Quantitative DW-MRI for Early Breast Cancer Treatment Response Assessment

描述（由申请人提供）：乳腺癌的新辅助化疗（NAC）治疗可以通过降低肿瘤分期，同时改善乳房保存，从而提供一个显著降低复发率和死亡率的机会。然而，患者面临的困境是，NAC仅对约70%的患者有效，并且反应是在治疗后期或治疗完成后通过手术切除组织的病理评估确定的，NAC用于确定长期无病生存期。无效的治疗降低了生活质量，增加了费用，并延迟了有效治疗的开始。在这项建议中，开发一种无创成像生物标志物，可以提供长期结果的早期预测，这将彻底改变乳腺癌患者的临床护理。此外，成像将通过为特定患者提供适应系统治疗的机会，为患者提供个性化的护理。本提案将通过高质量的临床数据、分析算法的开发、质量控制和软件实施的进步，采取综合的方法来开发成像协议和方法，将弥散加权MRI应用于乳腺癌患者的管理。1)使用两项多中心前瞻性临床试验获得的临床数据（Cancer Research赞助的英国试验，题为“建立先进半自动功能性磁共振成像在局部晚期乳腺癌对NAC反应的早期预测中的功效”，以及题为“NAC环境下适应性乳腺癌试验设计”的I-Spy 2临床试验）；2)使用新型温控假体实施质量保证方法；3)开发基于可变形配准的分析算法，对DW-MRI数据集进行新颖的、基于体素的和基于roi的分析，以提高成像响应生物标志物的敏感性；4) DW-MRI数据交钥匙分析软件的开发和验证。mri衍生的反应定量测量将被评估为临床结果测量的早期反应预测因素，使用新的分析方法，即在注册数据集上的功能扩散映射（fDM）以及替代的ROI统计。质量保证方法将从我们的MR幻影的多中心使用发展。与主要图像工作站制造商的工业伙伴关系/合作将有助于开发一个具有完整软件算法解决方案的平台，用于临床决策工具。基于DW-MRI数据的早期定量成像生物标志物的开发将为个性化患者护理提供帮助。

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.