Cancer Epidemiology Department, Moffitt Cancer Center & Research Institute, 12902 Bruce B Downs Blvd, Mail Stop: MRC Can/Cont, Tampa, FL 33612.
Corporate Compliance Department, Moffitt Cancer Center & Research Institute, 12902 Bruce B Downs Blvd, Tampa, FL 33612.
Acad Radiol. 2019 Sep;26(9):1181-1190. doi: 10.1016/j.acra.2018.10.009. Epub 2018 Dec 10.
Mammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study.
A matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PG) and standard deviation (PG) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated.
In adjusted models, both PG and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PG, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PG were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60.
The calibrated PG measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PG performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions.
乳腺密度是乳腺癌的一个重要危险因素，但要将其转化为临床应用，需要确保之前基于乳腺 X 线摄影的研究结果适用于当前的技术。本研究旨在评估先前开发的校准方法在应用于病例对照研究时，是否能产生预测乳腺癌风险的乳腺密度指标。
采用病例对照研究（n=319 对）评估两种经校准的乳腺密度指标。从六台 Hologic 乳腺 X 线摄影设备获取二维乳腺 X 线照片：三台常规 Selenia 二维全数字化乳腺 X 线摄影系统和三台 Dimensions 数字乳腺断层合成系统。我们评估了两种经校准的乳腺密度指标定量乳腺癌风险的能力：校准像素的平均值（PG）和标准差（PG）。匹配变量包括年龄、激素替代疗法的使用/持续时间、筛查史和乳腺 X 线摄影设备。校准后的指标与操作员辅助 Cumulus 方法确定的乳腺密度百分比（PD）进行比较。使用条件逻辑回归从连续模型和四分位数（Q）模型生成比值比（OR），置信区间为 95%。还使用接受者操作特征曲线下的面积（Az）作为比较指标。评估了单变量模型和调整体重指数和种族的模型。
在调整后的模型中，PG 和 PD 均与乳腺癌具有统计学显著相关性，Az 值相似，为 0.61-0.62。相应的 OR 和置信区间也相似。对于 PG，连续测量的 OR 为 1.34（1.09，1.66），Q2-Q4 的 OR 为 1.83（1.11，3.02）、2.19（1.28，3.73）和 2.20（1.26，3.85）。对于 PD，连续测量的 OR 为 1.43（1.16，1.76），Q2-Q4 的 OR 为 0.84（0.52，1.38）、1.96（1.19，3.23）和 2.27（1.29，4.00）。PG 的结果略有减弱且无统计学意义。连续测量的 OR 为 1.22（0.99，1.51），Az 值为 0.60；Q2-Q4 的 OR 为 1.24（0.78，1.97）、0.98（0.60，1.61）和 1.26（0.77，2.07），Az 值为 0.60。
经校准的 PG 指标与 PD 一样与乳腺癌显著相关。经校准的 PG 表现稍差。这些发现表明，之前开发的校准方法在更普遍的条件下能够复制。
