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妇女难以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。这种决定是高度个人化的，需要准确量化个人风险和对减少风险干预措施的反应特点。目前临床上的乳腺癌风险评估是不准确的， 个人层面。一个个性化的风险评估，结合妇女的特定风险状况，如解剖，功能，或她的乳房的生物学特征，可以帮助个性化的乳腺癌风险管理。乳腺摄影乳腺密度百分比（MPD）已被确定为一个独立的危险因素。乳腺磁共振成像（MRI）的最新进展提供了精细和高分辨率的能力，以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的对比度增强（也称为背景实质增强[BPE]）和在正常（非癌性）乳腺组织上计算的增强动力学（全部由动态对比度增强MRI（DCE-MRI）测量）可预测乳腺癌风险;和2）降低风险干预后BPE和FGT的变化测量患者对所施加干预的反应。我们建议研究乳腺MRI定量测量作为新的非侵入性乳腺癌危险因素的临床实用性。我们的假设是，客观量化的BPE，动力学，和FGT测量乳腺DCE-MRI是乳腺癌的风险和反应的生物标志物，降低风险的干预措施，提供独立于MPD的预测价值。我们将优化我们的自动化计算机算法，并回顾性分析病例对照设置中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扫描的分析。我们将评估MRI衍生指标作为降低风险干预措施的反应生物标志物（例如，输卵管卵巢切除术或他莫昔芬/雷洛昔芬）。我们在所有拟议目标方面都取得了初步成果。这个项目将结合联合收割机的计算成像科学家，放射科医生，医学肿瘤学家（乳腺癌高危项目主任），和生物统计学家的多学科专业知识。这项研究是第一个使用全自动计算机化分析来开发显着的乳腺DCE-MRI衍生风险生物标志物的研究。定量DCE-MRI为基础的生物标志物将推进我们的内在乳腺特征的个人风险概况的理解。这项研究将提供强有力的数据和理由，纳入定量乳腺DCE-MRI衍生的生物标志物，以更准确地评估乳腺癌风险，并帮助决策有关的风险降低干预措施，所有在个人水平。本研究将优化在主要医疗中心常规进行的大量乳腺DCE-MRI的使用;因此，本研究的结果高度转化为临床结果。