Evaluation of Clinical Outcomes of Unruptured bAVMs Treated With Medical Management Alone Based on Multimodal CT

Study Overview
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Brief Summary
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The purpose of this study is to investigate the role of multimodal CT in evaluating and predicting different clinical outcomes in patients with unruptured brain arteriovenous malformations with medical management alone. This study will be undertaken in the following aspects: Morphological, structural and imaging histological signal analysis based on multimodal CT to explore the relationship between imaging features and different clinical outcomes of bAVMs. Hemodynamic and flow pattern analysis of local and peripheral bAVMs based on multimodal CT to explore the association between blood flow features and different clinical outcomes. Based on multimodal CT and deep learning algorithms to create an automated segmentation model for bAVMs and a prediction model for distinct clinical outcomes. The obtained patient information includes: Baseline information, CT, MRI, DSA (Optional), and follow-up information. Follow-up will be undertaken at 6 months, 12 months, 24 months and 36 months after enrollment with the following primary observation. Main observation endpoints: Hemorrhage events associated with bAVMs New epilepsy symptoms or exacerbations New neurological dysfunction(including motor dysfunction, cognitive dysfunction, etc).

Detailed Description
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This study is a Long-term, multi-center, prospective registry study, and was supported by China National Key Research and Development Program Project No. 2021YFC2500502. The purpose of this study is to investigate the role of multimodal Computer Tomography(CT) in evaluating and predicting different clinical outcomes in patients with unruptured brain arteriovenous malformations with medical management alone. This study will be undertaken in the following aspects: Morphological, structural and imaging histological signal analysis based on multimodal CT to study the relationship between imaging features and different clinical outcomes of bAVMs. Hemodynamic and flow pattern analysis of local and peripheral bAVMs based on multimodal CT to explore the association between blood flow features and different clinical outcomes. Based on multimodal CT and deep learning algorithms to create an automated segmentation model for bAVMs and a prediction model for distinct clinical outcomes. To compare the efficacy of multimodal CT and MRI in evaluating the clinical outcomes of unruptured cerebral arteriovenous malformations. The obtained patient information includes: Baseline information CT images: including non-contrast CT(NCCT), CT Angiography, CT perfusion Magnetic Resonance Imaging(MRI): including T1-weighted image, T2-weighted image, T2-FLAIR, Time of Flight MR angiography(TOF-MRA), diffusion-weighted imaging(DWI), susceptibility weighted imaging(SWI) Digital subtraction angiography(DSA, optional) Follow-up information: follow-up will be undertaken at 6 months, 12 months, 24 months and 36 months after enrollment with the following primary observation. Main observation endpoints: Hemorrhage events associated with bAVMs, ensuring by CT scan; New epilepsy symptoms or exacerbations, ensuring by consultation or EEG ; New neurological dysfunction(including motor dysfunction, cognitive dysfunction, etc), ensuring by consultation, physical examination or any radiology examination. Inclusion criteria Patients with confirmed diagnosis of bAVMs in the cerebrum, basal ganglia, thalamus, corpus callosum, cerebellum and brainstem; Patients with no history of previous bAVM related hemorrhage confirmed by CT examination, or have radiographic examination completed prior to the hemorrhage. Patients with radiographic examinations before any treatment; Exclusion criteria. Patients with simple arteriovenous fistula; Patients with combined dural arteriovenous fistula; Arteriovenous malformations occurring in the spinal cord; Pregnant patients were not included in this study.

Official Title
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Evaluation of Clinical Outcomes of Unruptured Brain Arteriovenous Malformations Treated With Medical Management Alone Based on Multimodal Computer Tomography: A Long-term Multicenter Prospective Cohort Study

Conditions
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Brain Arteriovenous Malformations

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients with confirmed diagnosis of brain arteriovenous malformations in the brain, basal ganglia, thalamus, corpus callosum, cerebellum and other locations. Patients with no history of previous arteriovenous malformation-related hemorrhage and no arteriovenous malformation-related hemorrhage confirmed by CT examination. Patients with Radiographic examinations completed without any treatment Exclusion Criteria: Patients with simple arteriovenous fistula. Patients with combined dural arteriovenous fistulas. Arteriovenous malformations occurring in the spinal cord. Pregnant patients were not included in this study.

Ages Eligible for Study
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Minimum Age: 3 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of participants with rupture events | Arteriovenous malformation rupture during follow-up before treatment | 3 years after enrollment | 
| Number of participants with epilepsy Progression | Progression of seizure symptoms during follow-up, including but not limited to more frequent seizures, worsening of symptoms, etc. | 3 years after enrollment | 
| Number of participants with new onset neurological dysfunction | New neurological deficits during the follow-up period, including motor deficits, cognitive deficits, etc. | 3 years after enrollment | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of participants receiving treatment | Patients receiving any form of intervention during the follow-up period will stop the follow-up | 3 years after enrollment | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Unruptured arteriovenous malformations, Multimodal CT, Machine learning