Detection and evolution of diffusely abnormal white matter in multiple sclerosis: a deep learning approach

多发性硬化症中弥漫性异常白质的检测和进化：深度学习方法

• 批准号: 10379091
• 负责人: KHADER M HASAN
• 金额: $ 19.5万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10379091
• 关键词: Affect; Artificial Intelligence; Attenuated; Big Data; Blinded; Cerebrospinal Fluid; Characteristics; Clinical; Clinical Management; Complex; Conduct Clinical Trials; Data; Demyelinating Diseases; Demyelinations; Detection; Development; Diagnosis; Diffuse; Disease; Disease Progression; Engineering; Evolution; Frequencies; Functional disorder; Future; Gliosis; Goals; Histology; Image; Image Analysis; Individual; Knowledge; Label; Lead; Learning; Lesion; Liquid substance; Location; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Medical Imaging; Methods; Multi-Institutional Clinical Trial; Multiple Sclerosis; Multiple Sclerosis Lesions; Nature; Nerve Degeneration; Pathologic Processes; Patients; Pattern; Persons; Pharmaceutical Preparations; Phase; Play; Prevalence; Process; Protons; Publications; Randomized Clinical Trials; Recovery; Relapsing-Remitting Multiple Sclerosis; Reporting; Resolution; Resources; Role; Scanning; T2 weighted imaging; Techniques; Testing; Therapeutic; Time; Training; United States National Institutes of Health; Validation; automated segmentation; axonopathy; base; brain magnetic resonance imaging; brain tissue; burden of illness; cohort; convolutional neural network; cost; deep learning; deep learning model; deep neural network; density; follow-up; gray matter; imaging modality; improved outcome; large datasets; machine learning algorithm; model development; multiple sclerosis patient; nervous system disorder; neuroimaging; novel; success; targeted treatment; treatment effect; white matter; young adult

Multiple sclerosis (MS) is the most widespread non-traumatic, demyelinating disorder in young adults. Magnetic resonance imaging (MRI) aids in both diagnosing MS and assisting clinical management of patients. In addition to focal MS lesions, diffusely abnormal white matter (DAWM) is also seen on brain MRI in MS patients. While not understood completely, DAWM is thought to be a predictor of disease burden, possibly appears early on in the disease, and may be a marker of neurodegeneration in MS. However, longitudinal studies of DAWM are lacking, and segmentation of DAWM is manual, making it difficult to study the evolution of DAWM. The main objective of this proposal is to longitudinally study the development of DAWM in MS. This objective will be realized by analyzing preexisting longitudinal MRI data acquired on 1008 MS patients who participated in phase 3, blinded, multi-center clinical trial, referred to as CombiRx that was supported by NIH. The CombiRx data includes multi-contrast MRI and various clinical measures. Automatic identification of DAWM is a critical component of this proposal. Based on our preliminary studies, deep Learning (a class of machine learning algorithms) has the potential to automatically identify DAWM and estimate its volume. We will use the large CombiRx MRI data for training, validation, and testing of the deep learning models, and to study DAWM evolution in this MS cohort. The proposal has two major aims. In the first aim we will develop a deep learning model based on fully-convolutional neural networks for automatic segmentation of DAWM, gray matter, normal appearing white matter, and T2-hyperintense lesions guided by manual segmentation of two neuroimaging experts. In the second aim we will segment DAWM and all brain tissues, including focal lesions, at baseline and all available follow-up scans in the CombiRx cohort (up to 6.5 years). The temporal changes in volume, location, and MRI parameters of DAWM and focal T2 lesions will be computed. We will finally test whether DAWM is precursor to focal T2 lesions, associated with T2 lesion resolution, or a separate disease process altogether. If DAWM is shown to occur early on in the disease, it is possible to intervene sooner for improved outcome. Similarly, if DAWM is shown to be related to disease activity, it can serve as an objective and quantitative measure of the disease. Such an objective measurement would be highly valuable in developing targeted therapies and also in evaluating the treatment effect in MS patients.

多发性硬化症（MS）是年轻人中最普遍的非创伤性脱髓鞘疾病。 磁共振成像（MRI）有助于诊断MS和协助患者的临床管理。 除了局灶性MS病变，弥漫性异常白色物质（DAWM）也见于MS的脑部MRI 患者虽然尚未完全理解，但DAWM被认为是疾病负担的预测因子，可能是 在疾病早期出现，可能是MS神经退行性变的标志。 DAWM的研究缺乏，DAWM的分割是手动的，使得难以研究演化 关于DAWM本提案的主要目的是纵向研究MS中DAWM的发展。 通过分析1008例MS患者的既往纵向MRI数据， 参与了由NIH支持的III期、盲态、多中心临床试验，称为CombiRx。 CombiRx数据包括多对比MRI和各种临床指标。自动识别 DAWM是该提案的关键组成部分。基于我们的初步研究，深度学习（一类 机器学习算法）具有自动识别DAWM并估计其体积的潜力。我们将 使用大型CombiRx MRI数据对深度学习模型进行训练、验证和测试，并研究 该MS队列中的DAWM演变。该提案有两个主要目标。在第一个目标中，我们将发展一个深刻的 基于全卷积神经网络的DAWM、灰度自动分割学习模型 通过手动分割两个病灶，显示正常的白色物质和T2高信号病灶 神经影像学专家在第二个目标中，我们将分割DAWM和所有脑组织，包括局灶性病变， 基线时和CombiRx队列中所有可用的随访扫描（长达6.5年）。的时序变化 将计算DAWM和局灶性T2病变的体积、位置和MRI参数。我们将最终测试 DAWM是否是局灶性T2病变的前兆，与T2病变消退相关，或单独的疾病 整个过程。如果DAWM被证明在疾病的早期发生，就有可能更早地进行干预， 改善结果。同样，如果DAWM被证明与疾病活动相关，则它可以作为一个目标 和疾病的定量测量。这种客观的衡量方法在以下方面非常有价值： 开发靶向治疗，并评估MS患者的治疗效果。