Evidence map›Paper›PMID 19276457›Full record

SynthesisJournal of the National Cancer Institute2009

Prevention of breast cancer in postmenopausal women: approaches to estimating and reducing risk.

Steven R Cummings, Jeffrey A Tice, Scott Bauer, Warren S Browner, Jack Cuzick, Elad Ziv, Victor Vogel, John Shepherd, Celine Vachon, Rebecca Smith-Bindman and 1 more

Open access · bronzeAbstract readMeta-AnalysisReview
In one paragraph

Synthesis in Journal of the National Cancer Institute, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
107citing papers in PubMed, 3 pooled it
15.8field-weighted citation impact, top 1% 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

107 citing papers in PubMed, 3 syntheses or guidelines pooled it, 253 citations in OpenAlex.

  1. Pooled it
  2. A novel automated mammographic density measure and breast cancer risk.Journal of the National Cancer Institute · 2012
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  4. Kernel Density Estimation as a Measure of Environmental Exposure Related to Insulin Resistance in Breast Cancer Survivors.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2017
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47 more citing papers are in PubMed but not listed here.

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

11 authors at 6 institutions in 2 countries.

Steven R CummingsSan Francisco Coordinating Center, California Pacific Medical Center Research Institute, 94107, USA. scummings@sfcc-cpmc.net
Jeffrey A Tice
Scott Bauer
Warren S Browner
Jack Cuzick
Elad Ziv
Victor Vogel
John Shepherd
Celine Vachon
Rebecca Smith-Bindman
Karla Kerlikowske
Magee-Womens Research Institute · USMayo Clinic in Arizona · USUniversity of California, Los Angeles · USUniversity of California, San Francisco · USUniversity of Pittsburgh · USCalifornia Pacific Medical Center · US

Funding

San Francisco Mammography Registry-A Research SourceU01CA063740 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KERLIKOWSKE, KARLA M · 1994 to 2011
$6.2M
NCI NIH HHS CA63740
6 · The paper itself

Abstract

backgroundIt is uncertain whether evidence supports routinely estimating a postmenopausal woman's risk of breast cancer and intervening to reduce risk.

methodsWe systematically reviewed prospective studies about models and sex hormone levels to assess breast cancer risk and used meta-analysis with random effects models to summarize the predictive accuracy of breast density. We also reviewed prospective studies of the effects of exercise, weight management, healthy diet, moderate alcohol consumption, and fruit and vegetable intake on breast cancer risk, and used random effects models for a meta-analyses of tamoxifen and raloxifene for primary prevention of breast cancer. All studies reviewed were published before June 2008, and all statistical tests were two-sided.

resultsRisk models that are based on demographic characteristics and medical history had modest discriminatory accuracy for estimating breast cancer risk (c-statistics range = 0.58-0.63). Breast density was strongly associated with breast cancer (relative risk [RR] = 4.03, 95% confidence interval [CI] = 3.10 to 5.26, for Breast Imaging Reporting and Data System category IV vs category I; RR = 4.20, 95% CI = 3.61 to 4.89, for >75% vs <5% of dense area), and adding breast density to models improved discriminatory accuracy (c-statistics range = 0.63-0.66). Estradiol was also associated with breast cancer (RR range = 2.0-2.9, comparing the highest vs lowest quintile of estradiol, P < .01). Most studies found that exercise, weight reduction, low-fat diet, and reduced alcohol intake were associated with a decreased risk of breast cancer. Tamoxifen and raloxifene reduced the risk of estrogen receptor-positive invasive breast cancer and invasive breast cancer overall.

conclusionsEvidence from this study supports screening for breast cancer risk in all postmenopausal women by use of risk factors and breast density and considering chemoprevention for those found to be at high risk. Several lifestyle changes with the potential to prevent breast cancer should be recommended regardless of risk.

Indexed as

Models, StatisticalPostmenopauseRisk Reduction BehaviorAgedAntineoplastic Agents, HormonalBiomarkers, TumorBreastBreast NeoplasmsCase-Control StudiesConfidence IntervalsConfounding Factors, EpidemiologicEstrogen Receptor ModulatorsFemaleGonadal Steroid HormonesHumansLife StyleAntineoplastic Agents, HormonalBiomarkers, TumorEstrogen Receptor ModulatorsGonadal Steroid HormonesRaloxifene HydrochlorideTamoxifen

Identifiers

PMID19276457
PMCPMC2720698
OpenAlexW2121198301

What OpenQuestion holds

Textmetadata
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.