Predicting Patient Outcome in Multiple Sclerosis using a Quantitative Radiomic Approach

项目总结： 多发性硬化症(MS)是年轻人和中年人残疾的主要原因，是一种高度危险的疾病。 异质性疾病，在临床表现、病程和对 治疗。为了在MS中实现个性化护理，重要的是利用其临床异质性和 其潜在病理的多样性，并开发能够预测个体行为的模型 病人。多发性硬化症患者常规获得的脑磁共振图像(MRI)包含 反映潜在病理生理学的信息，这些信息可以通过 定量分析。放射组学，一项在肿瘤学领域发展良好的技术，改变了常规医学 将图像转换为可挖掘的高维数据，可对其进行建模以支持临床决策。 拟议项目的中心假设是，放射学分析与仔细的 特征选择和准确的建模，可以预测患者的结果和治疗反应 我们的假设将通过利用现有的3年成像和 来自CombiRx试验的临床数据，这是一项联合治疗的多中心III期研究 1008例复发缓解型多发性硬化(RRMS)患者。 我们将首先确定疾病严重程度的非侵入性放射组学生物标志物的可能性 RRMS。为此，我们将从FLAIR、PRE和 使用开源放射管道的增强后T1加权磁共振图像。通过适当的 特征选择，我们将识别一个独立的放射学特征集，能够表征个体 选择队列中核磁共振的表型。然后，我们将对所选要素进行横截面评估 确定它们在确定疾病严重程度、利用培训和验证方面的有效性 子集。我们的放射组学方法的性能将与传统的使用 临床和标准影像指标，如病变体积。 在这项提案的第二阶段，我们将探索基于放射组学的模型的性能 预测多发性硬化症的长期结果和治疗反应我们将建立预测疾病的模型 3年时使用选定的基线放射学特征的无活动状态(DAFS)，并确定 治疗反应表型。我们将调查和比较各种不同的性能 以不偏不倚的方式建立机器学习模型。最后，我们将评估每个方案的效果 通过比较治疗过程中不同治疗臂的变化，观察治疗方案对放射学特征的影响。 这可能为特定的治疗监测参数提供证据。

Project Summary: Multiple sclerosis (MS), a leading cause of disability in young and middle-aged adults, is a highly heterogeneous disease, with wide variations in clinical presentation, disease course and response to treatment. In order to personalize care in MS, it is important to harness its clinical heterogeneity and the diversity of its underlying pathology, and to develop models able to predict individual behavior of patients. Brain magnetic resonance images (MRI), acquired routinely in MS patients, contain information that reflects underlying pathophysiology, which may be brought into light through quantitative analyses. Radiomics, a technique well developed in oncology, converts routine medical images into mineable high-dimensional data that can be modeled to support clinical decision-making. The central hypothesis of the proposed project is that radiomic analysis, combined with careful feature selection and accurate modeling, can predict patient outcome and response to therapy using standard-of-care MRI in MS. Our hypothesis will be tested by leveraging existing 3-year imaging and clinical data from the CombiRx trial, a multi-center, phase-III investigation of combination therapy in 1008 relapsing-remitting MS (RRMS) patients. We will first determine the potential for non-invasive radiomic biomarkers of disease severity in RRMS. Towards this goal, we will extract radiomic features of MS lesions from FLAIR, pre and postcontrast T1-weighted MR images using an open-source radiomic pipeline. Through appropriate feature selection, we will identify an independent radiomic feature set able to characterize individual phenotype on MRI in a selection cohort. We will then evaluate the selected features cross-sectionally to determine their efficacy in characterizing disease severity, leveraging training and validation subsets. The performance of our radiomic approach will be compared to traditional models using clinical and standard imaging markers such as lesion volume. In the second stage of this proposal, we will explore the performance of radiomic-based models to predict long-term outcome and treatment response in MS. We will build models to predict disease activity free status (DAFS) at 3 years using selected baseline radiomic features, and identify treatment response phenotypes. We will investigate and compare the performance of various machine-learning models in an unbiased manner. Finally, we will assess the effect of each therapeutic regimen on radiomic features by comparing on-treatment changes across treatment arms. This may provide evidence for treatment-specific monitoring parameters.