Novel effects of phytoestrogenic soy isoflavones on serum calcium and chloride in premenopausal women: A 2-year double-blind, randomized, placebo-controlled study.

Background: Soy phytoestrogens are potential alternatives to postmenopausal hormone replacement therapy (HRT). Adverse effects of HRT such as myocardial infarction, stroke, and pulmonary embolism are mediated by calcium-induced signaling.

Objective: To determine whether soy isoflavones affect serum calcium in healthy female subjects.

Similarity of fibroglandular breast tissue content measured from magnetic resonance and mammographic images and by a mathematical algorithm.

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.

Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm.

Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a four- to six-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from two-dimensional (2D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit.

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor-intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography.