Advanced Statistical Analytics of MRI in MS

项目总结 基于MRI的多发性硬化症的定量放射组学分析，通过提取多发性硬化症的成像相关性来执行 病理生理学，已被认为是更准确和更早的诊断的关键，提高了精确度 在临床决策中，以及在靶向多发性硬化症治疗的试验中取得更强大的结果。不幸的是， 这些方法在多发性硬化症中应用仍处于初级阶段，多发性硬化症仍面临一些独特的挑战 在放射学分析可以转化为临床和研究实践之前需要解决。面临的一个重大挑战 MS的诊断和监测是为了区分脑白质病变的异质性，两者都来自于 病因学角度和组织损伤程度。存在融合的病变簇，这些病变 由多个病变组成，特别是在脑室角周围，这对解剖这个构成了一个关键的挑战。 皮损的异质性：尽管组织病理学显示内部和之间的表型差异很大 病变，大多数神经成像研究平均病变簇的度量失去了有价值的信息 每一个单独的损伤。在这项建议中，我们建议使用高级统计分析的信号强度从 多参数成像以区分单个病变并更准确地对其进行表型，因此 有助于更好地了解单个患者的疾病负担，并更容易应用于临床 实践和研究学习。 我们还将创建工具，以促进在 诊所。我们将通过与神经放射科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。 我们还建议开发一种最先进的方法来发现 正常白质和灰质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现和 利用教育资源传播所制定的方法。

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.