MAMMOGRAPHIC DENSITIES AND RISK OF BREAST CANCER

描述：（改编自研究者摘要） 该建议是开发测量乳房组织的改进方法 我们称之为“乳房X线摄影密度”的特征， 与乳腺癌的风险密切相关。这些改进的方法将 完全自动化，不需要观察者，并且可以测量体积， 而不是乳房中相关组织的投影面积。 不同个体的乳腺致密组织可能具有相似的 投影面积，但在厚度上并因此在数量上显著不同。 因为我们期望相关组织的数量更直接地 与乳腺癌风险相关的面积，我们认为， 测量体积将是乳腺癌的一个更强的风险因素， 投影面积。评估放射学致密乳腺组织的体积 作为乳腺癌的危险因素，我们必须在 控制条件，我们已经开发了一个校准设备和一个 厚度计，将被纳入该领域的成像过程中 乳房X光检查以评估乳房组织组成和厚度。 我们将在选定的乳房X线摄影部位安装校准设备， 将其纳入所有接受乳房X光检查的受试者的检查中。案件将 被诊断患有组织学证实的浸润性乳腺癌的受试者。一 对照组将从同一乳腺X线摄影部门选择，并且年龄匹配 案子共招募800对病例对照。的 在这个项目中要解决的假设是：1）自动测量 乳房中的乳房X线摄影致密组织的体积将产生更大的 比预计的风险更高。2)乳腺摄影致密的体积 乳腺组织将与其他风险因素更密切相关， 乳腺癌的发病率比预计的要高。3)联合收割机 体积、投影面积和其他乳房X线摄影特征将改善 与单独的密度百分比相比，乳腺癌风险的预测。

DESCRIPTION: (Adapted from the Investigator's Abstract) The long term goal of this proposal is to develop improved methods of measuring the breast tissue characteristics that we refer to as "mammographic densities" and have found to be strongly related to risk of breast cancer. These improved methods will be fully automated, will not require an observer, and will measure the volume, rather than the projected area, of the relevant tissues in the breast. Mammographically dense breast tissue in different individuals may have similar projected areas, but differ substantially in thickness and thus in quantity. Because we expect the quantity of the relevant tissue to be more directly related to risk of breast cancer than the projected area, we believe that measurement of volume will be a stronger risk factor for breast cancer than is the projected area. To assess the volume of radiologically dense breast tissue as a risk factor for breast cancer we must acquire images prospectively under controlled conditions, and we have developed a calibration device and a thickness gauge that will be incorporated into the field imaged during mammography to assess breast tissue composition and thickness. We will install the calibration device in selected mammography sites, and include it in the examination of all subjects having mammography. Cases will be subjects diagnosed with histologically verified invasive breast cancer. One control will be selected from the same mammography department, and age-matched to the case. A total of 800 case control pairs will be recruited. The hypotheses to be addressed in this project are: 1) Automated measurement of the volume of mammographically dense tissue in the breast will create larger gradients in risk than projected area. 2) The volume of mammographically dense breast tissue will be more strongly associated with other risk factors for breast cancer than the projected area. 3) Statistical models that combine volume, projected area, and other mammographic features will result in improved prediction of breast cancer risk compared to percent density alone.