Leon-Rojas Jose E
Grupo de Investigación Cerebro, Emoción y Conducta (CEC), Facultad de Medicina, Universidad de las Américas (UDLA), Quito EC170516, Ecuador.
J Clin Med. 2025 May 2;14(9):3158. doi: 10.3390/jcm14093158.
Intracranial aneurysms (IAs) are present in approximately 3-5% of the global population and carry a significant risk of rupture, leading to subarachnoid haemorrhage (SAH), a condition associated with high morbidity and mortality. Even with developments in neuroimaging, fundamental clinical difficulty remains in precisely predicting which aneurysms will rupture. Although aneurysm size, location, and patient history define traditional risk assessment, these elements by themselves have insufficient predictive ability. Key elements in rupture risk are aneurysm wall biology, haemodynamics, and inflammation; recent developments in magnetic resonance imaging (MRI) including high-resolution vascular wall imaging (VWI), 4D flow MRI, and quantitative susceptibility mapping (QSM) provide fresh insights on these aspects. The present evidence on these sophisticated MRI techniques is synthesised in this review of the literature, which also analyses their clinical relevance and addresses newly developed computational methods like machine learning for better risk stratification. I underline important studies showing the diagnostic and prognostic worth of MRI-based biomarkers, discuss present constraints, and suggest future lines of research. Personalised aneurysm care could benefit from the combination of multiparametric MRI data with artificial intelligence (AI), hence improving patient outcomes.
颅内动脉瘤(IA)在全球约3%-5%的人口中存在,具有显著的破裂风险,可导致蛛网膜下腔出血(SAH),这是一种与高发病率和死亡率相关的疾病。即使神经影像学有所发展,精确预测哪些动脉瘤会破裂的基本临床难题仍然存在。尽管动脉瘤大小、位置和患者病史决定了传统的风险评估,但这些因素本身的预测能力不足。破裂风险的关键因素是动脉瘤壁生物学、血流动力学和炎症;磁共振成像(MRI)的最新进展,包括高分辨率血管壁成像(VWI)、四维血流MRI和定量磁化率映射(QSM),为这些方面提供了新的见解。本文献综述综合了关于这些先进MRI技术的现有证据,分析了它们的临床相关性,并探讨了机器学习等新开发的计算方法,以实现更好的风险分层。我强调了显示基于MRI的生物标志物的诊断和预后价值的重要研究,讨论了当前的局限性,并提出了未来的研究方向。个性化的动脉瘤护理可以受益于多参数MRI数据与人工智能(AI)的结合,从而改善患者预后。