Identifying women at risk for breast cancer is not part of the current clinical paradigm for women's health even though strong risk factors, such as breast density, have been identified. A high percentage of dense parenchyma on mammograms appears to give a 4-6 fold risk to develop breast cancer. The biological cause for the association is unclear even after 20 years of study. The long-term goal of this proposed research is to determine the best global or local measure of breast density for risk assessment of high-mortality cancers. The objective of this application is to describe the relationship of specific local measures of volume breast density and density morphology to cancer risk for invasive cancers as well as DCIS cases, and to discover what serum, tissue, or clinical biomarkers act as a determinant of the macro distribution of dense breast tissue. The central hypothesis is that subregional measurements of the percent fibroglandular volume density are more strongly associated with local and global breast cancer risk. Very little is known of the distribution of dense tissue within breast tissue in women with cancer versus those without because an in vivo description of dense tissue distribution has not been available. Our secondary hypothesis is that specific biomarkers of breast density act as morphostats for density macro structure. Our approach is unique in that we will be using a novel pixel-by-pixel measure of volumetric breast density called Single X-ray Absorptiometry (SXA). Our first specific aim is to identify subregions of dense breast volume associated with subsequent breast cancer in women undergoing mammography. The working hypothesis for this aim is that subregions of the breast may be stronger risk predictor of local cancer in that region than global breast density. In addition, subregional density may be a stronger risk predictor of a woman's risk of breast cancer than global breast density due to the exclusion of peripheral adipose that envelopes the parenchyma. Our second specific aim is to Identify the association of breast morphology to risk and to key tissue, serum, and clinical correlates to test the morphostatic behavior of breast density. The coarse distribution of density is known to show morphostatic qualities between women as well as between a woman's two breasts. Our working hypothesis is that there may be a particular spatial distribution, or morphology, of dense tissue that is associated with cancer risk independent of the magnitude of the density. Our expected outcome will include: development of new regions of local breast density that will be made available for future studies using our large cohort, confirmation of breast density as either a local or global risk factor, and the identification of the most likely biomarker candidates driving breast density morphometry. Our findings will reduce the risks of harms for women undergoing mammography by providing a cancer risk marker to intelligently reduce the screening frequency of very low risk women. It will aid in the decreased mortality of high risk women by their more accurate identification and targeting for use of more sensitive imaging and risk reduction strategies.

Public Health Relevance

The proposed research is relevant to the public health because it will attempt to significantly improve the ability to quantify a woman's personal risk of breast cancer using measures taken from her screening mammography images. This is relevant to the NIH's mission because improved measures of breast cancer risk will advance significantly the Nation's capacity to protect and improve health.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
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Epidemiology of Cancer Study Section (EPIC)
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Verma, Mukesh
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University of California San Francisco
Schools of Medicine
San Francisco
United States
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Gierach, Gretchen L; Geller, Berta M; Shepherd, John A et al. (2014) Comparison of mammographic density assessed as volumes and areas among women undergoing diagnostic image-guided breast biopsy. Cancer Epidemiol Biomarkers Prev 23:2338-48