An automated approach for estimation of breast density.
An automated approach for estimation of breast density.
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一种估计乳房密度的自动化方法。
DOI：
10.1158/1055-9965.epi-08-0170
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发表时间：
2008-11
影响因子：
3.8
通讯作者：
Vachon, Celine M.
中科院分区：
文献类型：
--
作者：
Heine, John J.;Carston, Michael J.;Scott, Christopher G.;Brandt, Kathleen R.;Wu, Fang-Fang;Pankratz, Vernon Shane;Sellers, Thomas A.;Vachon, Celine M.
乳腺密度是乳腺癌的一个重要危险因素;然而，没有标准的评估方法。对自动乳腺密度方法（ABDM）进行了修改，并将其与半自动用户辅助显示方法（CM）和乳腺成像报告和数据系统（BI-RADS）四类组织成分测量进行了比较，以评估其预测未来乳腺癌风险的能力。在马约诊所进行的一项匹配乳腺癌病例（n=372）对照（n=713）研究中，使用数字化胶片乳房X线照片对这三种估计方法进行了评价。非癌性乳腺的头尾位（CC）乳腺X线照片平均在诊断前7年获得。筛选实践中无乳腺癌既往史的两个对照组与每个病例在年龄、既往筛查乳房X线照片的数量、最终筛查检查日期、该日期的绝经状态、最早和最新乳房X线照片之间的间隔以及居住地方面相匹配。适当时，使用Pearson线性相关（R）和斯皮尔曼等级相关（r）系数比较三种方法。使用条件Logistic回归估计与密度百分比（ABDM，CM）或BI-RADS类别四分位数相关的乳腺癌风险（比值比[OR]和95%置信区间[CI]）。估计受试者工作特征曲线下面积（AUC），并用于比较每种方法的区分能力。连续测量ABDM和CM彼此高度相关（R=0.70），但与BI-RADS的相关性较低（ABDM r=0.49，CM r=0.57）。与ABDM的最低到最高四分位数相关的风险估计值在幅度上更大（OR：1.0[ref]，2.3，3.0，5.2，p趋势<0.001）（OR：1.0[ref]，1.7，2.1和3.8; p趋势<0.001）和BI-RADS（OR：1.0[ref]，1.6，1.5，2.6; p趋势<0.001）方法。然而，所有方法在病例和对照状态之间的区分相似：ABDM、CM和BI-RADS的AUC分别为0.64、0.63和0.61。ABDM是从数字化胶片乳腺X线照片定量评估乳腺密度的可行选择。
Breast density is a strong risk factor for breast cancer; however, no standard assessment method exists. An automated breast density method (ABDM) was modified and compared with a semi-automated user-assisted display method (CM) and the Breast Imaging Reporting and Data System (BI-RADS) 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 (n=372) control (n=713) study at the Mayo Clinic using digitized film mammograms. Mammograms from the craniocaudal (CC) view of the noncancerous breast were acquired on average seven years before diagnosis. Two controls with no prior history of breast cancer from the screening practice were matched to each case on age, number of prior 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 where appropriate. Conditional logistic regression was used to estimate the risk of breast cancer (odds ratios [ORs] and 95% confidence intervals [CIs]) associated with the quartiles of percent density (ABDM, CM) or BI-RADS category. The area under the receiver operator characteristic curve (AUC) was estimated and used to compare the discriminatory capabilities of each approach. The continuous measures ABDM and CM were highly correlated with each other (R=0.70) but less with BI-RADS (r=0.49 for ABDM and r=0.57 for CM). Risk estimates associated with the lowest to highest quartiles of ABDM were greater in magnitude (ORs: 1.0[ref], 2.3, 3.0, 5.2, p-trend<0.001) than the corresponding quartiles for CM (ORs: 1.0[ref], 1.7, 2.1 and 3.8; p-trend<0.001) and BI-RADS (ORs: 1.0[ref], 1.6, 1.5, 2.6; p-trend<0.001) methods. However, all methods similarly discriminated between case and control status: AUCs were 0.64, 0.63 and 0.61 for ABDM, CM and BI-RADS, respectively. The ABDM is a viable option for quantitatively assessing breast density from digitized film mammograms.