Zhao Bingyang, Yu Weidong, Ju Dongsheng, Jiang Xinzhao, Zhao Zhongyu, Zhu Shenwen, Li Jie, He Siyu, Mang Jing
Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China.
Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin, China.
J Neurointerv Surg. 2025 Jun 27. doi: 10.1136/jnis-2025-023559.
Large vessel occlusion (LVO) is a major cause of acute ischemic stroke (AIS). Identifying its underlying etiology, particularly intracranial atherosclerotic stenosis (ICAS), is crucial for optimizing endovascular thrombectomy (EVT). Intra-procedural occlusive signs can offer clues, but their interpretation is often subjective. This study proposes a radiomics-based approach to objectively characterize angiographic signs and predict occlusion etiology in real time.
We retrospectively included 465 EVT-treated patients with acute M1-segment MCA occlusion from two centers (January 2018-December 2023). Radiomics features were extracted from angiographic parametric imaging (API) and used to develop a radiomics score via least absolute shrinkage and selection operator (LASSO) logistic regression. The score's predictive value for ICAS-LVO was assessed using logistic regression, and the optimal cut-off was determined via the Youden index. Subgroup analyses were performed to compare procedural outcomes between radiomics-inferred ICAS and embolic occlusions.
The radiomics score was significantly higher in ICAS-related occlusions than in embolic occlusions (median 0.39 vs 0.89, P<0.001) and was the strongest independent predictor of ICAS etiology (adjusted odds ratio (OR) 25.40, 95% CI 12.13 to 56.94, P<0.001). Key discriminative features included texture-based parameters from perfusion maps. Based on the Youden index, a cut-off of 0.569 was defined to stratify cases into radiomics-inferred ICAS and embolic groups. Among patients treated with contact aspiration, those with radiomics-inferred ICAS occlusion had lower first-pass reperfusion rates compared with those with radiomics-inferred embolic occlusion (35.6% vs 60.7%, P-value Bonferroni correction =0.004).
Radiomics features extracted from API offer an objective method for intra-procedural inference of occlusion etiology, particularly ICAS-LVO. This approach may support technical efficacy and procedural planning during EVT, especially in populations or regions with higher ICAS prevalence.
大血管闭塞（LVO）是急性缺血性卒中（AIS）的主要病因。识别其潜在病因，尤其是颅内动脉粥样硬化狭窄（ICAS），对于优化血管内血栓切除术（EVT）至关重要。术中闭塞征象可提供线索，但其解读往往具有主观性。本研究提出一种基于放射组学的方法，以客观地表征血管造影征象并实时预测闭塞病因。
我们回顾性纳入了来自两个中心（2018年1月至2023年12月）接受EVT治疗的465例急性大脑中动脉M1段闭塞患者。从血管造影参数成像（API）中提取放射组学特征，并通过最小绝对收缩和选择算子（LASSO）逻辑回归建立放射组学评分。使用逻辑回归评估该评分对ICAS-LVO的预测价值，并通过约登指数确定最佳截断值。进行亚组分析以比较放射组学推断的ICAS和栓塞性闭塞之间的手术结果。
ICAS相关闭塞的放射组学评分显著高于栓塞性闭塞（中位数0.39对0.89，P<0.001），并且是ICAS病因的最强独立预测因子（调整后的优势比（OR）为25.40，95%置信区间为12.13至56.94，P<0.001）。关键的鉴别特征包括来自灌注图的基于纹理的参数。根据约登指数，定义截断值为0.569，以将病例分为放射组学推断的ICAS组和栓塞组。在接受接触抽吸治疗的患者中，放射组学推断为ICAS闭塞的患者与放射组学推断为栓塞性闭塞的患者相比，首次通过再灌注率较低（35.6%对60.7%，经Bonferroni校正的P值=0.004）。
从API中提取的放射组学特征为术中推断闭塞病因，尤其是ICAS-LVO提供了一种客观方法。这种方法可能有助于支持EVT期间的技术疗效和手术规划，特别是在ICAS患病率较高的人群或地区。
