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.

乳腺组织的“内在”异质性，反映在乳房X线照片上的质地和空间组成中， 可以为乳房密度提供独立但互补的信息，以评估这两种风险 乳腺癌（BC）和掩饰可能导致乳房X线摄影片中错过BC。这可能是 对于需要改善风险分层的乳房密集的女性中，有40-50％的妇女尤其重要。我们 已经开发了自动化方法来定量测量实质复杂性特征 使用基于创新的晶格的方法全面表征现场数字乳房X线照片（FFDM） 乳房X线照片上实质性组织异质性。使用无监督的聚类应用于功能 从2000屏幕-FFDM测量，我们发现了四个可再现的“内在”实质复杂性的证据 独立促成BC风险的表型，考虑到乳房密度。在此提案中，我们将 扩展这套实质特征，对可推广到的实质表型进行分类和验证 多个种族/族裔群体，并检查他们与卑诗省风险和掩盖的关联。在AIM1中，我们将 表征和验证实质复杂性表型，反映了固有的”异质性 乳房实质。我们将使用已建立的自动化算法来测量代表的功能 从三个屏幕范围的36,000个屏幕FFDM中取样的实质异质性的统计和结构特性 大型多民族乳房X线摄影队列。我们将使用分层聚类方法和一个分式样本 方法，首先分类，然后独立验证一组可靠的不同实质表型 在所有乳房密度类别中，专门针对密集的乳房。在AIM 2中，我们将检查 实质复杂性表型与侵入性BC风险的关联。我们将衡量这些 3817事件的诊断前五年内执行的屏幕FFDM实质特征 侵入性癌症病例和7634例匹配对照，并将其分类为实质表型 AIM 1。我们将检查它们与BC的关联（既在所有密度和密集的乳房中） 调整已建立的危险因素和乳房密度。最后，在AIM 3中，我们将研究贡献 掩盖侵入性BC的实质复杂性表型。我们将检查实质是否 与真实阴性对照组相比，表型与间隔与屏幕检测的癌症有关， 在AIM 2中使用案例对照研究。AIMS 1和2也将在可用的女性子集中进行测试 数字乳房合成（DBT）考试（n = 300侵入性BC），以告知我们的结果转化为 新兴DBT技术。我们的提案大写了经验丰富的研究人员，生产性合作， 新颖的算法，并建立了良好的人群，并将阐明新型实质表型 这可以提高我们定义以差异BC风险定义女性子集的能力，并增加了卑诗省错过的风险。 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。