Novel Imaging Methods to Determine Breast Density

我们建议开发新的成像技术来量化乳房密度和乳房实质图案。 这可以用来进一步研究组织成分和癌症风险之间的联系。 根据目前的定义和测量，乳房密度是乳腺癌的一个重要风险因素。临床部 乳房X光摄影密度的测量不足以研究乳房组织的组成如何与 癌症风险，特别是在组织样本上。我们将开发以下内容：双X射线吸收测量仪 在临床数字乳腺摄影设备上进行乳腺组织成分定量(DXA)技术和一种新的 使用连接性(欧拉数)组合来量化投影的实质图案的技术 和分形维。这些措施将在整个乳房样本中进行量化，并与 乳房密度的当前临床测量(即乳房X光检查密度和BI-RADS评分)。此外，我们 将把这些技术扩展到薄(5毫米或更小)的乳房组织切片中。地方措施 密度和结构将与整个乳房的测量进行比较，方法是将乳房标本切开 整体性和总结性地方措施。最后，我们将研究是否使用磁力测量新陈代谢谱 磁共振魔角波谱(MR-MAS)对应于相关的特定病理组织类型 通过对相同样本的组织学分析，发现乳房致密(来自项目2)。 这项研究的结果可能导致(1)新的乳房成分临床措施的实施 和临床乳房X光检查中的风险，(2)薄层组织样本密度的新成分测量，例如 活检组织，以及(3)识别特定于乳房组织类型和 适用于新的磁共振光谱成像技术，以获得更敏感和更具体的风险测量。

We propose to develop novel imaging techniques to quantity breast density and breast parenchymal patterns that can be used to further study the linkage of tissue composition and cancer risk. Breast density, as currently defined and measured, is a strong risk factor for breast cancer. The clinical measure of mammographic density is inadequate to study how the composition of breast tissue relates to cancer risk, especially on tissue specimens. We will develop the following: a Dual X-ray Absorptiometry (DXA) technique to quantify breast tissue composition on a clinical digital mammography device and a novel technique to quantify the projected parenchyma pattern using a combination of connectivity (Euler number) and fractal dimensions. These measures will be quantified in whole breast specimens and compared to current clinical measures of breast density (i.e., mammographic density and BI-RADS scores). In addition, we will extend these techniques to their use in thin (5 mm or less) breast tissue sections. Local measures of density and structure will be compared to whole breast measures by sectioning breasts specimens in their entirety and summing the local measures. Lastly, we will study if metabolic profiles measured using magnetic resonance magic angle spectroscopy (MR-MAS) correspond to specific pathological tissue types associated with dense breast (from Project 2) by histology analysis of the same samples. The results of this study may lead to (1) the implementation of new clinical measures of breast composition and risk in clinical mammograms, (2) novel compositional measures of density for thin tissue samples such as biopsy tissue, and (3) the identification of a metabolic signature that is specific to breast tissue type and applicable to new MR spectroscopic imaging techniques for a more sensitive and specific measure of risk.