Novel Imaging Methods to Determine Breast Density

我们建议开发新的成像技术来量化乳腺密度和乳腺实质模式 可以用来进一步研究组织成分和癌症风险之间的联系。 目前定义和测量的乳腺密度是乳腺癌的一个重要风险因素。临床 乳房X线摄影密度的测量不足以研究乳房组织的组成与 癌症风险，特别是在组织标本上。我们将开发以下：双X射线吸收仪 (DXA)在临床数字乳房X光摄影设备上量化乳房组织成分的技术和一种新颖的 技术，以量化投影薄壁组织模式使用的组合连接（欧拉数） 和分形维数。这些测量将在整个乳房标本中进行量化，并与 乳房密度的当前临床测量（即，乳腺摄影密度和BI-RADS评分）。另外我们 将这些技术扩展到它们在薄（5 mm或更小）乳房组织切片中的使用。地方措施 密度和结构将通过将乳房标本切片， 整体性和局部性措施的总结。最后，我们将研究是否使用磁性测量代谢谱 磁共振魔角谱（MR-MAS）对应于特定的病理组织类型相关 致密乳腺（来自项目2）通过相同样本的组织学分析。 本研究的结果可能导致（1）实施新的临床措施的乳房组成 和风险，（2）用于薄组织样本的密度的新组成测量， 活组织检查组织，和（3）对乳腺组织类型特异性的代谢特征的鉴定，以及 适用于新的MR光谱成像技术，以更灵敏和更具体地测量风险。

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.