A Quantitative Description of the Percentage of Breast Density Measurement Using Full-field Digital Mammography

Rationale and Objectives: Breast density is a significant breast cancer risk factor that is measured from mammograms. However, uncertainty remains in both understanding its underlying physical properties as it relates to the breast and determining the optimal method for its measurement. A quantitative description of the information captured by the standard operator-assisted percentage of breast density (PD) measure was developed using full-field digital mammography (FFDM) images that were calibrated to adjust for interimage acquisition technique differences. Materials and Methods: The information captured by the standard PD measure was quantified by developing a similar measure of breast density (PDc) from calibrated mammograms automatically by applying a static threshold to each image. The specific threshold was estimated by first sampling the probability distributions for breast tissue in calibrated mammograms. A percent glandular (PG) measure of breast density was also derived from calibrated mammograms. The PD, PDc, and PG breast density measures were compared using both linear correlation (R) and quartile odds ratio measures derived from a matched case-control study. Results: The standard PD measure is an estimate of the number of pixel values above a fixed idealized x-ray attenuation fraction. There was significant correlation (P<.0001) between the PDc-PD (r = 0.78), PDc-PG (r = 0.87), and PD-PG (r = 0.71) measures of breast density. Risk estimates associated with the lowest to highest quartiles for the PDc measure (odds ratio [OR]: 1.0 ref., 3.4, 3.6, and 5.6), and the standard PD measure (OR 1.0 ref., 2.9, 4.8, and 5.1) were similar and greater than that of the calibrated PG measure (OR 1.0 ref., 2.0, 2.4, and 2.4). Conclusions: The information captured by the standard PD measure was quantified as it relates to calibrated mammograms and used to develop an automated method for measuring breast density. These findings represent an initial step for developing an automated measure built on an established calibration platform. A fully developed automated measure may be useful for both research- and clinical-based risk applications.