Mammographic density, breast cancer risk and risk prediction

Abstract

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.

Figure 1

Relationship between odds ratios (ORs) ranging from 1 to 1,000 and C-statistic for binary risk factor and outcome. Vertical line represents an OR of 1.5, which corresponds to the risk prediction possible using a Gail model risk probability of 0.0167 as a binary cut point [46].

Figure 2

Gain in C-statistic in three breast cancer risk prediction models with the addition of mammographic density (MD). Studies refer to Tice and colleagues [47], Barlow and colleagues [48], and Chen and colleagues [49]. Gail, Gail model; Gail 2, Gail model 2; Postmen Ext., postmenopausal extended Gail model; Premen Ext., premenopausal extended Gail model.