Predicting Patient Outcome in Multiple Sclerosis using a Quantitative Radiomic Approach

项目摘要： 多发性硬化症（MS）是青年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 治疗为了对MS进行个性化护理，重要的是利用其临床异质性， 其潜在病理的多样性，并开发能够预测个体行为的模型， 患者在MS患者中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 定量分析放射组学是一种在肿瘤学中发展良好的技术， 图像转换为可挖掘的高维数据，这些数据可以被建模以支持临床决策。 拟议项目的中心假设是，放射组学分析，结合仔细的 特征选择和准确建模，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年成像进行测试， CombiRx试验的临床数据，该试验是一项多中心、III期联合治疗研究， 1008例复发缓解型MS（RRMS）患者。 我们将首先确定非侵入性放射性生物标志物的疾病严重程度的潜力， RRMS。为了实现这一目标，我们将从FLAIR、前和后提取MS病变的放射组学特征。 对比后T1加权MR图像使用开源放射组学管道。通过适当 特征选择，我们将确定一个独立的放射组学特征集，能够表征个体 在选择队列中MRI上的表型。然后，我们将对所选要素进行横截面评估 以确定其在表征疾病严重程度、利用培训和验证 子集我们的辐射组学方法的性能将与传统模型进行比较， 临床和标准成像标记，例如病变体积。 在本提案的第二阶段，我们将探索基于辐射组学的模型的性能， 预测MS的长期结果和治疗反应。我们将建立模型来预测疾病 使用选定的基线放射组学特征评估3年时的无活动状态（DAFS），并确定 治疗反应表型。我们将调查和比较各种 以无偏见的方式建立机器学习模型。最后，我们将评估每一种方法的效果。 通过比较治疗组间的治疗期间变化，评估治疗方案对放射组学特征的影响。 这可能为治疗特异性监测参数提供证据。

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.