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A Scar Recognition Software for Chronic Spinal Cord Injury (SCI)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT04955509
Recruitment Status : Unknown
Verified September 2020 by Peking University Third Hospital.
Recruitment status was:  Not yet recruiting
First Posted : July 8, 2021
Last Update Posted : July 8, 2021
Information provided by (Responsible Party):
Peking University Third Hospital

Brief Summary:
To construct and validate a software to recognize scar for patients with chronic SCI based on multimodal MRI.

Condition or disease Intervention/treatment
Spinal Cord Injury Other: MRI

Detailed 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.

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Study Type : Observational
Estimated Enrollment : 25 participants
Observational Model: Cohort
Time Perspective: Other
Official Title: In Vivo Optimization and Clinical Application of a Scar Recognition Software for Chronic Spinal Cord Injury (SCI)
Estimated Study Start Date : September 1, 2021
Estimated Primary Completion Date : December 1, 2022
Estimated Study Completion Date : June 1, 2023

Resource links provided by the National Library of Medicine

Group/Cohort Intervention/treatment
random splitting based on random sequences generated by engineers to train and optimize a machine learning model
Other: MRI
conventional MRI and diffusion MRI

random splitting based on random sequences generated by engineers to evaluate the performance of the model
Other: MRI
conventional MRI and diffusion MRI

Primary Outcome Measures :
  1. Performance of the fitted model [ Time Frame: through study completion, an average of 2 year ]
    positive predictive value (PPV)

  2. Performance of the fitted model [ Time Frame: through study completion, an average of 2 year ]
    sensitivity (SE)

  3. Performance of the fitted model [ Time Frame: through study completion, an average of 2 year ]
    Dice coefficient score (DSC)

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Sampling Method:   Non-Probability Sample
Study Population
The patients came from the Peking University Third Hospital.

Inclusion Criteria:

  • (Prospective part) compliance to MRI scan
  • (Prospective part) no MRI contraindication
  • (Retrospective part) available conventional MRI data
  • clinical diagnosis of SCI (the course of disease≥1 year)

Exclusion Criteria:

  • prior head or neck surgery or accompanying diseases with neurologic deficits and/or symptoms including multiple sclerosis, motor neuron disease, or spinal cord tumor
  • images with motion artifact

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT04955509

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Contact: Mengze Zhang 18600393607
Contact: Ouyang Hanqiang

Sponsors and Collaborators
Peking University Third Hospital
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Principal Investigator: Huishu Yuan Peking University Third Hospital
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Responsible Party: Peking University Third Hospital Identifier: NCT04955509    
Other Study ID Numbers: M2020400,M2020356
First Posted: July 8, 2021    Key Record Dates
Last Update Posted: July 8, 2021
Last Verified: September 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Spinal Cord Injuries
Wounds and Injuries
Spinal Cord Diseases
Central Nervous System Diseases
Nervous System Diseases
Trauma, Nervous System