Nayeem Fatima, Ju Hyunsu, Brunder Donald G, Nagamani Manubai, Anderson Karl E, Khamapirad Tuenchit, Lu Lee-Jane W
Division of Human Nutrition, Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, 700 Harborside Drive, Galveston, TX 77555-1109, USA.
Division of Biostatistics, Department of Preventive Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX 77550-1147, USA.
Int J Breast Cancer. 2014;2014:961679. doi: 10.1155/2014/961679. Epub 2014 Jul 15.
Women with high breast density (BD) have a 4- to 6-fold greater risk for breast cancer than women with low BD. We found that BD can be easily computed from a mathematical algorithm using routine mammographic imaging data or by a curve-fitting algorithm using fat and nonfat suppression magnetic resonance imaging (MRI) data. These BD measures in a strictly defined group of premenopausal women providing both mammographic and breast MRI images were predicted as well by the same set of strong predictor variables as were measures from a published laborious histogram segmentation method and a full field digital mammographic unit in multivariate regression models. We also found that the number of completed pregnancies, C-reactive protein, aspartate aminotransferase, and progesterone were more strongly associated with amounts of glandular tissue than adipose tissue, while fat body mass, alanine aminotransferase, and insulin like growth factor-II appear to be more associated with the amount of breast adipose tissue. Our results show that methods of breast imaging and modalities for estimating the amount of glandular tissue have no effects on the strength of these predictors of BD. Thus, the more convenient mathematical algorithm and the safer MRI protocols may facilitate prospective measurements of BD.
乳腺密度高的女性患乳腺癌的风险比乳腺密度低的女性高4至6倍。我们发现，乳腺密度可以通过使用常规乳腺钼靶成像数据的数学算法或使用脂肪和非脂肪抑制磁共振成像（MRI）数据的曲线拟合算法轻松计算得出。在一组严格定义的绝经前女性中，同时提供乳腺钼靶和乳腺MRI图像，通过与已发表的繁琐直方图分割方法以及全视野数字乳腺钼靶设备在多变量回归模型中的测量相同的一组强预测变量，也能很好地预测这些乳腺密度测量值。我们还发现，既往妊娠次数、C反应蛋白、天冬氨酸转氨酶和孕酮与腺体组织量的关联比与脂肪组织量的关联更强，而脂肪体重、丙氨酸转氨酶和胰岛素样生长因子-II似乎与乳腺脂肪组织量的关联更强。我们的结果表明，乳腺成像方法和估计腺体组织量的方式对这些乳腺密度预测指标的强度没有影响。因此，更便捷的数学算法和更安全的MRI方案可能有助于乳腺密度的前瞻性测量。
