Evaluation of MRI Biomarkers of Breast Cancer Response

乳腺癌反应的 MRI 生物标志物评估

• 批准号: 8020100
• 负责人: Thomas E Yankeelov
• 金额: $ 30.7万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8020100
• 关键词: Address; Anatomy; Basic Science; Biologic Characteristic; Biological Markers; Biopsy; Blood Vessels; Blood flow; Breast Cancer Treatment; Cancer Detection; Cell Density; Cell Size; Cellularity; Characteristics; Clinical; Clinical Sciences; Clinical Trials; Communities; Data; Development; Diffusion Magnetic Resonance Imaging; Environment; Evaluation; Exhibits; Extracellular Space; Gold; Hematoxylin and Eosin Staining Method; Histology; Human; Image; Individual; Judgment; Magnetic Resonance Imaging; Malignant Neoplasms; Mammary Neoplasms; Mammography; Mastectomy; Measurable; Measurement; Measures; Methods; Metric; Monitor; Morphology; Neoadjuvant Therapy; Operative Surgical Procedures; Outcome; PECAM1 gene; Pathologic; Patients; Perfusion; Permeability; Physiological; Reporting; Research; Roentgen Rays; Sampling; Screening procedure; Specimen; Staining method; Stains; Testing; Time; Tissues; Treatment Effectiveness; Treatment Protocols; Ultrasonography; Water; Weight; base; blood perfusion; cancer care; cancer imaging; chemotherapy; clinical care; extracellular; imaging modality; improved; in vivo; insight; malignant breast neoplasm; neoplastic cell; novel; public health relevance; response; treatment response; tumor; water diffusion

DESCRIPTION (provided by applicant): Currently, breast tumor response to chemotherapy is monitored by frank changes in tumor morphology as measured by physical exam, mammography and/or ultrasound. Clinical judgments of the effectiveness of treatments are subjective and prone to error. A repeatable, non-invasive imaging method which can reliably assess tumor response would greatly improve clinical breast cancer care. The specialized magnetic resonance imaging (MRI) methods of dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) have matured to the point where they offer unique information on tumor status. DCE-MRI reports on relevant physiological parameters including vessel perfusion, vessel wall permeability, extravascular extracellular volume fraction, and (recently) cell size. DW-MRI can provide detailed information on tissue cellularity. We propose to combine a novel analysis of DCE-MRI data with DW-MRI data obtained at 3T to provide functional assessments of the response of breast cancer to treatment. We hypothesize that integrating these quantitative MRI methods will provide accurate and predictive measurements of tumor response after the first cycle of treatment. Furthermore, we will validate the imaging metrics by performing quantitative co- registering the in vivo MR images to histopathological staining of ex vivo mastectomy specimens. To test these hypotheses we will pursue three specific aims: 1. In patients selected by a specific treatment protocol, differentiate responders vs. non-responders by the differences in tumor vessel blood flow and integrity, tissue volume fractions, and tumor cell density. 2. Perform uni- and multi-variate correlation analysis between blood flow, vessel perfusion, extravascular extracellular volume fraction, intracellular water lifetime, and cell density to provide a more complete understanding of the breast tumor environment. 3. Perform quantitative co-registration between in vivo MR images and ex vivo histological specimens to validate the MRI measures. The proposed research will combine several new imaging methods to obtain quantitative information on how breast tumors respond to treatment. We hypothesize that this will let us distinguish responders from non- responders early in the course of treatment so that treatments can be optimized on an individual basis. PUBLIC HEALTH RELEVANCE: The proposed research will combine specialized magnetic resonance imaging (MRI) methods to obtain quantitative information on how human breast tumors respond to treatment. Developing methods of tumor characterization that could be applied early in treatment to assess response would have profound impact on the management of many patients. We hypothesize that the combined analysis of contrast enhanced MRI and diffusion MRI data will provide predictive, non-invasive measurements of tumor response to treatment.

描述（由申请人提供）：目前，通过身体检查，乳房X线摄影和/或超声测量的肿瘤形态的坦率变化来监测乳腺肿瘤对化学疗法的反应。关于治疗有效性的临床判断是主观的，容易出错。可以可靠地评估肿瘤反应的可重复的，无创的成像方法将大大改善临床乳腺癌护理。动态对比度增强MRI（DCE-MRI）和扩散加权MRI（DW-MRI）的专用磁共振成像（MRI）方法已经成熟到他们提供有关肿瘤状态的独特信息的地步。 DCE-MRI报告了相关的生理参数，包括血管灌注，血管壁的渗透性，血管外细胞外体积分数以及（最近）细胞大小。 DW-MRI可以提供有关组织细胞性的详细信息。我们建议将DCE-MRI数据的新分析与在3T下获得的DW-MRI数据结合在一起，以提供乳腺癌对治疗反应的功能评估。我们假设整合这些定量MRI方法将在第一个治疗周期后对肿瘤反应进行准确和预测的测量。此外，我们将通过进行定量共同记录体内MR图像，以验证成像指标，以将体内MR图像记录为离体乳房切除术样本的组织病理学染色。为了检验这些假设，我们将追求三个具体目标：1。在特定治疗方案中选择的患者中，通过肿瘤血管血流和完整性，组织体积分数和肿瘤细胞密度的差异来区分反应者与非反应者。 2。在血流，血管灌注，细胞外体积分数，细胞内水寿命和细胞密度之间进行单 - 和多变量的相关分析，以提供对乳腺肿瘤环境的更完整的了解。 3。在体内MR图像和离体组织学标本之间进行定量共同注册以验证MRI测量。拟议的研究将结合几种新的成像方法，以获取有关乳腺肿瘤如何反应治疗的定量信息。我们假设这将使我们在治疗过程中早期将反应者与非反应者区分开，以便可以单独优化治疗。 公共卫生相关性：拟议的研究将结合专门的磁共振成像（MRI）方法，以获取有关人类乳腺肿瘤如何应对治疗的定量信息。开发可以在治疗早期评估反应的肿瘤表征方法将对许多患者的治疗产生深远的影响。我们假设对比对比的组合分析增强了MRI和扩散MRI数据将提供对肿瘤对治疗反应的预测性，无创测量。

项目成果

Optimization of 7-T chemical exchange saturation transfer parameters for validation of glycosaminoglycan and amide proton transfer of fibroglandular breast tissue.

• DOI: 10.1148/radiol.14140762
• 发表时间: 2015-04
• 期刊: Radiology
• 影响因子: 19.7
• 作者: Dula AN;Dewey BE;Arlinghaus LR;Williams JM;Klomp D;Yankeelov TE;Smith S
• 通讯作者: Smith S

Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy.

• DOI: 10.1002/mrm.23203
• 发表时间: 2011-12
• 期刊: MAGNETIC RESONANCE IN MEDICINE
• 影响因子: 3.3
• 作者: Atuegwu, Nkiruka C.;Arlinghaus, Lori R.;Li, Xia;BrianWelch, E.;Chakravarthy, Bapsi A.;Gore, John C.;Yankeelov, Thomas E.
• 通讯作者: Yankeelov, Thomas E.

Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning.

使用定量 MRI 和机器学习早期预测乳腺肿瘤对新辅助化疗的反应。

• 发表时间: 2011
• 期刊: AMIA ... Annual Symposium proceedings. AMIA Symposium
• 作者: Mani,Subramani;Chen,Yukun;Arlinghaus,LoriR;Li,Xia;Chakravarthy,ABapsi;Bhave,SandeepR;Welch,EBrian;Levy,MiaA;Yankeelov,ThomasE
• 通讯作者: Yankeelov,ThomasE