乳腺X线摄影密度百分比和致密面积作为乳腺癌的风险因素。
Percent Mammographic Density and Dense Area as Risk Factors for Breast Cancer.
作者信息
Rauh C, Hack C C, Häberle L, Hein A, Engel A, Schrauder M G, Fasching P A, Jud S M, Ekici A B, Loehberg C R, Meier-Meitinger M, Ozan S, Schulz-Wendtland R, Uder M, Hartmann A, Wachter D L, Beckmann M W, Heusinger K
机构信息
Department of Gynecology and Obstetrics, University Hospital Erlangen, Erlangen.
Institute of Human Genetics, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen.
出版信息
Geburtshilfe Frauenheilkd. 2012 Aug;72(8):727-733. doi: 10.1055/s-0032-1315129.
Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD. We conducted a case control study with hospital-based patients with a diagnosis of invasive breast cancer and healthy women as controls. A total of 561 patients and 376 controls with available mammographic density were included into this study. We describe the differences concerning the common risk factors BMI, parital status, use of hormone replacement therapy (HRT) and menopause between cases and controls and estimate the odds ratios for PMD and DA, adjusted for the mentioned risk factors. Furthermore we compare the prediction models with each other to find out whether the addition of DA improves the model. Mammographic density and DA were highly correlated with each other. Both variables were as well correlated to the commonly known risk factors with an expected direction and strength, however PMD (ρ = -0.56) was stronger correlated to BMI than DA (ρ = -0.11). The group of women within the highest quartil of PMD had an OR of 2.12 (95 % CI: 1.25-3.62). This could not be seen for the fourth quartile concerning DA. However the assessment of breast cancer risk could be improved by including DA in a prediction model in addition to common risk factors and PMD. The inclusion of the parameter DA into a prediction model for breast cancer in addition to established risk factors and PMD could improve the breast cancer risk assessment. As DA is measured together with PMD in the process of computer-assisted assessment of PMD it might be considered to include it as one additional breast cancer risk factor that is obtained from breast imaging.
已知乳腺钼靶特征与乳腺癌风险相关。通过计算机辅助方法评估的乳腺钼靶密度百分比（PMD）是已确定的乳腺癌风险因素。在进行此项评估时，还会报告乳房的绝对致密面积（DA）。本研究的目的是评估除其他风险因素以及PMD之外，DA对乳腺癌风险的预测价值。我们对以医院为基础的浸润性乳腺癌患者和健康女性作为对照进行了病例对照研究。本研究共纳入了561例患者和376例有可用乳腺钼靶密度数据的对照。我们描述了病例组和对照组在常见风险因素体重指数（BMI）、生育状况、激素替代疗法（HRT）的使用和绝经方面的差异，并估计了经上述风险因素调整后的PMD和DA的优势比。此外，我们相互比较预测模型，以确定添加DA是否能改善模型。乳腺钼靶密度和DA彼此高度相关。这两个变量与常见风险因素的相关性也符合预期的方向和强度，然而PMD（ρ = -0.56）与BMI的相关性比DA（ρ = -0.11）更强。PMD最高四分位数组的优势比为2.12（95%可信区间：1.25 - 3.62）。DA的第四四分位数组未观察到这种情况。然而，除了常见风险因素和PMD外，将DA纳入预测模型可以改善乳腺癌风险评估。除了既定风险因素和PMD外，将参数DA纳入乳腺癌预测模型可以改善乳腺癌风险评估。由于在计算机辅助评估PMD的过程中DA与PMD一起测量，因此可以考虑将其作为从乳腺成像中获得的另一个乳腺癌风险因素。
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