Advanced Statistical Analytics of MRI in MS

MS 中 MRI 的高级统计分析

基本信息

批准号：
10561725
负责人：
Russell Takeshi Shinohara
金额：
$ 56.68万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10561725
关键词：
Adoption Biological Markers Biological Process Blood Vessels Brain Central Vein Chronic Clinic Clinical Clinical Research Data Detection Development Diagnosis Diagnostic Diffuse Disease Disease Progression Etiology Functional disorder Future Goals Heterogeneity Histopathology Horns Image Image Analysis Individual Lesion Location Magnetic Resonance Imaging Measures Methods Microglia Monitor Morphology Multimodal Imaging Multiple Sclerosis Multiple Sclerosis Lesions Myelin Neurologist Outcome Pathology Patients Pattern Phase Phenotype Predisposition Process Research Sequence Analysis Severities Signal Transduction Statistical Data Interpretation Statistical Methods System T2 weighted imaging Techniques Therapeutic Time Tissues Translating Translations Validation Ventricular Visit Visual Work analysis pipeline automated analysis burden of illness clinical decision-making clinical practice clinically relevant density detection method diagnostic accuracy disability education resources gray matter illness length imaging biomarker imaging study improved indexing individual patient infancy ischemic lesion magnetic resonance imaging biomarker multimodality multiparametric imaging neuroimaging neuropathology novel older patient precision medicine radiologist radiomics repaired research study software development statistics stem targeted treatment tissue injury tissue repair tool white matter

项目摘要

PROJECT SUMMARY Quantitative radiomic analysis of MS based on MRI, performed by extracting imaging correlates of MS pathophysiology, has been recognized as critical for more accurate and earlier diagnostics, improved precision in clinical decision-making, and more powerful outcomes in trials for targeted MS therapeutics. Unfortunately, the application of these approaches in MS are still in their infancy and several challenges unique to MS remain to be solved before radiomic analyses can be translated in clinical and research practice. A major challenge for the diagnosis and monitoring of MS is to disentangle the heterogeneity of white matter lesions, both from an etiologic perspective and in the degree of tissue injury. The presence of confluent clusters of lesions that are comprised of multiple lesions, particularly around the ventricular horns, poses a key challenge for dissecting this heterogeneity in lesions: while histopathology shows great phenotypic variability both within and between lesions, most neuroimaging studies average metrics across lesion clusters losing the valuable information about each individual lesion. In this proposal, we propose to use advanced statistical analysis of signal intensity from multi-parametric imaging to distinguish individual lesions and more accurately phenotype them, and thus facilitate much greater understanding of an individual patients burden of disease and easier application to clinical practice and research studies. We will also create tools that will facilitate the adoption of these techniques in the clinic. We will validate these approaches by comparison to expert neuroradiologist assessments and determine added value of these techniques. We further propose to develop a state-of-the-art method for the discovery of covariate effects in diffuse processes in the normal-appearing white matter and gray matter, which will facilitate many potential studies of MS pathology and therapeutics. We will also develop software implementations and educational resources to disseminate the methods developed.

项目概要基于 MRI 的 MS 定量放射组学分析，通过提取 MS 的成像相关性进行病理生理学，已被认为对于更准确、更早的诊断、提高精度至关重要临床决策，以及靶向多发性硬化症治疗试验中更强有力的结果。很遗憾，这些方法在 MS 中的应用仍处于起步阶段，并且 MS 特有的一些挑战仍然存在在放射组学分析应用于临床和研究实践之前需要解决这个问题。一个重大挑战 MS 的诊断和监测的目的是从白质病变的异质性中解脱出来病因学视角和组织损伤程度。存在融合的病变簇由多个病变组成，特别是在心室角周围，对解剖该病变提出了关键挑战病变的异质性：虽然组织病理学显示病变内部和病变之间存在很大的表型变异性病变，大多数神经影像学研究平均指标跨病变簇丢失了有价值的信息每个单独的病变。在本提案中，我们建议使用先进的信号强度统计分析多参数成像可区分单个病变并更准确地对它们进行表型分析，从而有助于更好地了解个体患者的疾病负担，并更容易应用于临床实践和研究。我们还将创建工具来促进这些技术的采用诊所。我们将通过与神经放射学家专家的评估进行比较来验证这些方法，并确定这些技术的附加值。我们进一步建议开发一种最先进的方法来发现正常出现的白质和灰质的弥散过程中的协变量效应，这将有助于许多关于多发性硬化症病理学和治疗学的潜在研究。我们还将开发软件实施和传播所制定的教育资源的方法。