Alshuqayfi KM, AlDallal U, Albulaihed S, Atallah O, Sharma M, Al-Ghuraibawi MA, Algabri MH, Ismail M, Hoz SS. Cerebral arteriovenous malformation calcifications: A systematic review, case series, and a proposed classification system.
Surg Neurol Int 2025;
16:104. [PMID:
40206768 PMCID:
PMC11980719 DOI:
10.25259/sni_102_2025]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2025] [Accepted: 02/06/2025] [Indexed: 04/11/2025] Open
Abstract
Background
Brain arteriovenous malformations (AVMs) are intracranial vascular lesions characterized by a nidus of vessels fed by an artery and drained by a vein, lacking intervening capillaries. Angiography remains the gold standard for a definitive diagnosis. There is a paucity in the literature regarding clinical presentation and management of patients with calcified cerebral AVM (cCAVM). This study aims to highlight the clinical presentation and management of patients with cCAVM and also to propose a classification of calcification patterns in cCAVMs based on brain computed tomography (CT) findings.
Methods
A systematic review using PubMed, Scopus, and Web of Science was done according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify cases that illustrate cCAVM. A case series was also presented to supplement the current literature.
Results
Twenty patients with cCAVM were included, with the male gender representing more than 50% of the patient population. Their age ranged from 11 to 69 years, with seizures being the most common presenting symptom. The frontal lobe was the most common location of AVMs, followed by the parietal lobe. Most (80%) of the calcifications were nidal, with the remaining being extranidal (20%).
Conclusion
The CT scans of patients displayed significant variability due to the unique characteristics of each cCAVM. To address this diversity, a novel classification system was developed to provide a comprehensive framework for understanding cCAVMs based on their location, size, and extent.
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