Brisson Jacques, Diorio Caroline, Mâsse Benoît
Population Health Research Unit and Breast Centre, Centre hospitalier affilié universitaire de Québec, Québec, Québec G1S 4L8, Canada.
Cancer Epidemiol Biomarkers Prev. 2003 Aug;12(8):728-32.
Mammographic breast densities are one of the strongest breast cancer risk factors. The two most frequently used classifications of breast densities are Wolfe's parenchymal pattern and the percentage of the breast with densities. In this analysis, associations of these two classifications with breast cancer risk were compared, and the dose response curve of risk with densities was examined. Three case-control studies were combined totaling 1060 cases with newly diagnosed breast cancer and 2352 controls. A single observer had assessed parenchymal pattern and percent density without any information on subjects. Relative risks (RRs) were estimated with logistic regression and spline functions adjusting for age and body weight. The two classifications were strongly correlated (r = 0.81, P = 0.0001). Breast cancer risk increased progressively with percent density reaching a 5-6-fold increase for women with 85% or more of the breast with densities compared with women with no density. In contrast, women with P2 or DY patterns had only a 2-3-fold increase in risk compared with women with N1 pattern. More importantly, among women with P2 or DY, RR varied substantially with percent density, whereas, among women with a given percent density, RR varied little with parenchymal pattern. Comparisons of multivariate models reveal that in the presence of parenchymal pattern, inclusion of percent density in the model improved the prediction of breast cancer risk (chi(2) = 35.5, P = 0.0082) but not the opposite (chi(2) = 1.1, P = 0.7662). These findings show that the percentage of the breast with densities provide more information on breast cancer risk than Wolfe's parenchymal patterns and that, once percent breast density is taken into account, no more information on breast cancer risk is given by assessing parenchymal pattern.
乳腺钼靶密度是最强的乳腺癌风险因素之一。最常用的两种乳腺密度分类方法是沃尔夫实质模式和乳腺密度百分比。在本分析中，比较了这两种分类与乳腺癌风险的关联，并研究了风险与密度的剂量反应曲线。三项病例对照研究合并，共有1060例新诊断乳腺癌病例和2352例对照。由一名观察者评估实质模式和密度百分比，且对受试者情况一无所知。采用逻辑回归和样条函数估计相对风险（RRs），并对年龄和体重进行校正。这两种分类方法高度相关（r = 0.81，P = 0.0001）。乳腺癌风险随密度百分比逐渐增加，与无密度的女性相比，乳腺密度达到85%或更高的女性风险增加5至6倍。相比之下，与N1模式的女性相比，P2或DY模式的女性风险仅增加2至3倍。更重要的是，在P2或DY模式的女性中，RR随密度百分比有很大差异，而在给定密度百分比的女性中，RR随实质模式变化很小。多变量模型比较显示，在存在实质模式的情况下，模型中纳入密度百分比可改善对乳腺癌风险的预测（χ² = 35.5，P = 0.0082），但反之则不然（χ² = 1.1，P = 0.7662）。这些发现表明，乳腺密度百分比比沃尔夫实质模式能提供更多关于乳腺癌风险的信息，并且一旦考虑了乳腺密度百分比，评估实质模式并不能提供更多关于乳腺癌风险的信息。
