Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model.

Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California 94143-1732, USA.
Annals of internal medicine (Impact Factor: 16.1). 04/2008; 148(5):337-47.
Source: PubMed

ABSTRACT Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography.
To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density.
Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort.
Screening mammography sites participating in the Breast Cancer Surveillance Consortium.
1,095,484 women undergoing mammography who had no previous diagnosis of breast cancer.
Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories.
During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14,766 women. The breast density model was well calibrated overall (expected-observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years.
The model has only modest ability to discriminate between women who will develop breast cancer and those who will not.
A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Breast density and single-nucleotide polymorphisms (SNPs) have both been associated with breast cancer risk. To determine the extent to which these two breast cancer risk factors are associated, we investigate the association between a panel of validated SNPs related to breast cancer and quantitative measures of mammographic density in a cohort of Caucasian and African-American women. In this IRB-approved, HIPAA-compliant study, we analyzed a screening population of 639 women (250 African American and 389 Caucasian) who were tested with a validated panel assay of 12 SNPs previously associated to breast cancer risk. Each woman underwent digital mammography as part of routine screening and all were interpreted as negative. Both absolute and percent estimates of area and volumetric density were quantified on a per-woman basis using validated software. Associations between the number of risk alleles in each SNP and the density measures were assessed through a race-stratified linear regression analysis, adjusted for age, BMI, and Gail lifetime risk. The majority of SNPs were not found to be associated with any measure of breast density. SNP rs3817198 (in LSP1) was significantly associated with both absolute area (p = 0.004) and volumetric (p = 0.019) breast density in Caucasian women. In African-American women, SNPs rs3803662 (in TNRC9/TOX3) and rs4973768 (in NEK10) were significantly associated with absolute (p = 0.042) and percent (p = 0.028) volume density respectively. The majority of SNPs investigated in our study were not found to be significantly associated with breast density, even when accounting for age, BMI, and Gail risk, suggesting that these two different risk factors contain potentially independent information regarding a woman's risk to develop breast cancer. Additionally, the few statistically significant associations between breast density and SNPs were different for Caucasian versus African American women. Larger prospective studies are warranted to validate our findings and determine potential implications for breast cancer risk assessment.
    BMC Cancer 01/2015; 15(1):1159. DOI:10.1186/s12885-015-1159-3 · 3.32 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To identify the prevalence rate of primary breast cancer in women younger than 30 years of age in a symptomatic population in Riyadh, Kingdom of Saudi Arabia. To analyze the imaging pattern and possible risk factors in cases with cancer. Breast cancer in this age group is generally rare and not clearly understood. At King Abdulaziz Medical City for National Guard, Riyadh, a retrospective 5-year (January 2006 to December 2010) data was collected from the Medical Imaging departmental records on breast imaging. Patients younger than 30 years of age were identified including those with breast cancer. The clinical presentation, risk factors, imaging findings and final outcomes were analyzed in a descriptive way. The total number of patients diagnosed with primary breast cancer was recorded. Seventeen out of a total of 4873 patients younger than 30 years examined had primary breast cancer constituting a rate of 3.5 per 1000 symptomatic patients. The age range was 17 to 29 with mean of 27. The total number of patients with primary breast cancer diagnosed during that period was 413 making a percentage of 4.1% (17 out of 413) in those younger than 30 years. First presentation with a palpable mass and imaging findings of unequivocal category 5 of Breast Imaging Reporting and Data System (BI-RADS) occurred in all. Eight patients had stage I and II while nine had stage III and IV cancers. Only 2 of the 17 had first-degree family history. The youngest was 17 years old. A prevalence rate of 3.5 per 1000 primary cancer occurred in the symptomatic population studied and 4 in every 100 primary cancer diagnosed in the unit occurred in women younger than 30 years. First presentation, low family trait and typical imaging features of malignancy was found in all cases.
  • Source
    Cancer Research 02/2012; 71(24 Supplement):P4-11-07-P4-11-07. DOI:10.1158/0008-5472.SABCS11-P4-11-07 · 9.28 Impact Factor

Full-text (2 Sources)

Available from
May 20, 2014