Digital breast tomosynthesis imaging biomarkers for breast cancer risk estimation

项目摘要 研究一再表明，限制乳房X线摄影灵敏度的乳腺密度也是一个很强的风险 乳腺癌的危险因素。因此，乳房密度信息越来越多地用于指导个性化 乳腺癌筛查和预防。然而，传统的2D乳房X射线摄影由于以下原因而固有地受到限制： 组织重叠在估计乳腺密度中的作用。此外，常用的 乳房X线摄影密度不能完全捕捉乳房实质模式的异质性， 乳腺癌风险的一个重要的额外指标。乳腺断层合成摄影是一种新兴的3D X射线模式 与2D乳房X线摄影术相比，其提供了优越的上级断层摄影乳房组织可视化。多项研究 已经表明，与传统的癌症筛查相比， 仅用乳房X光检查进行筛查，这目前正在推动断层合成的广泛实施， 一般人群乳腺癌筛查。除了提高筛选性能，我们的假设是 来自断层合成的乳腺密度和实质模式复杂性的3D测量可以优于 从传统的2D乳房X线摄影密度测量，以提高乳腺癌的风险估计。期间 该奖项的前一阶段，我们开发了乳腺密度和实质纹理的创新方法， 数字乳腺X射线摄影分析，并评估其与乳腺癌风险的相关性。我们的研究 显示了令人信服的证据，这些措施提供了强大的新的成像标记，可以增强 标准的乳房X光检查这种更新应用程序的重点是扩大我们的工作，以新兴的 X射线断层合成技术，通过开发来自X射线断层合成的乳房组织组成的新测量 并确定它们与乳腺癌风险的关系。为此，我们将开展最大规模的 迄今为止报告的关于断层合成和乳腺癌风险的关联研究，包括一项充分表征和 675例病例和2700例对照的不同样本嵌套在两个大型学术乳腺癌筛查中 在宾夕法尼亚大学（UPenn）和马约诊所的实践。在AIM 1中，我们将扩展和优化 我们的乳腺X线摄影实质分析软件用于乳腺断层合成，使用复杂的计算 我们小组开创的乳腺断层合成摄影方法;在AIM 2中，我们将确定 乳腺癌和新的断层合成密度和纹理措施，使用回顾性数据分析， 我们的训练集;在AIM 3中，我们将使用前瞻性收集的种族， 两个机构的样本都是多样的。在这种情况下，我们还将评估措施的绩效 源自合成数字乳腺X线照片，日益取代传统的数字乳腺X线照片图像 在断层合成采集中。这项研究将是第一个评估断层合成在癌症风险评估中的应用 并将开发必要的工具，使更大的多中心研究。最终，这些新的生物标志物 可能导致更准确的风险预测，改善个性化的乳腺癌筛查和预防。

Project Summary Studies have repeatedly shown that breast density, which limits mammographic sensitivity, is also a strong risk factor for breast cancer. As a result, breast density information is increasingly utilized in guiding personalizing breast cancer screening and prevention. Conventional 2D mammography, however, is inherently limited due to the effect of tissue superimposition in estimating breast density. In addition, the commonly used measures of mammographic density cannot fully capture the heterogeneity of the breast parenchymal pattern, shown to be an important additional indicator of breast cancer risk. Breast Tomosynthesis is an emerging 3D x-ray modality which offers superior, tomographic breast tissue visualization compared to 2D mammography. Multiple studies have shown that screening with tomosynthesis reduces recalls while increasing cancer detection compared to screening with mammography alone, which is currently fueling a broad implementation of tomosynthesis for general population breast cancer screening. In addition to improved screening performance, our hypothesis is that 3D measures of breast density and parenchymal pattern complexity from tomosynthesis can outperform density measures from conventional 2D mammography, to improve breast cancer risk estimation. During the previous phase of this award, we developed innovative methods for breast density and parenchymal texture analysis in digital mammography and evaluated their association to breast cancer risk. Our studies have shown compelling evidence that these measures provide powerful new imaging markers that can augment the standard mammographic measures. This renewal application focuses on extending our work to the emerging technology of tomosynthesis, by developing novel measures of breast tissue composition from tomosynthesis and by determining their association to breast cancer risk. Towards this end, we will perform the largest association study reported to-date for tomosynthesis and breast cancer risk, including a well-characterized and diverse sample of 675 cases and 2700 controls nested within two large academic breast cancer screening practices at the University of Pennsylvania (UPenn) and the Mayo Clinic. In AIM1 we will extend and optimize our mammographic parenchymal analysis software for breast tomosynthesis using sophisticated computational methods pioneered from our group for breast tomosynthesis; in AIM2 we will determine associations between breast cancer and the novel tomosynthesis density and texture measures, using retrospective data analysis as our training set; and in AIM3 we will perform independent validation using prospectively collected, ethnically diverse, samples from both institutions. Within this setting, we will also evaluate the performance of measures derived from synthetic digital mammograms, increasingly replacing conventional digital mammography images in tomosynthesis acquisition. This study will be the first to evaluate tomosynthesis in cancer risk assessment and will develop the necessary tools to enable larger multi-center studies. Ultimately, these novel biomarkers could lead to more accurate risk prediction, improving personalized breast cancer screening and prevention.