Clinical trial NCT06214767: Study on Treatment Decision-Making and Prognostic Follow-Up for Untreated Cerebral Cavernous Malformations
Official title: Study on Treatment Decision-Making and Prognostic Follow-Up for Untreated Cerebral Cavernous Malformations
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Hemangioma, Cavernous, Central Nervous System
Lead sponsor: Beijing Tiantan Hospital
Sponsor class: OTHER
Collaborators: name: RenJi Hospital
name: Beijing Chao Yang Hospital
name: Beijing Friendship Hospital
name: Guangzhou Red Cross Hospital
name: Affiliated Hospital of Guangdong Medical University
name: Shanxi Provincipal People's Hospital
name: Qilu Hospital of Shandong University
name: Central South University
name: First Affiliated Hospital of Harbin Medical University
First posted: 2024-01-22
Enrollment: 1200
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Untreated cerebral cavernous malformation patients

Brief summary: The goal of this observational study is to evaluate and predict the risk associated with cerebral cavernous malformations (CCMs) using advanced artificial intelligence and radiomics analysis technology. The study focuses on individuals who have been diagnosed with cerebral cavernous malformations (CCMs).

Main Questions to Answer:

How can AI-based radiomics features predict the risk of complications (such as bleeding or epilepsy) in individuals with CCMs? What are the most reliable imaging and clinical markers for assessing the prognosis of CCMs? Participants will be required to undergo regular medical imaging to gather traditional and radiomics imaging features.

Participants will provide clinical data, including past medical history and results of any laboratory tests.

Participants will be part of a three-year follow-up observation to monitor the progression or stability of CCMs.

Contribution of biological samples for advanced testing might also be requested.

This study aims to create an AI-based decision-making tool that will guide clinicians in the management of CCM, with the potential to significantly improve patient outcomes through personalized medical approaches.


Eligibility criteria: Inclusion Criteria:

1. Diagnosis of CCM based on brain MRI (T1, T2, SWI, and T2-Fluid-Attenuated Inversion Recovery).
2. Patients who have not received invasive treatment (surgery, radiotherapy, or multimodal therapy) in the past.
3. Patients undergoing surgery, or their legal guardians, agree to collect lesion tissue samples for related studies and sign a consent form for the collection of biological samples.
4. Patients under conservative observation, or their legal guardians, agree to collect imaging data for related research and sign a consent form for the use of imaging data.
5. Willingness to participate in long-term follow-up.

Exclusion Criteria:

1. Patients with acute intracranial symptomatic hemorrhage requiring emergency surgery.
2. Patients with other intracranial diseases, such as aneurysms, tumors, or other vascular malformations, excluding developmental venous anomalies (DVA).
3. Patients with severe underlying diseases affecting their functional status and short-term life expectancy.
4. Patients with severe psychiatric or psychological disorders.
5. Incomplete clinical or imaging data.

Primary outcomes:
measure: Functional Outcome:mRS
timeFrame: 3 years
Secondary outcomes:
measure: Radiological Outcome:Bleeding
timeFrame: 3 years
measure: Radiological Outcome:Increase in lesion volume
timeFrame: 3 years
measure: Syndrome Outcome:Drug-resistant epilepsy
timeFrame: 3 years
measure: Syndrome Outcome:Focal neurological deficits
timeFrame: 3 years
measure: All-cause mortality
timeFrame: 3 years
Other outcomes:
Locations:
city: Beijing
country: China
facility: Capital Medical University Affiliated Beijing Tiantan Hospital
state: Beijing
Study officials:
References: