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线乳房密度（BD）是一个重要的乳腺癌危险因素，仅次于遗传的BRCA突变。产生该结论的大多数研究都使用操作员辅助方法（应用于数字化膜）来估计BD的百分比（即PD，标准），该方法要求专家技术人员概述乳房区域并定义阈值。尽管显然是一个宝贵的研究工具，但该标准并不适合自动化，因此不适合在临床环境（即大规模实施）中应用患者风险评估和管理。我们的目标是通过提高我们最近在全田数字乳房X线摄影（FFDM）（美国乳房筛查的新兴标准方式）中推进我们最近的成就来将BD的研究价值转化为诊所的基础。 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协变量，即高相关但非线性， （4）证明了应用于原始乳房X线照片（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，我们以校准方法为基础，并将其应用于不同的FFDM单元。我们将使用匹配的案例对照研究，使用既有的FFDM数据集和新的FFDM数据集验证不同FFDM技术的VC和VR度量，并与以前的发现进行比较。由于检测器设计的差异有可能改变空间变化，因此必须评估这些影响新的V-Metrics以证明乳腺癌风险不取决于系统设计。我们将通过比较VC和VR来量化校准的收益，因为以高级图像处理和分析为代价得出增益。我们将确定最佳的乳房密度度量和表示（即需要校准），其中最佳由这些属性定义：自动化，定量，可重现，在不同的成像平台上保持一致，并至少提供与PD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允许将BD全面整合到临床环境中。潜在的应用包括在筛查频率，降低风险干预措施方面对患者的个性化护理以及乳房X线摄影可能无效的情况（即密集组织可显着降低敏感性或乳房X线摄影的特异性）。