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: 10217627
• 负责人: Refaat E Gabr
• 金额: $ 23.4万
• 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 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10217627
• 关键词: Affect; Artificial Intelligence; Attenuated; Big Data; Blinded; Brain; 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; Modeling; Multi-Institutional Clinical Trial; Multiple Sclerosis; Multiple Sclerosis Lesions; Nature; Nerve Degeneration; Pathologic Processes; Patients; Pattern; 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 tissue; burden of illness; cohort; convolutional neural network; cost; deep learning; 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。