Heine John J, Carston Michael J, Scott Christopher G, Brandt Kathleen R, Wu Fang-Fang, Pankratz Vernon Shane, Sellers Thomas A, Vachon Celine M
H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA.
Cancer Epidemiol Biomarkers Prev. 2008 Nov;17(11):3090-7. doi: 10.1158/1055-9965.EPI-08-0170.
Breast density is a strong risk factor for breast cancer; however, no standard assessment method exists. An automated breast density method was modified and compared with a semi-automated, user-assisted thresholding method (Cumulus method) and the Breast Imaging Reporting and Data System four-category tissue composition measure for their ability to predict future breast cancer risk. The three estimation methods were evaluated in a matched breast cancer case-control (n = 372 and n = 713, respectively) study at the Mayo Clinic using digitized film mammograms. Mammograms from the craniocaudal view of the noncancerous breast were acquired on average 7 years before diagnosis. Two controls with no previous history of breast cancer from the screening practice were matched to each case on age, number of previous screening mammograms, final screening exam date, menopausal status at this date, interval between earliest and latest available mammograms, and residence. Both Pearson linear correlation (R) and Spearman rank correlation (r) coefficients were used for comparing the three methods as appropriate. Conditional logistic regression was used to estimate the risk for breast cancer (odds ratios and 95% confidence intervals) associated with the quartiles of percent breast density (automated breast density method, Cumulus method) or Breast Imaging Reporting and Data System categories. The area under the receiver operator characteristic curve was estimated and used to compare the discriminatory capabilities of each approach. The continuous measures (automated breast density method and Cumulus method) were highly correlated with each other (R = 0.70) but less with Breast Imaging Reporting and Data System (r = 0.49 for automated breast density method and r = 0.57 for Cumulus method). Risk estimates associated with the lowest to highest quartiles of automated breast density method were greater in magnitude [odds ratios: 1.0 (reference), 2.3, 3.0, 5.2; P trend < 0.001] than the corresponding quartiles for the Cumulus method [odds ratios: 1.0 (reference), 1.7, 2.1, and 3.8; P trend < 0.001] and Breast Imaging Reporting and Data System [odds ratios: 1.0 (reference), 1.6, 1.5, 2.6; P trend < 0.001] method. However, all methods similarly discriminated between case and control status; areas under the receiver operator characteristic curve were 0.64, 0.63, and 0.61 for automated breast density method, Cumulus method, and Breast Imaging Reporting and Data System, respectively. The automated breast density method is a viable option for quantitatively assessing breast density from digitized film mammograms.
乳腺密度是乳腺癌的一个重要风险因素；然而，目前尚无标准的评估方法。一种自动乳腺密度测量方法经过改进，并与一种半自动、用户辅助的阈值法（积云法）以及乳腺影像报告和数据系统的四类组织构成测量法进行比较，以评估它们预测未来患乳腺癌风险的能力。在梅奥诊所的一项匹配的乳腺癌病例对照研究（分别为n = 372和n = 713）中，使用数字化乳腺钼靶片对这三种估计方法进行了评估。来自未患癌乳房头尾位视图的乳腺钼靶片平均在诊断前7年获取。从筛查实践中选取两名无乳腺癌病史的对照，在年龄、既往乳腺钼靶筛查次数、最终筛查检查日期、当时的绝经状态、最早和最晚可用乳腺钼靶片之间的间隔以及居住地等方面与每个病例进行匹配。根据情况适当使用Pearson线性相关系数（R）和Spearman秩相关系数（r）来比较这三种方法。使用条件逻辑回归来估计与乳腺密度百分比四分位数（自动乳腺密度测量法、积云法）或乳腺影像报告和数据系统类别相关的患乳腺癌风险（比值比和95%置信区间）。估计受试者工作特征曲线下面积，并用于比较每种方法的鉴别能力。连续测量方法（自动乳腺密度测量法和积云法）彼此高度相关（R = 0.70），但与乳腺影像报告和数据系统的相关性较低（自动乳腺密度测量法的r = 0.49，积云法的r = 0.57）。与自动乳腺密度测量法从最低到最高四分位数相关的风险估计值在幅度上更大[比值比：1.0（参考值），2.3，3.0，5.2；P趋势<0.001]，高于积云法[比值比：1.0（参考值），1.7，2.1，3.8；P趋势<0.001]和乳腺影像报告和数据系统[比值比：1.0（参考值），1.6，1.5，2.6；P趋势<0.001]方法的相应四分位数。然而，所有方法在区分病例和对照状态方面表现相似；自动乳腺密度测量法、积云法和乳腺影像报告和数据系统的受试者工作特征曲线下面积分别为0.64、0.63和0.61。自动乳腺密度测量法是从数字化乳腺钼靶片中定量评估乳腺密度的一个可行选择。
