{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:25:16Z","timestamp":1790270716962,"version":"4.1.0"},"reference-count":175,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T00:00:00Z","timestamp":1725494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:p>Cerebral aneurysms are abnormal dilations of blood vessels in the brain that have the potential to rupture, leading to subarachnoid hemorrhage and other serious complications. Early detection and prediction of aneurysm rupture are crucial for effective management and prevention of rupture-related morbidities and mortalities. This review aims to summarize the current knowledge on risk factors and predictive indicators of rupture in cerebral aneurysms. Morphological characteristics such as aneurysm size, shape, and location, as well as hemodynamic factors including blood flow patterns and wall shear stress, have been identified as important factors influencing aneurysm stability and rupture risk. In addition to these traditional factors, emerging evidence suggests that biological and genetic factors, such as inflammation, extracellular matrix remodeling, and genetic polymorphisms, may also play significant roles in aneurysm rupture. Furthermore, advancements in computational fluid dynamics and machine learning algorithms have enabled the development of novel predictive models for rupture risk assessment. However, challenges remain in accurately predicting aneurysm rupture, and further research is needed to validate these predictors and integrate them into clinical practice. By elucidating and identifying the various risk factors and predictive indicators associated with aneurysm rupture, we can enhance personalized risk assessment and optimize treatment strategies for patients with cerebral aneurysms.<\/jats:p>","DOI":"10.3389\/fphys.2024.1454016","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T08:04:00Z","timestamp":1725523440000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Risk factors and predictive indicators of rupture in cerebral aneurysms"],"prefix":"10.3389","volume":"15","author":[{"given":"Xiguang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,9,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1136\/neurintsurg-2019-014936","article-title":"MRA versus DSA for the follow-up imaging of intracranial aneurysms treated using endovascular techniques: a meta-analysis","volume":"11","author":"Ahmed","year":"2019","journal-title":"Journal of neurointerventional surgery"},{"key":"B2","doi-asserted-by":"publisher","first-page":"239","DOI":"10.3390\/jpm11040239","article-title":"Multi-view convolutional neural networks in rupture risk assessment of small, unruptured intracranial aneurysms","volume":"11","author":"Ahn","year":"2021","journal-title":"J. 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