Quantitative assessment of breast MRIs for breast cancer risk prediction

乳腺 MRI 定量评估用于乳腺癌风险预测

• 批准号: 9274819
• 负责人: Shandong Wu
• 金额: $ 31.7万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9274819
• 关键词: Adverse effects; Algorithms; American Cancer Society; Anatomy; Biologic Characteristic; Biological Markers; Blood Vessels; Breast; Breast Cancer Risk Factor; Characteristics; Clinic; Clinical; Computational algorithm; Computing Methodologies; Contrast Media; Data; Decision Making; Diagnostic; Goals; High Risk Woman; High-Risk Cancer; Image; Individual; Intervention; Investigation; Joints; Kinetics; MRI Scans; Magnetic Resonance Imaging; Malignant Neoplasms; Mammary Gland Parenchyma; Mammographic Density; Mammography; Masks; Measures; Medical Oncologist; Medical center; Modeling; Molecular; Outcome Study; Ovariectomy; Patients; Populations at Risk; Predictive Value; Property; Raloxifene; Reporting; Reproducibility; Research Design; Resolution; Risk; Risk Assessment; Risk Factors; Risk Management; Role; Salpingo-Oophorectomy; Scanning; Tamoxifen; Time; Time trend; Tissues; Variant; Woman; Women' s Group; Work; base; breast density; cancer diagnosis; cancer risk; cancer subtypes; case control; clinical decision-making; computerized; contrast enhanced; density; digital; high risk; high risk population; imaging biomarker; imaging scientist; improved; in vivo; longitudinal analysis; longitudinal dataset; magnetic resonance imaging biomarker; malignant breast neoplasm; multidisciplinary; non-invasive imaging; programs; public health relevance; quantitative imaging; radiologist; response; response biomarker; routine imaging; screening

* DESCRIPTION (provided by applicant): Many women who are considered at high risk of developing breast cancer struggle to choose enhanced surveillance or risk-reducing interventions, which can be highly invasive and have negative side effects. Such decisions are highly personal, requiring accurate quantification of individual risk and response characteristics to risk-reducing interventions. Current breast cancer risk assessment in the clinic is imprecise at the individual level. A personalized risk assessment that incorporates a woman's particular risk profile, such as anatomical, functional, or biological characteristics of her breast, can help to individualize breast cancer risk management. Mammographic breast percentage density (MPD) has been an established independent risk factor. Recent advances in breast magnetic resonance imaging (MRI) provide exquisite and high-resolution capabilities to characterize in-vivo properties of breast tissue that are related to breast cancer risk. Recent studies, including our pilot quantitative studies, indicate that: 1) volumetric fibroglandular (i.e., dense) tissue (FGT), contrast enhancement of FGT (aka background parenchymal enhancement [BPE]), and enhancement kinetics computed on normal (not cancerous) breast tissue, all measured from dynamic contrast-enhanced MRI (DCE- MRI), are predictive of breast cancer risk; and 2) changes of BPE and FGT after risk-reducing interventions measure patients' responses to applied intervention. We propose to investigate the clinical utility of breast MRI- based quantitative measures as new non-invasive breast cancer risk factors. Our hypothesis is that objectively quantified BPE, kinetics, and FGT measured on breast DCE-MRI are biomarkers of breast cancer risk and response to risk-reducing interventions, providing predictive value independent of MPD. We will optimize our automated computer algorithms and retrospectively analyze the DCE-MRI scans of 600 women in a case- control setting, including analysis of longitudinal MRI scans acquired over an 8-year timeframe. We will assess the MRI-derived measures as a response biomarker to risk-reducing interventions (e.g., salpingo- oophorectomy, or tamoxifen/raloxifene). We have achieved strong preliminary results across all of the proposed aims. This project will combine the multi-disciplinary expertise of a computational imaging scientist, radiologists, a medical oncologist (breast cancer high-risk program director), and a biostatistician. This study is the first of its kind that uses fully automated computerized analysi to develop significant breast DCE-MRI- derived risk biomarkers. Quantitative DCE-MRI-based biomarkers will advance our understanding of intrinsic breast characteristics pertaining to individual risk profiles. This study will provide strong data and rationale for incorporating quantitative breast DCE-MRI-derived biomarkers to more accurately assess breast cancer risk and to aid in the decision-making regarding risk-reducing interventions, all at the individual leve. This study will optimize the use of a large volume of breast DCE-MRIs that are routinely performed in major medical centers; the outcome of this study is therefore highly translational to the clinic.

* 描述（由申请人提供）：许多被认为具有患乳腺癌高风险的女性很难选择加强监测或降低风险的干预措施，这些措施可能具有高度侵入性并具有负面副作用，此类决定是高度个人化的，需要准确的量化。目前临床上的乳腺癌风险评估并不准确。 个体层面的个性化风险评估已成为一种独立的方法，该评估结合了女性特定的风险状况，例如乳房的解剖、功能或生物学特征，有助于个体化乳腺癌风险管理。乳腺磁共振成像 (MRI) 的最新进展提供了精确的高分辨率能力来表征与乳腺癌风险相关的乳腺组织的体内特性。最近的研究（包括我们的试点定量研究）表明：1。 ) 体积纤维腺（即致密）组织 (FGT)、FGT 对比增强（又名背景实质增强 [BPE]）以及对正常（非癌性）乳腺组织计算的增强动力学，全部通过动态对比增强 MRI (DCE-MRI) 测量) )，可预测乳腺癌风险；2) 降低风险干预措施后 BPE 和 FGT 的变化测量患者对所应用干预措施的反应。基于 MRI 的定量测量作为新的非侵入性乳腺癌风险因素，我们的假设是，在乳腺 DCE-MRI 上测量的客观量化 BPE、动力学和 FGT 是乳腺癌风险和对降低风险干预措施的反应的生物标志物，提供预测价值。我们将优化我们的自动化计算机算法，并在病例对照环境中回顾性分析 600 名女性的 DCE-MRI 扫描，包括对 8 年时间范围内获得的纵向 MRI 扫描进行分析。评估 MRI 衍生措施作为降低风险干预措施（例如输卵管卵巢切除术或他莫昔芬/雷洛昔芬）的反应生物标志物。该项目将结合多学科专业知识。这项研究由计算成像科学家、放射科医生、肿瘤内科医师（乳腺癌高危项目主任）和生物统计学家共同参与，是此类研究中第一项使用全自动计算机分析来进行研究的研究。开发重要的乳腺 DCE-MRI 衍生的风险生物标志物 基于定量 DCE-MRI 的生物标志物将增进我们对与个体风险概况相关的乳腺固有特征的理解。为了更准确地评估乳腺癌风险并帮助做出有关降低风险干预措施的决策，所有这些都在个体层面上进行。这项研究将优化主要医疗中心常规进行的大量乳腺 DCE-MRI 的使用。结果；因此，这项研究对临床具有高度转化作用。