Automated Quantitative Measures of Breast Density

乳房密度的自动定量测量

• 批准号: 8625722
• 负责人: JOHN J HEINE
• 金额: $ 58.87万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8625722
• 关键词: Accounting; Achievement; Algorithms; Area; Automation; BRCA1 gene; Benchmarking; Breast; Breast Cancer Risk Factor; Calibration; Clinic; Clinical; Data; Data Set; Detection; Development; Digital Mammography; Environment; Epidemiologic Methods; Film; Foundations; Frequencies; Goals; Healthcare; Image; Image Analysis; Imaging Phantoms; Individual; Inherited; Intervention; Least-Squares Analysis; Logistic Regressions; Machine Learning; Magnetic Resonance Imaging; Mammography; Manufacturer Name; Matched Case-Control Study; Measurement; Measures; Methodology; Methods; Metric; Modality; Modeling; Mutation; Outcome; Patient Care; Patients; Process; Relative (related person); Research; Risk; Risk Assessment; Risk Factors; Risk Management; Risk Reduction; Specificity; Standardization; System; Techniques; Technology; Time; Tissues; Translating; Translations; Variant; Woman; Work; base; breast density; cancer risk; density; design; detector; digital; digital imaging; image processing; improved; malignant breast neoplasm; meetings; novel; prospective; public health relevance; research study; screening; tool

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).

描述（由申请人提供）：乳房 X 光检查乳腺密度 (BD) 是一个重要的乳腺癌危险因素，其重要性仅次于遗传性 BRCA 突变。大多数得出此结论的研究都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出乳房区域并定义阈值。尽管显然是一个非常宝贵的研究工具，但该标准不适合自动化，因此不适合在临床环境（即大规模实施）中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线摄影（FFDM）（美国新兴的乳房筛查标准模式）方面的最新成就，为将BD已证实的研究价值转化为临床奠定基础。我们使用特定单元开发了 FFDM 校准系统，产生了四个重要发现：(1) 标准化技术使所有图像的像素值具有可比性，(2) 新的校准空间变化 BD 测量（或 Vc）提供了更强的(3) Vc 是 PD 的函数，PD 是 BD 的另一个校准指标，也是一个重要的风险因素，以及其他重要的风险协变量，即高度相关但非线性， (4) 证明应用于原始乳房 X 光检查 (或 Vr) 的变异测量 (或 V) 也是一个重要的乳腺癌风险因素。 在这项拟议的工作中，我们以校准方法为基础，并将其应用于不同的 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，与我们之前的研究结果进行比较。由于探测器设计的差异可能会改变空间变化，因此必须在新的 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而获得的。我们将确定最佳的乳腺密度测量和表示（即需要校准），其中最佳由以下属性定义：自动化、定量、可重复、在不同成像平台上一致，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颖的分析策略，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 BD 测量提供一个处方。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、降低风险干预措施以及确定乳房X光检查可能无效的情况（即致密组织显着降低乳房X光检查的敏感性或特异性的情况）方面对患者进行个性化护理。