Abstract
Women with high breast density (BD) have a 4- to 6-fold greater risk for breast cancer than women with low BD. We found that BD can be easily computed from a mathematical algorithm using routine mammographic imaging data or by a curve-fitting algorithm using fat and nonfat suppression magnetic resonance imaging (MRI) data. These BD measures in a strictly defined group of premenopausal women providing both mammographic and breast MRI images were predicted as well by the same set of strong predictor variables as were measures from a published laborious histogram segmentation method and a full field digital mammographic unit in multivariate regression models. We also found that the number of completed pregnancies, C-reactive protein, aspartate aminotransferase, and progesterone were more strongly associated with amounts of glandular tissue than adipose tissue, while fat body mass, alanine aminotransferase, and insulin like growth factor-II appear to be more associated with the amount of breast adipose tissue. Our results show that methods of breast imaging and modalities for estimating the amount of glandular tissue have no effects on the strength of these predictors of BD. Thus, the more convenient mathematical algorithm and the safer MRI protocols may facilitate prospective measurements of BD.