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- Essai clinique NCT04955509
A Scar Recognition Software for Chronic Spinal Cord Injury (SCI)
In Vivo Optimization and Clinical Application of a Scar Recognition Software for Chronic Spinal Cord Injury (SCI)
Aperçu de l'étude
Statut
Les conditions
Intervention / Traitement
Description détaillée
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.
Type d'étude
Inscription (Anticipé)
Contacts et emplacements
Coordonnées de l'étude
- Nom: Mengze Zhang
- Numéro de téléphone: 18600393607
- E-mail: zmzforever@pku.edu.cn
Sauvegarde des contacts de l'étude
- Nom: Ouyang Hanqiang
- E-mail: ouyanghanqiang@bjmu.edu.cn
Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
- Enfant
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Sexes éligibles pour l'étude
Méthode d'échantillonnage
Population étudiée
La description
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
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
- Modèles d'observation: Cohorte
- Perspectives temporelles: Autre
Cohortes et interventions
Groupe / Cohorte |
Intervention / Traitement |
---|---|
Training
random splitting based on random sequences generated by engineers to train and optimize a machine learning model
|
conventional MRI and diffusion MRI
|
Testing
random splitting based on random sequences generated by engineers to evaluate the performance of the model
|
conventional MRI and diffusion MRI
|
Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
---|---|---|
Performance of the fitted model
Délai: through study completion, an average of 2 year
|
positive predictive value (PPV)
|
through study completion, an average of 2 year
|
Performance of the fitted model
Délai: through study completion, an average of 2 year
|
sensitivity (SE)
|
through study completion, an average of 2 year
|
Performance of the fitted model
Délai: through study completion, an average of 2 year
|
Dice coefficient score (DSC)
|
through study completion, an average of 2 year
|
Collaborateurs et enquêteurs
Parrainer
Les enquêteurs
- Chercheur principal: Huishu Yuan, Peking University Third Hospital
Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Anticipé)
Achèvement primaire (Anticipé)
Achèvement de l'étude (Anticipé)
Dates d'inscription aux études
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Plus d'information
Termes liés à cette étude
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
- M2020400,M2020356
Plan pour les données individuelles des participants (IPD)
Prévoyez-vous de partager les données individuelles des participants (DPI) ?
Informations sur les médicaments et les dispositifs, documents d'étude
Étudie un produit pharmaceutique réglementé par la FDA américaine
Étudie un produit d'appareil réglementé par la FDA américaine
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