影像学评估颅内动脉瘤破裂风险的研究进展

Research Progress on Radiology to Assess the Rupture Risk of Intracranial Aneurysms

摘要: 颅内动脉瘤是蛛网膜下腔出血的首要病因，其破裂致死致残率高，是临床核心挑战。随着影像技术进步不断上升，未破裂动脉瘤检出率增加。因此精准识别高危未破裂动脉瘤是当前临床决策的核心挑战。颅内动脉瘤破裂风险的影像学评估正从传统形态学向多维度、定量化、智能化方向深入融合。传统CTA、MRA、DSA等结构成像是形态学评估的基础，而高分辨率血管壁成像、计算流体动力学、4D血流磁共振等新兴技术，实现了对动脉瘤壁病理状态和血流动力学的直接可视化与量化评估。在此基础上，人工智能与影像组学通过整合多模态信息，展现出构建个体化预测模型的巨大潜力。尽管当前体系面临技术标准不一、研究偏倚及临床转化瓶颈等挑战，但未来通过建立标准化多中心数据库、推动多模态信息临床整合并拓展新兴评估维度，有望最终实现精准识别高危动脉瘤，优化临床决策。

Abstract: Intracranial aneurysms (IAs) are primary cause of subarachnoid hemorrhage (SAH), with high rates of mortality and disability upon rupture, posing a significant clinical burden. Advances in imaging technology have led to a significant increase in the detection of unruptured intracranial aneurysms (UIAs). Consequently, accurately distinguishing high-risk UIAs that require intervention from stable ones has become a central challenge in clinical management. The imaging assessment of IAs rupture risk is evolving from traditional morphological evaluation towards a multi-dimensional, quantitative, and intelligent paradigm. While conventional structural imaging (CTA, MRA and DSA) remains the cornerstone, emerging techniques like high-resolution vessel wall imaging (HR-VWI), computational fluid dynamics (CFD), and 4D flow MRI enable direct visualization and quantification of aneurysm wall pathology and hemodynamic stresses. Building on this, artificial intelligence (AI) and radiomics demonstrate great potential for constructing individualized risk prediction models by integrating these multimodal data. Despite current challenges-including lack of standardization, study biases, and translational hurdles-future efforts focused on establishing standardized multi-center databases, integrating multimodal data into clinical workflows, and exploring novel assessment dimensions are expected to enable precise identification of high-risk aneurysms and optimize clinical decision-making.