Automated Quantitative Measures of Breast Density

描述（由申请人提供）：乳腺摄影乳腺密度（BD）是一个重要的乳腺癌风险因素，仅次于遗传性BRCA突变。得出这一结论的大多数研究使用操作员辅助方法（应用于数字化胶片）来估计BD的百分比（即PD，标准），这需要专业技术人员勾勒乳房区域并定义阈值。尽管该标准显然是一种非常宝贵的研究工具，但它不适合自动化，因此不适合在临床环境中应用（即大规模实施），用于患者风险评估和管理。我们的目标是通过推进我们最近在全视野数字乳腺X射线摄影（FFDM）（美国乳腺筛查的新兴标准模式）方面的成就，为将BD的研究价值转化为临床奠定基础。我们开发了一个FFDM校准系统，使用一个特定的单元，产生了四个重要的发现：（1）使像素值在所有图像上可比较的标准化技术，（2）提供比标准更强的风险测量的新的校准的空间变化BD测量（或Vc），（3）Vc是PD的函数，PD是BD的另一个校准的测量，也是显著的风险因素，和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线照片（或Vr）的变化度量（或V）也是显著的乳腺癌风险因素。 在这项拟议的工作中，我们建立在我们的校准方法，并将其应用到不同的FFDM单元。我们将验证不同FFDM技术的Vc和Vr测量值，并使用现有和新的FFDM数据集进行匹配病例对照研究，将其与我们以前的研究结果进行比较。由于探测器设计的差异有可能改变空间变化，因此必须评估新V指标中的这些影响，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来量化从校准中获得的增益，因为增益是以高级图像处理和分析为代价获得的。我们将确定最佳乳腺密度测量和表示（即是否需要校准），其中最佳由以下属性定义：自动化、定量、可重现、不同成像平台之间的一致性，并提供至少与PD提供的风险预测相当的风险预测。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，包括统计学习，以更好地捕捉进口之间的关系， 风险协变量。这项工作将提供一个处方，使最佳的BD测量。这项工作的成功完成将使BD全面融入临床环境。潜在的应用包括在筛查频率、降低风险干预措施和识别乳房X线摄影可能无效的情况（即致密组织显著降低乳房X线摄影的灵敏度或特异性）方面对患者进行个性化护理。

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).