity of Vermont, Burlington, Vermont.
Sally D HerschornUniversity of Vermont, Burlington, Vermont.
Louise A BrintonHormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, Maryland.
Mark E ShermanBreast and Gynecologic Cancer Research Group, Division of Cancer Prevention, National Cancer Institute, NIH, Bethesda, Maryland.

Funding

Hormone-Related CancersZIACP010126 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI GIERACH, GRETCHEN · 2009 to 2025
$103.5M
VERMONT BREAST CANCER SURVEILLANCE SYSTEMU01CA070013 · NCI · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI GELLER, BERTA M · 1995 to 2011
$6.7M
Subregional Breast Density and Cancer RiskR21CA157254 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SHEPHERD, JOHN ALAN · 2012 to 2013
$357k
Intramural NIH HHS ZIA CP010126NCI NIH HHS 1R21CA157254NCI NIH HHS R21 CA157254NCI NIH HHS U01 CA070013NCI NIH HHS U01CA70013
6 · The paper itself

Abstract

backgroundMammographic density (MD), the area of non-fatty-appearing tissue divided by total breast area, is a strong breast cancer risk factor. Most MD analyses have used visual categorizations or computer-assisted quantification, which ignore breast thickness. We explored MD volume and area, using a volumetric approach previously validated as predictive of breast cancer risk, in relation to risk factors among women undergoing breast biopsy.

methodsAmong 413 primarily white women, ages 40 to 65 years, undergoing diagnostic breast biopsies between 2007 and 2010 at an academic facility in Vermont, MD volume (cm(3)) was quantified in craniocaudal views of the breast contralateral to the biopsy target using a density phantom, whereas MD area (cm(2)) was measured on the same digital mammograms using thresholding software. Risk factor associations with continuous MD measurements were evaluated using linear regression.

resultsPercent MD volume and area were correlated (r = 0.81) and strongly and inversely associated with age, body mass index (BMI), and menopause. Both measures were inversely associated with smoking and positively associated with breast biopsy history. Absolute MD measures were correlated (r = 0.46) and inversely related to age and menopause. Whereas absolute dense area was inversely associated with BMI, absolute dense volume was positively associated.

conclusionsVolume and area MD measures exhibit some overlap in risk factor associations, but divergence as well, particularly for BMI. IMPACT: Findings suggest that volume and area density measures differ in subsets of women; notably, among obese women, absolute density was higher with volumetric methods, suggesting that breast cancer risk assessments may vary for these techniques.

Indexed as

AgedAge FactorsBody Mass IndexBreastBreast DensityBreast NeoplasmsCross-Sectional StudiesFemaleHumansImage-Guided BiopsyImage Interpretation, Computer-AssistedMammary Glands, HumanMenopauseMiddle AgedOrgan SizeProspective Studies

Identifiers

PMID25139935
PMCPMC4337788

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Registered trials

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.