MRI Resource for Physiologic, Metabolic and Anatomic Biomarkers

生理、代谢和解剖生物标志物的 MRI 资源

• 批准号: 10270096
• 负责人: Hanzhang Lu
• 金额: $ 159.81万
• 依托单位: HUGO W. MOSER RES INST KENNEDY KRIEGER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10270096
• 关键词: Academia; Address; Aging; Anatomy; Anemia; Area; Bayesian Method; Biological Markers; Biometry; Biopsy; Brain Diseases; Cerebrovascular Disorders; Clinic; Clinical; Clinical Research; Clinical Trials; Complex; Computer software; Consensus; Contrast Media; Country; Critical Pathways; Data; Data Analyses; Data Set; Decision Making; Dementia; Detection; Development; Diagnosis; Diagnostic; Disease; Engineering; Ensure; Evaluation; Failure; Fingerprint; Foundations; Future; Goals; Hospitals; Image; In Situ; Industry; Injections; Injury; Institutes; Ischemia; Joints; Lesion; Libraries; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Manufacturer Name; Measures; Medical; Medical Technology; Metabolic; Methods; Monitor; Pain; Patient Care; Patients; Pharmaceutical Preparations; Pharmacodynamics; Physiologic pulse; Physiological; Process; Prognosis; Property; Public Health Schools; Radiology Specialty; Readiness; Reporting; Reproducibility; Research; Research Personnel; Resources; Sampling; Schools; Science; Services; Site; Speed; Standardization; Techniques; Technology; Terminology; Testing; Therapeutic; Tissues; Training; Translating; Translational Research; Traumatic Brain Injury; United States National Institutes of Health; Universities; Validation; Vocabulary; Water; White Matter Hyperintensity; addiction; base; biomarker validation; candidate marker; candidate validation; clinical care; clinical outcome assessment; clinical practice; computerized data processing; data acquisition; deep learning; design; drug development; glymphatic function; imaging biomarker; improved; innovation; investigator training; magnetic resonance imaging biomarker; medical schools; multimodality; multiscale data; novel; outcome prediction; precision medicine; product development; prognostic; public-private partnership; quantitative imaging; response biomarker; success; tool; working group

SUMMARY The importance of biomarkers in clinical practice and regulatory science is difficult to overstate. The ultimate goal for clinical care of the future is to determine the most appropriate therapy for each patient’s unique version of a particular disease. This concept of precision medicine has been identified as a national priority in the USA, leading to a need for biomarkers that can provide objective and reproducible information. In addition, regulatory science for the approval of new qualified drugs and medical technologies has an urgent need for qualified biomarkers to report on the success or failure of these drugs and technologies. Imaging biomarkers, providing in situ “biopsies”, have the potential to provide such specific information and, ultimately, to improve routine clinical care and speed up development of new treatments. The overall goal of this Resource therefore is to develop novel noninvasive MRI candidate biomarkers that can ultimately be used for: (i) personalized assessment of patients for diagnosis, prognosis, and treatment monitoring; (ii) monitoring of the development of new medical technologies and drugs, i.e. for better guiding of clinical trials. The significance of our proposed developments lies in providing the fundamental design and initial testing (i.e. not clinical trials itself) of new MRI candidate biomarkers with the potential to provide surrogate quantitative imaging endpoints that are as close as possible to clinical endpoints. As such, this Resource will focus on designing, calibrating and standardizing new magnetic resonance (MR) technologies to provide reliable measures across sessions, scanners, and raters. This technology will then be disseminated for larger scale patient studies to allow clinical validation. The MRI Resource for Physiologic, Metabolic and Anatomic Biomarkers is an interdepartmental and interdisciplinary consortium combining facilities and expertise of the F.M. Kirby Research Center at Kennedy Krieger Institute (KKI), the Department of Radiology at Johns Hopkins University (JHU) School of Medicine, the Center for Imaging Science at the JHU Whiting School of Engineering, and the Department of Biostatistics at the JHU Bloomberg School of Public Health. We propose 4 TRD projects, 3 on MR acquisition approaches and one bringing them together with advanced multi-scale data analysis methods that include machine learning. To assure a proper choice of technologies, we will interact closely with a group of clinical and research experts in a push-pull relationship through collaborative projects (CPs) that focus on brain diseases, disorders, and injuries that have a need for new quantitative MR technology to better and noninvasively assess them. These include anemia/ischemia and related white matter hyperintensity lesions (CPs 1,5), aging, cerebrovascular disease, and dementia (CPs 2,7,10), traumatic brain injury (CP4), glymphatic function (CP6), pain (CP8) and addiction (CP9). As an initial testbed for our methods, we also have a group of service projects (SPs) to which we will provide both data acquisition methods and data analysis software. Finally, we will train investigators in their use and disseminate methods nationwide and to MRI manufacturers for even broader application.

概括 生物标志物在临床实践和监管科学中的重要性怎么强调都不为过。 未来临床护理的目标是为每位患者的独特情况确定最合适的治疗方法 精准医疗的概念已被确定为美国的国家优先事项， 导致需要能够提供客观和可重复信息的生物标志物。 新的合格药物和医疗技术的科学审批迫切需要合格的 成像生物标志物，提供 原位“活检”有可能提供此类具体信息，并最终改善常规 因此，本资源的总体目标是临床护理和加速新疗法的开发。 开发新型非侵入性 MRI 候选生物标志物，最终可用于：(i) 个性化 评估患者的诊断、预后和治疗监测；(ii) 监测进展； 新的医疗技术和药物，即更好地指导临床试验我们提出的意义。 发展在于提供新 MRI 的基本设计和初始测试（即不是临床试验本身） 候选生物标志物有可能提供尽可能接近的替代定量成像终点 因此，本资源将重点关注设计、校准和标准化。 新的磁共振 (MR) 技术可跨会话、扫描仪和扫描仪提供可靠的测量 然后，这项技术将被传播到更大规模的患者研究中，以进行临床验证。 生理、代谢和解剖生物标志物的 MRI 资源是跨部门和 跨学科联盟结合了肯尼迪 F.M.柯比研究中心的设施和专业知识 克里格研究所 (KKI)、约翰·霍普金斯大学 (JHU) 医学院放射科、 JHU 怀廷工程学院成像科学中心和生物统计学系 JHU Bloomberg 公共卫生学院提出了 4 个 TRD 项目，其中 3 个是关于 MR 获取方法的项目，以及 一种将它们与包括机器学习在内的先进的多尺度数据分析方法结合在一起。 为了确保技术的正确选择，我们将与一组临床和研究专家密切互动 通过专注于脑部疾病、紊乱和疾病的合作项目 (CP) 建立推拉关系 需要新的定量 MR 技术来更好地、无创地评估损伤。 包括贫血/缺血和相关的白质高信号病变（CPs 1,5）、衰老、脑血管 疾病和痴呆（CP 2、7、10）、创伤性脑损伤（CP4）、类淋巴功能（CP6）、疼痛（CP8）和 作为我们方法的初始测试平台，我们还有一组服务项目（SP）。 最后，我们将提供数据采集方法和数据分析软件。 他们的使用和传播方法在全国范围内以及 MRI 制造商中得到更广泛的应用。