Mammographic Breast Density as a General Marker of Breast Cancer Risk

Vachon, Celine M.; Brandt, Kathleen R.; Ghosh, Karthik; Scott, Christopher G.; Maloney, Shaun D.; Carston, Michael J.; Pankratz, V. Shane; Sellers, Thomas A.

doi:10.1158/1055-9965.epi-06-0738

Cited by 185 publications

(184 citation statements)

References 40 publications

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“…Dividing the pixels related to fibroglandular tissue by the total number of pixels making up the entire breast allowed for an estimate of percent breast density. This measure has consistently been associated with breast cancer [21,22], and has high intra-observer correlation ([0.95 for our reader on over 700 mediolateral oblique (MLO) and cranial-caudal (CC) images). Breast density assessments were available for 1,384 women: 1,169 with both MLO and CC views, 268 with MLO views only, and 27 with CC views only.…”

Section: Data Collectionmentioning

confidence: 81%

Dietary patterns and breast density in the Minnesota Breast Cancer Family Study

Tseng

Vierkant

Kushi

et al. 2008

Cancer Causes Control

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Objective Whether dietary patterns, rather than single foods or nutrients, are associated with breast density is not known. We investigated this in the Minnesota Breast Cancer Family Study. Methods Participants completed a 153-item food fre quency questionnaire and provided screening mammograms for breast density assessment using a computer-assisted method. We used multivariate linear regression to quantify dietary pattern-breast density associations. Results Among 3,147 women with dietary information, three dietary patterns emerged from principal components analysis: a fruit-vegetable-cereal pattern, a salad-sauce-pasta/ grain pattern, and a meat-starch pattern. Among 1,286 women with breast density estimates, the fruit-vegetable-cereal and salad-sauce-pasta/grain patterns were inversely

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Section: Data Collectionmentioning

confidence: 81%

Dietary patterns and breast density in the Minnesota Breast Cancer Family Study

Tseng

Vierkant

Kushi

et al. 2008

Cancer Causes Control

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“…Use of Cumulus for mammograms acquired in this way is well established, and the inclusion of prior mammograms as well as contralateral breast of diagnostic mammograms provides a similar methodology to published validation of Cumulus for breast cancer risk. 26,87,88 In a study in which mammograms (contralateral breast for cancers, and matched controls) were assigned to density classes, the RR for women in the highest density group associated with radiologist allocation was higher than computer-based allocation using interactive thresholding. 26 However, in this Family History analogue mammography substudy, VAS assessments were not related to cancer risk; this may be due, in part, to interobserver variability dominating any genuine effect in a small evaluation.…”

Section: Discussionmentioning

confidence: 99%

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Evans

Astley

Stavrinos³

et al. 2016

Programme Grants Appl Res

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BackgroundIn the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.ObjectivesTo (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.DesignTwo cohort studies assessing breast cancer incidence.SettingHigh-risk FHC and the NHSBSP Greater Manchester, UK.ParticipantsA total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].InterventionsQuestionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.Main outcome measuresBreast cancer incidence.Data sourcesThe NHSBSP; cancer registration.ResultsA total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.ConclusionsRisk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.Future workA pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.FundingThe National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

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“…One strength of the current study is the semiautomated method to quantify mammographic density that has consistently been shown to be associated with breast cancer (20) and has high intra-reader reliability (12). In addition, a comprehensive pathology review of the tissue was done by a single expert pathologist blinded to density data.…”

Section: Discussionmentioning

confidence: 99%

“…Subjects were selected from a casecontrol study (n = 372 cases, n = 713 controls), which is described in detail elsewhere (12). Briefly, breast cancer cases for the case-control study were women who were older than 40 years, had been diagnosed with primary invasive breast cancer between 1997 and 2001, had at least two prior screening mammograms done 2 years or more before the breast cancer diagnosis, and lived within a 193.12-km (120-mile) radius of the Mayo Clinic in Rochester, Minnesota.…”

Section: Methodsmentioning

confidence: 99%

Association of Mammographic Density with the Pathology of Subsequent Breast Cancer among Postmenopausal Women

Ghosh

Brandt²,

Sellers

et al. 2008

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background: Limited studies have examined the associations between mammographic density and subsequent breast tumor characteristics. Methods: Eligible women were part of a case-control study of postmenopausal breast cancer, were 40 years or older and had a routine mammogram 4 years or more before their diagnosis. Mammographic density (percent density, dense area, and nondense area) was estimated using a computer-assisted thresholding program. At the time of cancer diagnosis, cases were classified as asymptomatic or symptomatic based on medical record review and breast imaging workup. Pathologic review was done blinded to the density status. Linear regression models and tests for trend examined the association between pathologic characteristics of the breast tumor and the components of density for all participants, and stratified by symptom status at diagnosis. Results: Of the 286 eligible cases, 77% were 60 years or older and mean percent density was 29.5% (SD, 14.6%).

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Mammographic Breast Density as a General Marker of Breast Cancer Risk

Cited by 185 publications

References 40 publications

Dietary patterns and breast density in the Minnesota Breast Cancer Family Study

Dietary patterns and breast density in the Minnesota Breast Cancer Family Study

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Association of Mammographic Density with the Pathology of Subsequent Breast Cancer among Postmenopausal Women

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