High-dimensional Modeling of PET for radiomic Biomarker Discovery

用于放射组学生物标志物发现的 PET 高维建模

基本信息

批准号：
10686238
负责人：
Ani Eloyan
金额：
$ 70.91万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10686238
关键词：
Alzheimer&apos s Disease Alzheimer&apos s disease patient Amyloid Atrophic Bayesian neural network Binding Biological Biological Markers Brain Clinical Clinical Research Clinical Treatment Clinical Trials Cognitive Complex Data Data Analyses Data Collection Development Dimensions Disease Disease Progression Epidemiology Etiology Future Goals Image Imaging technology Impaired cognition Late Onset Alzheimer Disease Learning Magnetic Resonance Imaging Mathematics Measurement Measures Mediator Medical Imaging Methods Modeling Morphology Multimodal Imaging Noise Observational Study Outcome Output Participant Pathology Patients Physiological Population Positron-Emission Tomography Research Methodology Scanning Severities Severity of illness Shapes Site Standardization Statistical Methods Statistical Models Structure Techniques Technology Testing Texture Time Tracer Uncertainty Visit Visualization automated algorithm biomarker discovery biomarker identification biomedical imaging cerebral atrophy clinical predictors cognitive performance complex data deep learning design disease diagnosis drug development early onset feature extraction high dimensionality imaging modality improved independent component analysis insight interest learning strategy longitudinal analysis longitudinal positron emission tomography multimodal data multimodality nervous system disorder neural network neuroimaging neuroimaging marker novel novel strategies patient biomarkers predictive modeling primary outcome radiological imaging radiomics secondary outcome statistics success targeted treatment tau Proteins time interval tool uptake

项目摘要

Project Summary We propose to develop statistical methods for the analysis of longitudinal positron emission tomography (PET) data for patients with Alzheimer’s disease (AD). Disease biomarkers identified from PET data are necessary for integrating these imaging modalities in observational studies and in clinical trials for drug development for AD. We propose statistical methods for the analysis of PET images including dimension reduction and biomarker extraction from voxel-wise intensity analysis that incorporate disease progression in two observational studies including data on early-onset and late-onset AD participants. Our methods allow for integration of MRI atrophy measures in the analyses to obtain multimodal predictors of disease severity and progression. The proposed methods will utilize the complex data and noise structure for developing powerful tools for biomarker identification that can be used for finding differences of disease progression between populations of interest. Particularly, our proposed biomarkers incorporate information on shape and texture of radiological images for prediction. The proposed methods can be incorporated to analyze data in future studies in most AD data collection centers as well as to apply in radiomics of imaging data in general.

项目摘要我们建议开发统计方法，以分析纵向正电子发射断层扫描（PET）阿尔茨海默氏病（AD）患者的数据。从PET数据中确定的疾病生物标志物是必需的将这些成像方式整合在观察性研究和AD药物开发的临床试验中。我们提出了用于分析PET图像在内的统计方法，包括降低尺寸和生物标志物从体素强度分析中提取，在两项观察性研究中结合了疾病进展包括有关早发和晚发广告参与者的数据。我们的方法允许整合MRI萎缩分析中的措施获得了疾病严重程度和进展的多模式预测指标。提议方法将利用复杂的数据和噪声结构来开发强大的生物标记识别工具可用于查找目标人群之间疾病进展的差异。特别是我们的提议的生物标志物包含有关放射学图像的形状和纹理的信息，以进行预测。这可以将建议的方法合并以在大多数AD数据收集中心的未来研究中分析数据通常应用成像数据的放射组学。