Evaluation of novel tomosynthesis density measures in breast cancer risk prediction

在美国，乳腺筛查已迅速过渡到数字乳腺断层合成摄影（DBT），这是一种X射线技术 其中从有限数量的低剂量X射线源投影重建3-D图像。DBT报价 上级组织可视化，允许直接测量致密组织的实际体积， 比估计的百分比或体积（从二维乳房X光片）。由于乳腺密度是一个强有力的预测， 掩蔽和风险，DBT体积密度措施，包括我们最近开发的和第一个同类产品，充分 自动化的三维测量，有可能改善个性化的乳腺癌（BC）风险预测。没有 迄今为止的研究已经在大型、不同的队列或亚群中评价了DBT体积密度测量， 了解这些措施的影响，以改善对掩蔽和风险的预测，从而有针对性地预防 和筛选方法。我们的目标是全面检查DBT体积密度措施 作为侵入性、间歇性和晚期BC的风险因素，并评估其对临床相关 跨多个种族群体的BC风险模型和人工智能（AI）算法。我们建议 在三个进行常规DBT的大型乳腺筛查队列中进行的研究，每个队列都有全面的临床 风险因素、每名女性多次DBT、随访和BC结局。具体来说，我们将建立一个嵌套的 对3,000多例侵袭性BC病例和9,000例对照进行的病例对照研究，这些病例在设施、年龄、种族、民族 入组日期、DBT和随访时间以及估计新研究和商业DBT体积 从入组至6岁，通过DBT筛查检查测量并确定临床BI-RADS密度 诊断前3个月（或对照的相应随访）。在目标1中，我们将评估DBT体积 入组时，密度测量及其组合作为侵入性、间期和晚期BC的风险因素 DBT检查，以及癌症五年内的DBT检查（或对照随访），使用 艺术商业和研究算法。我们还将通过DBT考试评估关联的差异， 年龄、种族、绝经状态和体重指数。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测量，包括BCSC 5年风险模型、新型BCSC 6-晚期癌症的5年累积风险模型，其次是5年和10年的Tyrer-Cuzick模型， 年风险。利用这些结果，我们将确定DBT密度措施对高风险阈值的影响 用于预防性治疗和定制成像。最后，在目标3中，我们将评估DBT的贡献 体积密度测量三种新型AI算法，用于BC风险和检测， 短期和长期的侵入性、晚期和间歇性BC。我们的创新研究将是最大的， 告知新型DBT体积密度测量如何增强BC风险分层和预测 跨多个种族，并建立多样化的资源，以评估新的DBT措施和风险模型， 它们会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。

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.