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: 10439901
• 负责人: Hanzhang Lu
• 金额: $ 121.72万
• 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/10439901
• 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; imaging facilities; imaging science; 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很难夸大其词。最终 对未来临床护理的目标是确定每个患者独特版本的最合适的疗法 特定疾病。这种精确医学的概念已被确定为美国的国家优先事项 导致需要提供客观和可重复信息的生物标志物。另外，调节性 批准新合格药物和医疗技术的科学迫切需要合格的 生物标志物报告这些药物和技术的成功或失败。成像生物标志物，提供 原位“活检”，有可能提供此类特定信息，并最终改善常规 临床护理和加快新疗法的发展。因此，该资源的总体目标是 开发新颖的无创MRI候选生物标志物，最终可以用于：（i）个性化 评估患者的诊断，预后和治疗监测； （ii）监视开发 新的医疗技术和药物，即更好地指导临床试验。我们提议的意义 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） 候选生物标志物具有提供替代定量成像终点的潜力 尽可能到临床终点。因此，此资源将重点放在设计，校准和标准化上 新的磁共振（MR）技术可在会话，扫描仪和 评估者。然后，该技术将被传播以进行大规模的患者研究，以允许临床验证。 生理，代谢和解剖生物标志物的MRI资源是部门间的 跨学科财团结合了F.M.的设施和专业知识肯尼迪的柯比研究中心 约翰·霍普金斯大学（JHU）医学院放射学系Krieger Institute（KKI）， JHU Whiting工程学院和生物统计学系的成像科学中心 JHU彭博公共卫生学院。我们提出了4个TRD项目，3个有关MR收购方法的项目和 将它们与包括机器学习在内的高级多尺度数据分析方法一起将它们融合在一起。到 确保适当的技术选择，我们将与一组临床和研究专家紧密互动 通过协作项目（CPS）的推动力关系，专注于脑部疾病，疾病和 需要新的定量MR技术来更好地评估它们的伤害。 包括贫血/缺血和相关的白质高强度病变（CPS 1,5），衰老，脑血管 疾病和痴呆症（CPS 2,7,10），创伤性脑损伤（CP4），胶囊功能（CP6），疼痛（CP8）和 成瘾（CP9）。作为我们方法的初始测试，我们也有一组服务项目（SP） 我们将同时提供数据采集方法和数据分析软件。最后，我们将培训调查人员 他们在全国范围内的使用和传播方法，并向MRI制造商提供更广泛的应用。