Quantitative DW-MRI for Early Breast Cancer Treatment Response Assessment

用于早期乳腺癌治疗反应评估的定量 DW-MRI

• 批准号: 8676478
• 负责人: THOMAS L CHENEVERT
• 金额: $ 49.71万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-09 至 2015-09-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8676478
• 关键词: Advanced Development; Algorithmic Software; Algorithms; Automation; Biological Markers; Biological Preservation; Breast; Breast Cancer Treatment; Calibration; Cancer Patient; Characteristics; Clinical; Clinical Data; Clinical Trials; Clinical Trials Database; Clinical Trials Design; Collaborations; Computer software; Data; Data Set; Death Rate; Development; Diagnostic Neoplasm Staging; Diffusion; Diffusion Magnetic Resonance Imaging; Disease-Free Survival; Evaluation; Fatty acid glycerol esters; Freedom; Hour; Image; Lesion; Location; Magnetic Resonance Imaging; Mammary Neoplasms; Manufacturer Name; Maps; Measurement; Measures; Methods; Metric; Michigan; Morphologic artifacts; Multi-Institutional Clinical Trial; Multicenter Trials; Neoadjuvant Therapy; Oncologist; Outcome; Outcome Measure; Pathologic; Patient Care; Patients; Process; Protocols documentation; Quality Control; Quality of life; Recurrence; Reproducibility; Site; Solutions; System; Systemic Therapy; Temperature; Testing; Tissues; Treatment outcome; Tumor stage; Universities; Validation; Variant; Vendor; anticancer research; base; chemotherapy; clinical care; clinical decision-making; computerized; cost; design; effective therapy; image processing; image registration; improved; malignant breast neoplasm; novel; prospective; quality assurance; response; statistics; surgical research; tool; treatment response; tumor

DESCRIPTION (provided by applicant): Breast cancer treatment with neoadjuvant chemotherapy (NAC) can provide an opportunity for achieving a major decrease in recurrence and death rates by down-staging the tumor while improving breast preservation. However, the dilemma for patients is that NAC is only effective in about 70% of patients and the response is determined late or on completion of therapy with pathologic assessment of surgically excised tissue following NAC used to determine long-term disease-free survival. Ineffective therapy decreases quality of life, increases costs, and delays commencement of effective treatment. In this proposal, development of a noninvasive imaging biomarker which could provide for very early prediction of long-term outcome would revolutionize the clinical care of breast cancer patients. Furthermore, imaging would provide patient care to be individualized by providing an opportunity to adapt systemic treatment to a particular patient. This proposal will undertake a comprehensive approach to develop imaging protocols and methods for applying diffusion-weighted MRI for management of breast cancer patients through availability of high-quality clinical data, analytical algorithm development, advances in quality control and software implementation: 1) Use of clinical data obtained from two multi-center prospective clinical trials (Cancer Research sponsored UK trial entitled "Establishing the Efficacy of Advanced Semi-automated Functional MR Imaging in the Early Prediction of Response of Locally Advanced Breast Cancer to NAC" as well as the I-Spy 2 clinical trial entitled "An adaptive breast cancer trial design in the setting of NAC"; 2) Implementation of quality assurance methods using a novel temperature controlled phantom; 3) Development of analytical algorithms using deformable registration for novel, voxel-based as well as ROI-based analysis of DW-MRI data sets to enhance the sensitivity of the imaging response biomarker; 4) Development and validation of a software application for turn-key analysis of DW-MRI data. MRI-derived quantitative measurements of response will be evaluated as early response predictors of clinical outcome measures using novel analytical approaches, i.e. functional diffusion mapping (fDM) on registered data sets along with alternative ROI statistics. Quality assurance methods will be developed from multi-center use of our MR phantom. An industrial partnership/collaboration with a major image workstation manufacturer will assist with development of a platform with a complete software algorithmic solution for use as a clinical decision tool. Development of an early quantitative imaging biomarker based on DW-MRI data would provide for individualized patient care.

描述（由申请人提供）：乳腺癌新辅助化疗（NAC）治疗可以通过降低肿瘤分期，同时改善乳房保护，从而大幅降低复发率和死亡率。然而，患者的困境是NAC仅在约70%的患者中有效，并且反应是在晚期或在治疗完成时通过NAC后的手术切除组织的病理学评估来确定的，以确定长期无病生存。无效的治疗会降低生活质量，增加成本，并延迟有效治疗的开始。在这项提案中，开发一种非侵入性的成像生物标志物，可以提供非常早期的预测长期的结果将彻底改变乳腺癌患者的临床护理。此外，成像将通过提供使系统治疗适应特定患者的机会来提供个性化的患者护理。该提案将采取综合方法，通过提供高质量的临床数据、分析算法开发、质量控制和软件实施方面的进展，制定应用扩散加权MRI管理乳腺癌患者的成像方案和方法：1）使用从两个多中心前瞻性临床试验获得的临床数据（癌症研究赞助的英国试验，题为“建立先进的半自动功能性磁共振成像在早期预测局部晚期乳腺癌对NAC的反应中的功效”，以及I-Spy 2临床试验，题为“NAC背景下的适应性乳腺癌试验设计”; 2）使用新型温控体模实施质量保证方法; 3）开发使用可变形配准的分析算法，DW-MRI数据集的基于体素以及基于ROI的分析，以增强成像响应生物标志物的灵敏度; 4）DW-MRI数据交钥匙分析软件应用程序的开发和验证。将使用新的分析方法，即在登记的数据集上沿着替代ROI统计数据的功能性扩散映射（fDM），将MRI衍生的缓解定量测量值作为临床结局指标的早期缓解预测因子进行评价。质量保证方法将从我们的MR体模的多中心使用中开发。与主要影像工作站制造商的工业合作伙伴关系/协作将有助于开发一个平台，该平台具有完整的软件算法解决方案，可用作临床决策工具。基于DW-MRI数据的早期定量成像生物标志物的开发将提供个性化的患者护理。