Volumetric breast density has long been thought to hold the promise of stronger breast cancer risk prediction due to its accuracy in quantifying the true cellular composition of the breast. We found that volumetric breast density measured as either fibroglandular volume or percent fibroglandular volume was strongly associated with risk of breast cancer. Both statistical methods (c-index and NRI) used compare models that include breast density with clinical risk factors showed that either fibroglandular volume measure significantly improved the accuracy of risk assessment over risk factors alone. However, the c-index was not statistically different between models that included any measure of breast density.
Our study used a novel method for accurately measuring volumetric breast density, called SXA that derives fibroglandular volume and percent fibroglandular volume from a calibration phantom and screening single X-ray tube voltage images (12
). The use of a reference sample in each mammogram minimized the assumptions made in calculating breast density related to changes in X-ray tube and film sensitivity and allowed automated determination of accurate and precise measures of mammographically dense volume. The use of an in-image phantom allows physically-calibrated cross-comparison over time and between different makes and models of mammography systems. Longitudinal cross-calibration is particularly important in studies that monitor changes in breast density resulting from treatment. In addition, all SXA measures for this study were performed in a fully automated mode while the percent dense area measures were made manually by a skilled reader. Thus, there are strong advantages of the SXA method in terms training/labor costs and pooling ability compared to percent dense area.
The odds ratios in our study are comparable to other studies for similar measures and categories. Barlow et al. reported a BI-RADS-4:BI-RADS-1 odds ratio of 3.93 for premenopausal and 3.15 for postmenopausal women (2
). In the Barlow et al. paper, BI-RADS-1 contained 4.3% and 10.2% of the pre- and postmenopausal population, and BI-RADS-4 contained 14.4% and 5.3% respectively. Thus, by percentage of women in the highest and lowest density categories, BI-RADS compares more extreme spectrums of density than quintile analysis. Boyd et al. (9
) reported an odds ratio of 6.1 between the least category of “no density” and the ≥ 75% dense area categories using a semiquantitative method (radiologist manual readings). As with BI-RADS, these categories contained fewer women at more extreme densities than quintile analysis with only 7% and 18% of the women respectively in those two extreme categories. Vachon et al. (20
) reported an odds ratios of 2.7 between 4th
quartiles for percent dense area with a 3-year median time before diagnosis. There are many other examples in the literature with values ranging from 3 to 6 fold increase in risk between the highest and lowest categories of density that vary depending on the method and median time before diagnosis. A meta-analysis of available studies (10
) showed that the odds ratios were much stronger for percent dense area than for Wolfe grade or BI-RADS classification. Our odds ratios for percent dense area are similar to others that have reported on the strength of breast density as a risk factor.
The NRI method of comparing models is relatively new, but is useful to in showing how models that include fibroglandular volume measures may improve risk prediction. First, the risk estimate accuracy for women who did not develop breast cancer in the 5 years of the study was higher when absolute fibroglandular volume was included in the risk model than without. More accurate identification of women at low risk for breast cancer undergo less frequent screening mammograms. Models that included either absolute fibroglandular volume or percent fibroglandular volume were significantly more accurate in their risk assessment than risk factors alone among women who develop breast cancer. Women at high breast cancer risk may consider and benefit from more frequent breast imaging, genetic counseling, or chemoprevention. Thus, is it clear that more accurate risk models that include a volumetric breast density measure could lead to more women being offered and undergoing appropriate prevention measures.
There have been other methods proposed for measuring percent fibroglandular volume. The standard mammographic form (SMF version 2.2β, Siemens Molecular Imaging Limited) is a method used in film mammograms to quantify the volume of “interesting” dense tissue (21
). In this case, density associated with the skin and adipose tissues are estimated and subtracted off from the percent fibroglandular volume. Aitken et al. (23
) compared percent dense area to the SMF measure and found significant associations for percent dense area with a 2.19 (95% CI 1.28-3.72) interquartile relative risk, but no significant association to breast cancer for SMF. Hologic (Hologic, Inc., Bedford, MA) has marketed an adaptation of the SMF method for digital mammography called Quantra. However, it has yet to be reported if Quantra differs substantially from SMF regarding it risk association. Pawluczyk et al. (24
) reported on a method for measure of volumetric breast density that relies on the precise description of the digital mammography system’s geometry, breast thickness, and x-ray characteristics to derive a volumetric breast density. No associations with risk have been reported for the Pawluczyk or other volumetric methods using either mammography (24
), magnet resonance imaging (26
), tomosynthesis (28
), or ultrasound tomography (29
). Thus, it is important to note that the results of this study may not be generalizable to all measures of fibroglandular volume since they vary in the way the dense volume is modeled (with or without skin, with or without total water, the quality control methods (with absolute phantom standards or relative standards), and the calibration standards (adipose tissue or fatty acid for 0 percent fibroglandular volume; parenchymal tissue or water for 100 percent fibroglandular volume.)
Fibroglandular volume and percent dense area may capture different variations in breast morphology related to cancer risk. Fibroglandular volume should directly capture risk related to increasing volumes of stromal tissue and is thereby expected to be positively associated with BMI. Percent dense area is weakly related to fibroglandular volume and may capture additional risk related to the distribution of stromal tissue in the breast. Whereas percent dense area assumes that dark areas of a mammogram are composed of lipid, SXA directly measures the density of areas that appear to be only fat. Adipose tissue contains water that contributes to density in the SXA calculations. As shown in , the percent fibroglandular volume was higher on average than percent dense area (44.5% versus 27.1% for controls). This is likely due to the fact that SXA estimates of density includes all lean tissue, including the density from water in the adipose tissue, whereas percent dense area does not. As shown in , the lower limit of percent dense area is zero percent dense area but approximately 18% for percent fibroglandular volume because the fatty breast will still contain approximately 18% water.
Although the inclusion of adipose water in the fibroglandular compartment may limit dynamic range to some degree, the approach is less noisy than more complicated models that try to extract only parenchyma tissue since no approximations of skin thickness or hydration of the fat are used in the calculation of density. Free water in adipose tissue does vary for individuals and a constant value cannot be used for the general case. In magnetic resonance imaging studies measuring breast total water content versus either delineated fibroglandular volume or percent dense area, the adipose %water (at 0, zero percent dense area) was found to be 14% from Graham et al. (30
) and 18% from Boyd et al. (31
Our study has limitations. First 40% of the participants were excluded due to lack of phantom calibration that arose from technical issues regarding the position and construction of the phantom. These issues have since been resolved in later generation phantoms by placing the phantom on top rather than between compression paddles, and eliminating moving parts (32
). Secondly, our results were based on digitized film data. A majority of systems in the US are now full-field digital mammography systems that have improved linearity and dynamic range. Thus these results should be reproducible in newer mammography systems to a similar if not improved accuracy. Studies using SXA on full-field digital mammography systems are ongoing. In addition, BI-RADS density scores were not available and comparisons to this standard clinical measure could not be made.
We conclude that fibroglandular volume and percent fibroglandular volume, estimated with the SXA method, are strong predictors of breast cancer, and more accurate predictors of breast cancer risk than percent dense area.