Grant Number:	5R03CA135699-02 Interpret this number
Primary Investigator:	Maskarinec, Gertraud
Organization:	University Of Hawaii At Manoa
Project Title:	A Pooled Analysis of Mammographic Density and Breast Cancer Risk
Fiscal Year:	2010

DESCRIPTION (provided by applicant): Breast density as assessed in mammographic images is a strong predictor of breast cancer risk, but the strength of the association may differ with body weight. It is important to understand these sources of heterogeneity when breast density is used as an intermediate endpoint in intervention studies and as a predictor of individualized breast cancer risk. The proposed project makes use of existing data from previous case-control studies on mammographic density and breast cancer risk. The specific aims of the proposed project are to examine breast cancer risk associated with mammographic density across levels of body mass index (BMI) and breast size; to elucidate methodologic issues in comparing mammographic images that were obtained from different locations and using different technologies; and to initiate a working group to collaborate on studies of mammographic densities. This project will combine data from four case-control studies on mammographic densities. The total number of subjects will be 4,229 with 1,747 cases and 2,482 controls with an ethnic distribution of 45% Caucasian, 38% Asian, 8% African- American, and 9% Others and a wide variation in BMI and breast size. The data base will be based on a common format for all variables of interest, breast cancer status and stage, breast density as percent and as absolute density, anthropometric, and reproductive variables. One mammogram for each subject will be assessed by one reader to assure the comparability of density readings across studies using a quantitative assessment method. We will apply general linear models to compare the previous density readings obtained through different methods with the new standardized readings. Breast cancer risk associated with percent density for different levels of BMI and breast size will be estimated using the SUDAAN logistic regression procedure and adjusting for confounders. The investigators will plan collaborative studies and consider the possibility of inviting additional researchers to contribute their mammographic data for future innovative investigations of mammographic density. This project will provide new information on how body weight affects breast cancer risk related to breast density. These findings will be useful for breast cancer risk estimation and breast cancer prevention.





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