Vachon Celine M, van Gils Carla H, Sellers Thomas A, Ghosh Karthik, Pruthi Sandhya, Brandt Kathleen R, Pankratz V Shane
Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
Breast Cancer Res. 2007;9(6):217. doi: 10.1186/bcr1829.
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.
在本综述中,我们研究了乳腺X线密度作为乳腺癌独立危险因素的证据,描述了纳入密度因素的风险预测模型,并讨论了在临床实践中使用乳腺X线密度的当前及未来意义。乳腺X线密度在多个群体以及不同乳腺X线检查年龄中,都是乳腺癌持续且强有力的危险因素。最近,这一危险因素已被纳入现有的乳腺癌风险预测模型,虽提升幅度较小,但模型的鉴别准确性有所提高。经过验证后,这些模型可能会取代现有的盖尔模型用于临床风险评估。然而,这些改进模型得出的绝对风险估计在表征个体患癌概率的能力方面仍存在局限。有前景的乳腺X线密度新测量方法,包括可通过全场数字化乳腺X线摄影进行标准化的体积密度,可能会产生更强的危险因素,并提高风险预测模型的准确性。