Eng Amanda, Gallant Zoe, Shepherd John, McCormack Valerie, Li Jingmei, Dowsett Mitch, Vinnicombe Sarah, Allen Steve, dos-Santos-Silva Isabel
Breast Cancer Res. 2014 Sep 20;16(5):439. doi: 10.1186/s13058-014-0439-1.
Mammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM).
The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables.
Quantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD = 0.68, P = 0.05), and Quantra slightly lower (AUC = 0.63; P = 0.06), than Cumulus (AUC = 0.65).
Fully-automated methods are valid alternatives to the labour-intensive "gold standard" Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments.
乳腺钼靶密度是一种强大的乳腺癌风险因素，也是筛查敏感性的主要决定因素。然而，目前尚无针对全视野数字乳腺钼靶摄影（FFDM）的经过验证的估计方法。
在来自414例病例和685例对照的3168张FFDM图像中，评估了三种基于面积的方法（BI-RADS、半自动Cumulus和基于ImageJ的全自动方法）以及三种全自动体积测量方法（Volpara、Quantra和单能X线吸收法（SXA））的性能。使用线性回归模型评估对照中乳腺癌风险因素与密度之间的关联，并使用逻辑回归模型评估密度与乳腺癌风险的关联，同时对年龄、体重指数（BMI）和生殖变量进行调整。
Quantra和基于ImageJ的方法分别有4%和11%的参与者未能得出测量结果。所有六种密度评估方法均显示，百分比密度（PD）与年龄、BMI、在乳腺钼靶检查时已生育和绝经呈负相关。所有方法中，PD与乳腺癌均呈正相关，但PD每增加一个标准差，Volpara（1.83；95%可信区间：1.51至2.21）和Cumulus（1.58；1.33至1.88）的风险增加最高，而基于ImageJ的方法（1.45；1.21至1.74）、Quantra（1.4；1.19至1.66）和SXA（1.37；1.16至1.63）的风险增加较低。处于PD最高五分位数（或BI-RADS 4）的女性患癌风险分别是处于最低五分位数（或BI-RADS 1）女性的8.26（4.28至15.96）、3.94（2.26至6.86）、3.38（2.00至5.72）、2.99（1.76至5.09）、2.55（1.46至4.43）和2.96（0.50至17.5）倍，分别对应Volpara、Quantra、Cumulus、SXA、基于ImageJ的方法和BI-RADS（所有趋势P值均<0.0001）。与Cumulus（曲线下面积（AUC）=0.65）相比，基于ImageJ的方法在区分病例和对照方面能力略高（PD的AUC=0.68，P=0.05），而Quantra略低（AUC=0.63；P=0.06）。
全自动方法是用于量化FFDM中密度的劳动密集型“金标准”Cumulus的有效替代方法。具体方法的选择将取决于目的和环境，但纵向密度评估需要采用相同的方法。
