Study on Treatment Decision-Making and Prognostic Follow-Up for Untreated Cerebral Cavernous Malformations

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.