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

乳腺实质的放射组学表型及其与乳腺癌风险和检测的关联

• 批准号: 9897495
• 负责人: KARLA M KERLIKOWSKE
• 金额: $ 63.26万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9897495
• 关键词: Accounting; Age; Algorithms; American College of Radiology; Breast; Breast Cancer Detection; Breast Cancer Risk Factor; Cancer Detection; Case-Control Studies; Categories; Collaborations; Detection; Diagnosis; Digital Breast Tomosynthesis; Digital Mammography; Discrimination; Drug or chemical Tissue Distribution; Ethnic Origin; Ethnic group; Film; Future; Health Care Costs; Heterogeneity; High Risk Woman; Image; Imaging technology; Individual; Laws; Lead; Malignant Neoplasms; Mammary Gland Parenchyma; Mammographic screening; Mammography; Mandatory Reporting; Masks; Measures; Methods; Pattern; Phenotype; Population; Population Heterogeneity; Prevention; Prevention approach; Property; Provider; Race; Reproducibility; Research Personnel; Risk; Risk Factors; Risk Reduction; Risk stratification; Sampling; Sampling Studies; Spatial Distribution; Stratification; Structure; Technology; Testing; Texture; Time; Tissues; Translations; Variant; Woman; automated algorithm; base; breast cancer diagnosis; breast density; cancer invasiveness; case control; cohort; density; digital; digital imaging; experience; improved; innovation; insight; malignant breast neoplasm; novel; racial and ethnic; racial diversity; radiomics; research clinical testing; screening; secondary analysis; standardize measure; supplemental screening; tomosynthesis; tool; tumor

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.

乳房组织的“内在”异质性，反映在乳房X线照片的纹理和空间组成上， 可以为乳腺密度提供独立但互补的信息，用于评估 乳腺癌（BC）和遮蔽，可能导致筛查性乳腺X线摄影遗漏BC。这可能是 对于40-50%的乳腺致密的女性来说，这一点尤其重要，因为她们需要改善风险分层。我们 已经开发了自动化方法来定量测量全脑实质复杂性特征， 现场数字乳腺X线摄影（FFDM），使用创新的基于网格的方法来全面表征 乳腺X光片上的实质组织异质性。将无监督聚类应用于特征 从2000年的屏幕FFDM测量，我们发现了四个可重复的“内在”实质复杂性的证据 表型独立地导致乳腺癌风险，占乳腺密度。在本提案中，我们将 扩展这组实质特征，分类和验证可概括为 多个种族/民族群体，并检查其与BC风险和掩蔽的关联。在AIM 1中，我们将 表征和验证实质复杂性表型，反映“内在”异质性， 乳腺实质我们将使用已建立的自动算法来测量代表 从三个样本中采样的36，000个屏幕FFDM上的实质异质性的统计和结构特性 大型多种族乳房X光检查队列。我们将使用分层聚类方法，并使用分裂样本 方法，首先进行分类，然后独立验证一组强大的不同实质表型 在所有乳腺密度类别中，特别是对于致密乳腺。在AIM 2中，我们将检查 实质复杂性表型与侵袭性BC风险的相关性。我们将测量这些 3817例事件诊断前5年内进行的屏幕实质特征-FFDM 浸润性癌病例和7634例匹配对照，并将其分为以下实质表型： 目标1。我们将研究它们与BC的关系（仅在所有密度水平和致密乳腺中） 根据已确定的风险因素和乳腺密度进行调整。最后，在AIM 3中，我们将研究 以掩盖侵袭性BC。我们将检查脑实质 与真阴性对照相比， 使用AIM 2中的病例对照研究。AIMS 1和2也将在一个可用的女性子集中进行测试。 数字乳腺断层合成摄影（DBT）检查（N=300例侵入性BC），以将我们的结果翻译为 新兴的DBT技术。我们的提案利用了经验丰富的调查人员，富有成效的合作， 新的算法和已建立的、充分表征的队列，并将阐明新的实质表型 这可以提高我们确定不同BC风险和错过BC风险增加的女性子集的能力。 我们的研究最终将为更有效，量身定制的BC筛查和预防方法铺平道路。