Evaluation of novel tomosynthesis density measures in breast cancer risk prediction

新型断层合成密度测量在乳腺癌风险预测中的评价

• 批准号: 10680241
• 负责人: KARLA M KERLIKOWSKE
• 金额: $ 70.18万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10680241
• 关键词: 3-Dimensional; Advanced Malignant Neoplasm; Age; Algorithms; Artificial Intelligence; Asian; Black race; Body mass index; Breast; Breast Cancer Detection; Breast Cancer Risk Factor; Breast Cancer Surveillance Consortium; Breast Magnetic Resonance Imaging; Calibration; Cancer Detection; Chemoprevention; Classification; Clinic; Clinical; Data; Development; Diagnosis; Digital Breast Tomosynthesis; Digital Mammography; Discrimination; Dose; Enrollment; Ethnic Origin; Evaluation; FDA approved; Frequencies; Goals; Image; Individual; Intervention; Life Style; Malignant Neoplasms; Mammographic screening; Mammography; Masks; Measurement; Measures; Menopausal Status; Modality; Modeling; Modernization; Nested Case-Control Study; Outcome; Pennsylvania; Population; Population Heterogeneity; Prevention; Prevention approach; Preventive therapy; Prognosis; Race; Research; Resources; Risk; Risk Assessment; Risk Estimate; Risk Factors; Risk Management; Roentgen Rays; San Francisco; Source; Technology; Three-Dimensional Image; Time; Tissues; Translating; University Hospitals; Visualization; Woman; advanced breast cancer; age group; artificial intelligence algorithm; breast cancer diagnosis; breast density; cancer invasiveness; cancer risk; cancer subtypes; clinical risk; clinically relevant; cohort; density; evidence base; follow-up; high risk; improved; individualized prevention; innovation; malignant breast neoplasm; mammography registry; novel; racial population; risk prediction; risk prediction model; risk stratification; routine screening; screening; supplemental screening; tomosynthesis

Breast screening has rapidly transitioned in the US to digital breast tomosynthesis (DBT), an x-ray technology in which 3-D images are reconstructed from a limited number of low-dose x-ray source projections. DBT offers superior tissue visualization allowing for the direct measurement of the actual volume of dense tissue, rather than an estimated percent or volume (from a 2-D mammogram). Since breast density is a strong predictor of masking and risk, DBT volumetric density measures, including our recently developed and first of its kind, fully automated 3-D measure, have the potential to improve individualized breast cancer (BC) risk prediction. No studies to date have evaluated DBT volumetric density measures in large, diverse cohorts or subpopulations to understand the impact of these measures to improve prediction of masking and risk in order to tailor prevention and screening approaches. Our goal is to comprehensively examine DBT volumetric density measures as risk factors for invasive, interval and advanced BC, and evaluate their impact on clinically relevant BC risk models and artificial intelligence (AI) algorithms across multiple racial groups. We propose this research in three large breast screening cohorts that perform routine DBT, each with comprehensive clinical risk factors, multiple DBT per woman, follow-up and BC outcomes. Specifically, we will establish a nested case-control study of over 3,000 invasive BC cases and 9,000 controls matched on facility, age, race, ethnicity, date of enrollment DBT and follow-up time and estimate novel research and commercial DBT volumetric measures as well as ascertain clinical BI-RADS density from DBT screening exams from enrollment up to 6 months prior to diagnosis (or corresponding follow-up for controls). In Aim 1, we will evaluate DBT volumetric density measures and their combinations as risk factors for invasive, interval and advanced BC, at enrollment DBT exam, as well as DBT exams within five years of the cancer (or follow-up for controls), using state of the art commercial and research algorithms. We will also assess differences in associations by time of DBT exam, age, race, menopausal status and body mass index. In Aim 2, we will evaluate the contribution of DBT volumetric density measures to clinical BC risk models, including the BCSC 5-year risk model, the novel BCSC 6-year cumulative risk model for advanced cancer, and secondarily, the Tyrer-Cuzick model for both 5 and 10- year risk. Using these results, we will determine the impact of DBT density measures on high-risk thresholds for preventative therapy and tailored imaging. Finally, in Aim 3, we will evaluate the contribution of DBT volumetric density measures to three novel AI algorithms developed for BC risk and detection, with risk of invasive, advanced and interval BC in the short- and longer-term. Our innovative study will be the largest to inform how novel DBT volumetric density measures can augment BC risk-stratification and prediction across multiple races and build a diverse resource to evaluate new DBT measures and risk models as they evolve. These findings will build an evidence base to inform personalized prevention approaches.

在美国，乳房筛查已经迅速过渡到数字乳房断层合成(DBT)，这是一种X射线技术 其中3D图像是从有限数量的低剂量X射线源投影重建的。DBT提供 卓越的组织可视化，允许直接测量致密组织的实际体积，而不是 而不是估计的百分比或体积(来自2-D乳房X光检查)。因为乳房密度是一个很强的预测因子 掩蔽和风险，DBT体积密度测量，包括我们最近开发的和第一个此类完全 自动化的3-D测量，有可能改善个性化乳腺癌(BC)风险预测。不是 到目前为止的研究已经评估了DBT在大的、不同的队列或亚群体中的体积密度测量 了解这些措施的影响，以改进对掩饰和风险的预测，以便量身定做预防 和筛选方法。我们的目标是全面检查DBT体积密度测量 作为侵袭性、间歇性和晚期BC的危险因素，并评估其对临床相关的影响 BC风险模型和多个种族组的人工智能(AI)算法。我们建议这样做 对三个进行常规DBT的大型乳房筛查队列的研究，每个队列都有全面的临床 危险因素、每名妇女多个DBT、随访和BC结局。具体地说，我们将建立嵌套的 对3000多例侵袭性BC病例和9000名对照进行的病例对照研究，这些病例与对照在发病机制、年龄、种族、民族、 加入DBT的日期和后续时间，并估计新研究和商业DBT体积 从DBT筛查检查到登记6次，测量和确定临床BI-RADS密度 在诊断前几个月(或对对照进行相应的随访)。在目标1中，我们将评估DBT体积 密度测量及其组合是入选时侵袭性、间歇性和晚期BC的危险因素 DBT检查，以及癌症五年内的DBT检查(或对照的随访)，使用状态 艺术、商业和研究算法。我们还将通过DBT考试的时间来评估联想的差异， 年龄、种族、绝经状况和体重指数。在目标2中，我们将评估DBT的贡献 临床BC风险模型的体积密度度量，包括BCSC 5年风险模型、新型BCSC 晚期癌症的6年累积风险模型，其次是5岁和10岁的Tyrer-Cuzick模型。 年风险。利用这些结果，我们将确定DBT密度测量对高风险阈值的影响 用于预防性治疗和量身定做的成像。最后，在目标3中，我们将评估DBT的贡献 体积密度测量三种为BC风险和检测开发的新人工智能算法，具有以下风险 侵袭性、进展性和间歇性BC在短期和长期。我们的创新研究将是最大的 介绍新的DBT体积密度测量如何增强BC风险分层和预测 并构建不同的资源来评估新的DBT措施和风险模型 它们在进化。这些发现将建立一个证据基础，为个性化预防方法提供信息。