Spatiotemporal Modeling of MRI Brain Lesion Trajectories of Biomarker Discovery

生物标志物发现的 MRI 脑损伤轨迹时空建模

• 批准号: 9269638
• 负责人: Ani Eloyan
• 金额: $ 23.1万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-15 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9269638
• 关键词: Address; Affect; Area; Bioinformatics; Biological Markers; Brain; Brain imaging; Characteristics; Clinical; Clinical Trials; Complex; Country; Data; Data Analyses; Data Quality; Demyelinations; Development; Disease; Disease Progression; Engineering; Epidemiology; Etiology; Evolution; Future; Goals; Image; Imagery; Imaging technology; Incidence; Inflammatory; Lead; Lesion; Location; Magnetic Resonance Imaging; Measurement; Medical Imaging; Methodology; Methods; Modality; Modeling; Multimodal Imaging; Multiple Sclerosis; Natural History; Necrosis; Observational Study; Outcome; Pathology; Patients; Pattern; Population; Process; Sampling; Series; Severity of illness; Statistical Methods; Statistical Models; Structure; Study models; Techniques; Technology; Time; Tissues; analytical method; base; bioimaging; biomarker development; biomarker discovery; biomarker identification; clinical practice; computer science; data structure; design; high dimensionality; imaging study; improved; in vivo; insight; longitudinal analysis; magnetic resonance imaging biomarker; multimodality; multiple sclerosis patient; multiple sclerosis treatment; neuroimaging; public health relevance; repaired; semiparametric; spatiotemporal; success; symptomatology; treatment effect; treatment trial; white matter

 DESCRIPTION (provided by applicant): We propose to develop statistical methods for the analysis of longitudinal magnetic reso- nance imaging (MRI) data for patients with multiple sclerosis (MS). Disease biomarkers identified from MRI are necessary for studying disease progression in observational studies and for assessing treatment effects of therapies in clinical trials. We propose statistical methods for the analysis of longitudinal MRI intensity time courses that integrate information across multiple modalities. The proposed methods will harness the complex data structure of clinical MRI for identifying biomarkers that can be utilized in future studies and that are implementable in MS centers across the country.

 描述（由适用提供）：我们建议开发用于分析多发性硬化症患者（MS）患者的纵向磁性转化成像（MRI）数据的统计方法。从MRI中鉴定出的疾病生物标志物对于研究观察性研究中的疾病进展和评估临床试验中疗法的治疗效果是必要的。我们提出了用于分析纵向MRI强度时间课程的统计方法，这些方法跨多种方式整合了信息。提出的方法将利用临床MRI的复杂数据结构来识别可以在未来的研究中使用的生物标志物，并在全国MS中心实施。