Automated Quantitative Measures of Breast Density

描述(申请人提供)：乳房X光检查乳房密度(BD)是一个重要的乳腺癌风险因素，其大小仅次于遗传性BRCA突变。产生这一结论的大多数研究使用操作员辅助的方法(应用于数字化胶片)来估计BD的百分比(即PD，标准)，这需要专家技术人员勾勒出乳房区域并定义阈值。尽管该标准显然是一种非常有价值的研究工具，但它本身并不适合自动化，因此不适合在临床环境下(即大规模实施)应用于患者风险评估和管理。我们的目标是通过推进我们在全视野数字乳房X光摄影(FFDM)方面的最新成就，为将BD已被证明的研究价值转化为临床奠定基础。全场数字乳房X光摄影是美国新兴的乳房筛查标准模式。我们使用一个特定的单元开发了一个用于FFDM的校准系统，该系统产生了四个重要的发现：(1)使所有图像的像素值具有可比性的标准化技术，(2)提供比标准更强的风险度量的新的校准空间变化BD测量(或VC)，(3)VC是PD的函数，PD是BD的另一个校准测量，也是显著的风险因素，以及其他重要的风险协变量，即高度相关但非线性， 以及(4)证明了应用于原始乳房X光(或VR)的变异度量(或V)也是一个重要的乳腺癌危险因素。在这项拟议的工作中，我们建立在我们的校准方法的基础上，并将其应用于不同的FFDM单元。我们将验证来自不同Ffdm技术的VC和VR测量，并使用先前存在的和新的ffdm数据集，使用匹配的病例对照研究与我们先前的发现进行比较。由于探测器设计的差异有可能改变空间差异，因此有必要在新的V-指标中评估这些影响，以证明乳腺癌风险不依赖于系统设计。我们将通过比较VC和VR来量化来自校准的增益，因为增益是以牺牲高级图像处理和分析为代价的。我们将确定最佳的乳房密度测量和表示(即是否需要校准)，其中最佳由以下属性定义：自动化、定量、可重复性、跨不同成像平台的一致性，并提供至少与PD提供的风险预测相同的风险预测。为了实现我们的目标，我们使用公认的技术并引入新的分析策略，其中包括统计学习，以更好地捕获导入之间的关系 风险协变量。这项工作将为进行最佳的BD测量提供处方。这项工作的成功完成将使BD能够全面整合到诊所环境中。潜在的应用包括在筛查频率、减少风险干预方面对患者进行个性化护理，以及识别乳房X光检查可能无效的情况(即致密组织显著降低乳房X光检查的敏感度或特异度)。

DESCRIPTION (provided by applicant): Mammographic breast density (BD) is a significant breast cancer risk factor, second in magnitude only to inherited BRCA mutations. Most research studies generating this conclusion used an operator-assisted method (applied to digitized film) to estimate the percentage of BD (i.e. PD, the standard), which requires an expert technician to outline the breast region and define thresholds. Although clearly an invaluable research tool, this standard does not lend itself to automation, and is therefore not amenable for application in the clinical setting (i.e. large-scale implementation) for patient risk assessment and management. Our goal is to lay the foundation for translating the demonstrated research value of BD into the clinic by advancing our recent achievements in full field digital mammography (FFDM), the emerging standard modality for breast screening in the US. We developed a calibration system for FFDM using a specific unit that produced four significant findings: (1) a standardization technique that makes pixel values comparable across all images, (2) a new calibrated spatial variation BD measurement (or Vc) that offered a stronger measurement of risk than the standard, (3) Vc is a function of PD, another calibrated measure of BD that is also a significant risk factor, and other important risk covariates, i.e. high correlation but non-linear, and (4) demonstrated the variation measure (or V) applied to raw mammograms (or Vr) is also a significant breast cancer risk factor. In this proposed work we build on our calibration approach and apply it to different FFDM units. We will validate the Vc and Vr measures from different FFDM technology and make comparisons with our previous findings using a matched case-control study using both pre-existing and new FFDM datasets. Because differences in detector designs have the potential to alter spatial variation, it is imperative to assess these influences n the new V-metrics to demonstrate that breast cancer risk is not dependent upon the system design. We will quantify the gains derived from calibration by comparing Vc and Vr, because gains are derived at the expense of advanced image processing and analyses. We will determine the optimal breast density measure and representation (i.e. is calibration required), where optimal is defined by these attributes: automated, quantitative, reproducible, consistent across different imaging platforms, and offers risk prediction at least equivalent with that offere by PD. To meet our objectives, we use accepted techniques and introduce novel analysis strategies that include statistical learning to better capture the relationships between the import risk covariates. This work will provide a prescription for making the optimal BD measurement. The successful completion of this work will allow the full scale integration of BD into the clinica environment. Potential applications include personalized care of patients in terms of screening frequency, risk reduction interventions, and the identification of situations where mammography may be ineffective (i.e. where dense tissue significantly reduces either sensitivity or specificityof mammography).