Owens Monica-Rae, Tenhoeve Samuel A, Rawson Clayton, Azab Mohammed, Karsy Michael
Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA.
College of Osteopathic Medicine, NOORDA College, Provo, Utah, USA.
J Neuroimaging. 2025 Mar-Apr;35(2):e70037. doi: 10.1111/jon.70037.
Intracranial aneurysms, with an annual incidence of 2%-3%, reflect a rare disease associated with significant mortality and morbidity risks when ruptured. Early detection, risk stratification of high-risk subgroups, and prediction of patient outcomes are important to treatment. Radiomics is an emerging field using the quantification of medical imaging to identify parameters beyond traditional radiology interpretation that may offer diagnostic or prognostic significance. The general radiomic workflow involves image normalization and segmentation, feature extraction, feature selection or dimensional reduction, training of a predictive model, and validation of the said model. Artificial intelligence (AI) techniques have shown increasing interest in applications toward vascular pathologies, with some commercially successful software including AiDoc, RapidAI, and Viz.AI, as well as the more recent Viz Aneurysm. We performed a systematic review of 684 articles and identified 84 articles exploring the applications of radiomics and AI in aneurysm treatment. Most studies were published between 2018 and 2024, with over half of articles in 2022 and 2023. Studies included categories such as aneurysm diagnosis (25.0%), rupture risk prediction (50.0%), growth rate prediction (4.8%), hemodynamic assessment (2.4%), clinical outcome prediction (11.9%), and occlusion or stenosis assessment (6.0%). Studies utilized molecular data (2.4%), radiologic data alone (51.2%), clinical data alone (28.6%), and combined radiologic and clinical data (17.9%). These results demonstrate the current status of this emerging and exciting field. An increased pace of innovation in this space is likely with the expansion of clinical applications of radiomics and AI in multiple vascular pathologies.
颅内动脉瘤的年发病率为2%-3%,是一种罕见疾病,破裂时会带来显著的死亡和发病风险。早期检测、高危亚组的风险分层以及患者预后的预测对治疗至关重要。放射组学是一个新兴领域,利用医学成像的量化来识别超出传统放射学解释的参数,这些参数可能具有诊断或预后意义。一般的放射组学工作流程包括图像归一化和分割、特征提取、特征选择或降维、预测模型的训练以及该模型的验证。人工智能(AI)技术在血管疾病应用方面的关注度日益增加,一些商业上成功的软件包括AiDoc、RapidAI和Viz.AI,以及最近的Viz Aneurysm。我们对684篇文章进行了系统综述,确定了84篇探讨放射组学和AI在动脉瘤治疗中应用的文章。大多数研究发表于2018年至2024年之间,2022年和2023年发表的文章超过一半。研究类别包括动脉瘤诊断(25.0%)、破裂风险预测(50.0%)、生长率预测(4.8%)、血流动力学评估(2.4%)、临床结局预测(11.9%)以及闭塞或狭窄评估(6.0%)。研究使用了分子数据(2.4%)、仅放射学数据(51.2%)、仅临床数据(28.6%)以及放射学和临床数据相结合(17.9%)。这些结果展示了这个新兴且令人兴奋的领域的现状。随着放射组学和AI在多种血管疾病中的临床应用不断扩展,这个领域的创新步伐可能会加快。