Cerebral arteriovenous malformations classification systems in comparison with Spetzler-Martin: A comparative review

医学 颅内动静脉畸形 脑血管造影 放射科 血管造影
作者
Mohamad M. Assker,Ahmed M. Youssef,Suha A. Mohammed,Noor M. Akar,Mohammed Mahdi Hashim,Narjis Kadhim,Noor Al-Saadi,Mostafa H. Algabri,Mohd Hassan Shukur,Mustafa Ismail,Ahmed Muthana,Samer S. Hoz
出处
期刊:Surgical Neurology International [Medknow]
卷期号:16: 173-173
标识
DOI:10.25259/sni_140_2025
摘要

Arteriovenous malformations (AVMs) are complex vascular anomalies requiring classification systems to guide treatment and predict outcomes. This review evaluates multiple AVM classification systems, including the widely used Spetzler-Martin Grading System (SMGS), emphasizing their importance in neurosurgery for improving clinical decision-making and communication. We conducted a literature search using Google Scholar, PubMed, and Scopus to gather information on AVM classification systems. Our inclusion criteria involved articles that referenced a well-established classification system with at least two components. Radiological, surgical, and clinical outcomes systematically categorized nine distinct AVM grading systems. The review focuses on comparing the advantages and limitations of different AVM classification systems to the SMGS. A review of 33 articles highlights the evolution of AVM classification systems, with the SMGS as a foundation for surgical outcomes. Systems such as the Pollock-Flickinger and Pittsburgh AVM scale improve radiosurgery predictions, while Lawton-Young adds factors for surgical precision. Specialized scores refine grading for specific cases, and simplified systems like Spetzler-Ponce enhance usability in unique contexts. AVM classification systems, including Spetzler-Martin, Pollock-Flickinger, and Lawton-Young, provide critical insights into treatment and prognosis. While Spetzler-Martin effectively predicts surgical outcomes, systems like Lawton-Young enhance accuracy by incorporating additional factors but may face challenges in clinical application due to complexity. Continued refinement and validation are essential to improve predictive accuracy, optimize patient care, and connect research with clinical practice.
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