Advanced Statistical Analytics of MRI in MS

项目摘要 基于MRI的MS定量放射组学分析，通过提取MS的成像相关物进行 病理生理学，已被认为是更准确和更早的诊断，提高精度的关键 在临床决策中，以及在靶向MS疗法的试验中获得更有力的结果。不幸的是， 这些方法在MS中的应用仍处于起步阶段，MS还面临着一些独特的挑战 在放射组学分析可以转化为临床和研究实践之前需要解决。面临的一大挑战 多发性硬化的诊断和监测是为了从多发性硬化和多发性硬化中分离出白色病变的异质性， 病因学观点和组织损伤程度。存在融合的病灶簇， 由多个病变组成，特别是在心室角周围，这对解剖这一病变构成了关键挑战。 病变的异质性：虽然组织病理学显示在病变内和病变间存在很大的表型变异性， 大多数神经影像学研究对病变群的指标进行平均，丢失了关于 每个单独的损伤。在这个建议中，我们建议使用先进的信号强度统计分析， 多参数成像可区分个体病变并更准确地对其进行表型，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 实践与研究。 我们还将创建工具，以促进在 诊所我们将通过与神经放射学专家评估的比较来验证这些方法，并确定 这些技术的附加值。 我们还建议开发一种最先进的方法来发现 在正常出现的白色物质和灰质中扩散过程中的协变量效应，这将促进 MS病理学和治疗学的许多潜在研究。我们还将开发软件实现， 教育资源，以传播所制定的方法。

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.