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

> **NIH NIH R21** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2021 · $234,000

## Abstract

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.

## Key facts

- **NIH application ID:** 10217627
- **Project number:** 1R21NS118320-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Refaat E Gabr
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $234,000
- **Award type:** 1
- **Project period:** 2021-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10217627

## Citation

> US National Institutes of Health, RePORTER application 10217627, Detection and evolution of diffusely abnormal white matter in multiple sclerosis: a deep learning approach (1R21NS118320-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10217627. Licensed CC0.

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