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.

Highlights

  • Breast density (BD) reflects the proportion of fibroglandular tissue in the breast and is one of the strongest independent predictors of breast cancer risk [1,2,3,4]

  • The multiple possible variations in instrument settings can confound the use of mammograms for BD estimates, and for this reason phantoms or step-wedge standards are included for calibration of mammography when measuring volumetric density [6, 7]

  • We previously showed that breast glandularity measured as percent glandular tissue (%-G), glandular tissue volume (GV), fat volume (FV), and total volume (TV) from mammographic and magnetic resonance imaging (MRI) images were highly correlated with one another by ordinary least square regression (R2) and intraclass correlation (ICC) analyses [9]

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Summary

Introduction

Breast density (BD) reflects the proportion of fibroglandular tissue in the breast and is one of the strongest independent predictors of breast cancer risk [1,2,3,4]. The most widely used method for measuring BD is the histogram segmentation method (HSM) using mammographic images, as pioneered by Byng et al [5]. Mammography is designed to detect early breast cancer rather than to measure BD, and the radiation dose required for detecting cancer is greater for women with dense breasts. The multiple possible variations in instrument settings can confound the use of mammograms for BD estimates, and for this reason phantoms or step-wedge standards are included for calibration of mammography when measuring volumetric density [6, 7]. Individualized imaging parameters are routinely stored in the DICOM header of the mammogram report. We developed a mathematical model (MATH) that uses a substantial number of these individualized imaging

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