Centre for Molecular, Environmental, Genetic, and Analytic Epidemiology, The University of Melbourne, Level 1, Carlton, Victoria, Australia.
Am J Epidemiol. 2009 Dec 15;170(12):1571-8. doi: 10.1093/aje/kwp313. Epub 2009 Nov 12.
Mammographic density is one of the strongest predictors of breast cancer risk. Typically expressed as a percentage of the breast area occupied by radiologically dense tissue on a mammogram, its full value may not be realized because of its negative association with body mass index. A simpler measure of mammographic density, independent of other breast cancer risk factors and equally predictive of risk, would be preferable for risk prediction models. Percentage and area measures of mammographic density were determined for 815 women at high risk for breast cancer from the baseline assessments in the International Breast Cancer Intervention Study I, a trial of tamoxifen for breast cancer prevention conducted between 1992 and 2001. Multivariate linear regression was used to assess associations between risk factors and the mammographic measures. Percent dense area was negatively associated with age, body mass index, menopausal status, predicted risk, and smoking status (R(2) = 24%). Dense area was negatively associated with only age and body mass index (R(2) = 7%), and the latter association was much weaker than for percent dense area. Nondense area was positively associated with age, body mass index, and predicted risk (R(2) = 36%). Dense area was not associated with the multitude of risk factors that percent dense area was, making it a simpler biomarker for risk prediction modeling. Both dense area and percent dense area should be presented whenever possible for comparisons in research.
乳腺密度是乳腺癌风险的最强预测因子之一。通常以乳腺 X 光片中乳腺组织的辐射状致密程度占乳腺总面积的百分比表示,但由于其与体重指数呈负相关,因此其全部价值可能无法实现。对于风险预测模型,一种与其他乳腺癌风险因素无关且同样可预测风险的乳腺密度的更简单衡量方法将更为理想。在 1992 年至 2001 年期间进行的一项针对乳腺癌预防的他莫昔芬试验——国际乳腺癌干预研究 I 的基线评估中,对 815 名乳腺癌高危女性确定了乳腺密度的百分比和面积衡量标准。多元线性回归用于评估风险因素与乳腺测量之间的关联。致密面积与年龄、体重指数、绝经状态、预测风险和吸烟状况呈负相关(R²=24%)。致密面积仅与年龄和体重指数呈负相关(R²=7%),且后者的关联远弱于百分比致密面积。非致密面积与年龄、体重指数和预测风险呈正相关(R²=36%)。致密面积与百分比致密面积所关联的众多风险因素没有关联,因此它是风险预测模型更简单的生物标志物。在研究中,只要有可能,就应同时呈现致密面积和百分比致密面积,以便进行比较。