Clinical Trial Details
— Status: Not yet recruiting
Administrative data
| NCT number |
NCT04955509 |
| Other study ID # |
M2020400,M2020356 |
| Secondary ID |
|
| Status |
Not yet recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
September 1, 2021 |
| Est. completion date |
June 1, 2023 |
Study information
| Verified date |
September 2020 |
| Source |
Peking University Third Hospital |
| Contact |
Mengze Zhang |
| Phone |
18600393607 |
| Email |
zmzforever[@]pku.edu.cn |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
To construct and validate a software to recognize scar for patients with chronic SCI based on
multimodal MRI.
Description:
Spinal cord injury (SCI) is a kind of serious neurologic damage caused by violence to the
spinal cord, resulting in various functions of the body below the injury level, including
motor, sensory, sphincter, and reflex dysfunction in varying degrees, usually resulting in
permanent and irreversible functional loss or paralysis of patients. The treatment of SCI is
an essential problem in the world. In the past decades, experimental research on SCI involves
genes, proteins, cells, and tissues, and has made great progress. However, these studies
mainly focus on the SCI at the early stage, rather than the later stage. The reason is that
in the later stage, scar formed by glial cells and fibroblasts in the injured area is a
physical and chemical barrier, which inhibits the regeneration and myelination of nerve axons
and results in inhibiting spinal cord repairment. Therefore, before the treatment of chronic
SCI, the key problem is to distinguish glial scar tissue from normal tissue and eliminate its
influence.
As glial scar inhibits axon regeneration, eliminating glial scar is necessary for the repair
of the injured spinal cord. In recent years, a large number of experimental studies have been
carried out to destroy the process of glial scar formation after SCI by enzyme digestion and
antibody. Though these methods reduced glial scar, residual glial scars were reported in
animal experiments. Compared to biochemical methods, surgical resection of glial scar tissue
is a relatively simple and effective method to eliminate glial scars. Due to the limited
regeneration ability of nerves after SCI, it is important to identify scar tissue accurately
before operations to avoid surgical injury to normal tissue, which is also the premise of
further research and clinical application of various interventional treatment methods.
Magnetic resonance imaging (MRI) is one of the most commonly used non-invasive imaging
techniques to evaluate the degree of injury and therapeutic effect of SCI. Nemours MRI
studies on SCI show the impact of SCI on the central nervous system from the structural and
functional level and prove the potential application value of MRI in assisting doctors in the
diagnosis of SCI. A small number of previous studies have used magnetization transfer
imaging, and diffusion tensor imaging to detect glial scar tissue, showing the potential
application value of these images in differentiation between glial scar and surrounding
normal spinal cord. However, because glial cells, which constitute glial scar, are also
important components of normal spinal cord tissue, previous studies only identified glial
scar from a single aspect, such as tissue type, macromolecular component, or water molecular
diffusion strength. Therefore, their specificities were unsatisfactory. Relative methods were
unable to identify glial scar accurately and finally resulted in difficulty in treatment
arrangement and evaluation of prognosis, which hinders the development of SCI treatment
research.
Combing multimodal MRI, including conventional MRI and diffusion MRI, with supervised machine
learning makes accurate glial identification in chronic SCI possible. multimodal MRI can
depict the differences between scar tissue and non-scar tissue from the aspects of cell
composition, water molecular dispersion, structural complexity, etc. Comparing to MRI with a
single model, multimodal MRI provides more specific features. Machine learning, a way to
construct robust and accurate models, can mine the quantitative relationship between imaging
features and clinical diagnosis results, reveal MRI feature markers of the glial scar, to
improve the accuracy of identification. The research work, combined with medicine, imaging,
and artificial intelligence technology, is expected to solve the problem of accurate and
non-invasive identification of glial scar in chronic SCI, which has potential application
value for laboratory research and clinical treatment of chronic SCI.
