影像组学在颅内动脉瘤中的应用研究进展

Research progress in the application of radiomics in intracranial aneurysms

颅内动脉瘤是常见的脑血管疾病,发病率高,破裂后致死、致残率高,准确评估其破裂风险至关重要。影像组学基于影像图像高通量提取大量定量特征,在颅内动脉瘤特别是风险分层方面展现出巨大应用潜力。影像组学结合机器学习算法尤其是深度学习有望提升颅内动脉瘤稳定性评估的效能,提高临床工作效率。现对影像组学在颅内动脉瘤中的应用研究进展进行综述,为动脉瘤的个体化分层和管理提供参考。

Aneurysms are common cerebrovascular diseases with a high incidence rate,high mortality,and disability rate after rupture.Therefore,it is crucial to assess their risk of rupture accurately.Radiomics,based on high-throughput extraction of quantitative features from imaging data,has shown great potential,particularly in the risk stratification of aneurysms.Combining radiomics with machine learning,especially deep learning algorithms has the potential to enhance the efficacy of stability assessment for intracranial aneurysms and improve clinical workflow efficiency.This article provides an overview of the research progress in applying radiomics in intracranial aneurysms,aiming to provide references for individual stratification and management of aneurysms in clinical practices.