Radiomic phenotypes of breast parenchyma and association with breast cancer risk and detection

乳房组织的“内在”异质性，反映在乳房X光照片上的纹理和空间构成上， 可为乳房密度提供独立但互补的信息，以评估两者的风险 乳癌(BC)和遮盖可能导致筛查乳房X光检查漏掉BC。这可能是 对于40%-50%的乳房致密的女性来说尤其重要，她们需要改进风险分层。我们 开发了自动方法，从完整的角度定量测量实质复杂性 使用创新的基于网格的方法来全面表征现场数字乳房X光片(FFDM) 乳房X线片上实质组织的不均一性。使用应用于要素的无监督聚类 从2000年的Screen-FFDM中测量，我们发现了四个可重现的内在实质复杂性的证据 独立导致乳腺癌风险的表型，占乳房密度的比例。在这项提案中，我们将 扩展这组实质特征，分类和验证可概括为 多个种族/民族，并检查它们与BC风险和掩饰的关联。在AIM1中，我们将 表征和验证实质复杂性的表型，反映出 乳房实质。我们将使用已建立的自动化算法来测量代表 在36,000屏上的实质非均质性的统计和结构特征 大型多种族乳房X光检查队列。我们将使用层次聚类方法和拆分样本 方法，首先分类，然后独立验证一组不同的实质表型 在所有乳房密度类别中，特别是针对致密乳房。在AIM 2中，我们将研究 实质复杂性表型与侵袭性BC风险的关系。我们将衡量这些指标 屏幕上的实质性特征-3817事件诊断前五年内进行了FFDM 侵袭性癌症患者和7634名匹配的对照组，并将他们归类为来自 目标1.我们将研究它们与BC的关系(仅适用于所有级别的密度和致密乳房) 根据已确定的风险因素和乳房密度进行调整。最后，在AIM 3中，我们将检查其贡献 实质复杂性的表型来掩盖侵袭性BC。我们将检查实质是否 与真阴性对照相比，表型与间隔时间与筛查检测到的癌症有关， 在AIM 2中使用病例对照研究。AIM 1和2也将在可用的妇女子集中进行测试 数字乳房断层合成(DBT)检查(N=300个侵入性BC)，以将我们的结果转换到 新兴的DBT技术。我们的提案充分利用了经验丰富的调查人员，富有成效的合作， 新的算法，以及已建立的、特征良好的队列，并将阐明新的实质表型 这可以提高我们确定患有不同BC风险和错过BC风险增加的女性亚群的能力。 我们的研究最终将为更有效的、量身定制的BC筛查和预防方法铺平道路。

The `intrinsic' heterogeneity of breast tissue, reflected in texture and spatial composition on the mammogram, may provide independent but complementary information to breast density for the assessment of both risk of breast cancer (BC) and masking that can lead to a missed BC on screening mammography. This may be especially important for the 40-50% of women with dense breasts who need improved risk stratification. We have developed automated methods to quantitatively measure parenchymal complexity features from full field digital mammograms (FFDM) using an innovative lattice-based approach to comprehensively characterize parenchymal tissue heterogeneity on the mammogram. Using unsupervised clustering applied to features measured from 2000 screen-FFDM, we found evidence for four reproducible `intrinsic' parenchymal complexity phenotypes that independently contributed to BC risk, accounting for breast density. In this proposal, we will expand this set of parenchymal features, classify and validate parenchymal phenotypes generalizable to multiple racial/ethnic groups, and examine their association with BC risk and masking. In AIM1, we will characterize and validate parenchymal complexity phenotypes reflecting the `intrinsic' heterogeneity of the breast parenchyma. We will use established automated algorithms to measure features representing statistical and structural properties of parenchymal heterogeneity on 36,000 screen-FFDM sampled from three large multi-ethnic mammography cohorts. We will use hierarchical clustering methods, and a split-sample approach, to first classify, and then independently validate a robust set of distinct parenchymal phenotypes among all breast density categories and specifically for dense breasts. In AIM 2, we will examine the association of parenchymal complexity phenotypes with risk for invasive BC. We will measure these parenchymal features on screen-FFDM performed within five years prior to diagnosis from 3817 incident invasive cancer cases and 7634 matched controls, and classify them into the parenchymal phenotypes from Aim 1. We will examine their association with BC (both across all levels of density and dense breasts only) adjusting for established risk factors and breast density. Finally, in AIM 3, we will examine the contribution of parenchymal complexity phenotypes to masking invasive BC. We will examine whether parenchymal phenotypes are associated with interval vs. screen-detected cancers, compared to true-negative controls, using the case-control study in AIM 2. AIMS 1 and 2 will also be tested within a subset of women with available digital breast tomosynthesis (DBT) exams (N=300 invasive BC), to inform the translation of our results to the emerging DBT technology. Our proposal capitalizes on experienced investigators, productive collaborations, novel algorithms, and established, well-characterized cohorts and will elucidate novel parenchymal phenotypes that can improve our ability to define subsets of women at differential BC risk and increased risk of missed BC. Our study will ultimately pave the way for more effective, tailored BC screening and prevention approaches.