The association between MD and breast cancer has been investigated in more than 50 studies over the last three decades. These studies have varied in their approaches to the measurement of MD (reviewed in Table ), study designs, and populations. The majority (n
= 42) of these studies were recently reviewed [3
] and incorporated into a meta-analysis. They illustrate a high prevalence of increased density in the general population, whether estimated by percentage density (26% to 32% of women had 50% or more), parenchymal pattern (21% to 55% of women had the P2 or DY pattern), or Breast Imaging Reporting and Data System (BI-RADS) density (31% to 43% had a BI-RADS of 3 or 4) (Table ). Also, the results show that there exists a strong dose-response association between MD and breast cancer regardless of the type of assessment (quantitative or qualitative), the population (symptomatic or asymptomatic), or whether the density assessment was made on a negative mammogram years prior to the cancer diagnosis (incidence studies) or on the contralateral mammogram at the time of the breast cancer diagnosis (prevalence studies) (Table ). However, the strongest overall associations are seen among the quantitative percentage density phenotype estimated subjectively by a radiologist or using semi-objective methods (thresholding and the planimetry or tracing methods) [8
]. Other aspects of the mammogram which have been less frequently examined with risk include the absolute area of density [4
], types of densities (nodular versus homogeneous) [15
], and computer-automated measures of characteristics of the underlying mammogram image or pixel distribution [3
]. To date, these have not consistently shown stronger estimates with breast cancer than the quantitative MD measure [3
]. That density assessed at a variety of institutions with mammograms over differing time periods showed highly consistent results illustrates that the MD and breast cancer association is not greatly influenced by mammogram quality, estimation method, or year the mammogram was performed.
Classifications of mammographic density
MD is correlated with several breast cancer risk factors; the strongest associations are seen with body mass index (BMI) and age [4
]. Importantly, though, MD is an independent risk factor for breast cancer, illustrated in the majority of studies by its robust association with breast cancer after adjustment for these and other risk factors for breast cancer [4
]. In fact, recent studies of percentage MD and breast cancer illustrated that failure to adjust for BMI resulted in underestimation of the effect of MD on risk [17
]. Thus, the adjustments for BMI and age are important for accurately estimating the risk associated with MD.
Modification of association by risk factors and ethnicity
Few studies have examined potential modifying effects of risk factors on the MD and breast cancer association. Hormone replacement therapy (HRT), especially combination therapy, consistently shows a strong positive association with MD [19
] and should be considered in analyses of MD with risk, but studies have not seen a modification of the MD and risk association by HRT use [18
]. A stronger association of MD and risk has been seen among women with breast cancer in a first-degree relative [21
]; but among carriers with a BRCA1
mutation, relative risks were similar to those of non-carriers [25
]. The suggestion of stronger associations among women with high BMI [20
] has been reported, as well as findings that the higher risk associated with low parity is stronger among women with high MD [20
]. No difference of association has been seen by alcohol use [27
]. To date, there is little consistent evidence that the risk associated with MD varies according to other risk factors for breast cancer.
The MD and breast cancer association is not limited to older or younger women of mammogram age. But there is currently no consensus as to whether the association is stronger among one age or menopausal group. Some studies [4
] observed stronger risk estimates among postmenopausal women (or those over age 50), whereas others [6
] found stronger associations in younger or premenopausal women or neither group [30
]. The recent meta-analysis suggested stronger relative risks at older ages that were limited to the 25% to 49% category (versus less than 5%) but no consistent increase across all categories [3
]. Importantly, a larger proportion of premenopausal women have dense breasts (greater than 50% dense), with estimates of 37% among premenopausal women compared with 12% among postmenopausal women. Even without significant differences in association by menopausal status, the attributable risk is much higher in younger women (26%) than in older women (7%) [6
]. This underscores the importance of MD for potential risk prediction in younger women.
Unfortunately, due to the nature of this trait's dependence on a mammogram for estimation, the significance of MD in young women below mammogram age is unknown.
In addition, MD has been seen to be associated with increased risk across several ethnic groups. Studies of Caucasians, African-Americans, and Asian-Americans [12
] have all shown increased risk with percentage or area density. However, the magnitude of association has been weaker [14
] or inconsistent in the Asian and Asian-American populations [12
], questioning the importance of this predictor in the Asian population. In fact, some have suggested that absolute area of density is a better measure of breast cancer risk than percentage density in the Asian population due to their distinct physical proportions [33
]. In general, MD assessed as the parenchymal pattern, percentage density, and absolute area of density appears to be a strong risk factor in a number of populations.
More than masking bias
The relationship between MD and breast cancer is thought to be multifactorial, and in early studies, the main explanation was thought to be due to 'masking bias' [34
]. In breasts with extensive MD, cancers may be masked because they have the same x-ray attenuation properties as fibroglandular tissue. At an initial mammogram, then, cancers in dense breasts would often escape detection and could manifest themselves shortly thereafter. Therefore, the sole inclusion of incident cases arising shortly after a negative screening examination would erroneously give the impression of increased breast cancer risk in women with extensive MD. The MD and breast cancer association was expected to disappear with longer follow-up and repeated screening. But two large cohort studies from the 1990s [4
] challenged the 'masking bias' hypothesis, finding increased breast cancer risks for at least 7 to 10 years after a screening examination. This is also confirmed in the latest large-scale studies on MD and breast cancer risk [6
]. Furthermore, although relative risks for breast cancer are higher when studying incident cases diagnosed relatively shortly after a negative examination than when studying prevalent cases, risk is still strong among prevalent cases [36
]. Similarly, although relative risks are higher when studying interval cancers than when studying screen-detected cancers, studies of screen-detected cancers still demonstrate a strong association [6
]. This was recently illustrated in three nested case-control studies by Boyd and colleagues [6
], who found that compared with women with density in less than 10% of the mammogram, women with more than 75% density had an increased risk of breast cancer (odds ratio [OR] = 4.7; 95% confidence interval [CI]: 3.0, 7.4), whether detected by screening (OR = 3.5; 95% CI: 2.0, 6.2) or detected within 12 months of a negative screening examination (OR = 17.8; 95% CI: 4.8, 65.9).
In summary, the MD and breast cancer association is robust irrespective of measurement of MD, strong in magnitude, not explained by masking bias, independent of the influence of other risk factors, and generalizable to several populations, including both premenopausal and postmenopausal women. Due to the high prevalence of increased MD in the population, this risk factor could explain a large proportion of breast cancers as well as provide additional clinical information for breast cancer risk prediction. Translating the estimates of risk corresponding to different levels of MD into a model that could be used as an assessment tool for breast cancer risk prediction is a logical consideration and is explored in the following section.