Purpose

Computed tomography angiography (CTA) is routinely used to detect large-vessel occlusion (LVO) in patients with suspected acute ischemic stroke; however, visual analysis is time consuming and prone to error. To evaluate solutions to support imaging triage, we tested performance of automated analysis of CTA source images (CTASI) at identifying patients with LVO.

Methods

Stroke patients with LVO were selected from a prospectively acquired cohort. A control group was selected from consecutive patients with clinically suspected stroke without signs of ischemia on CT perfusion (CTP) or infarct on follow-up. Software-based automated segmentation and Hounsfield unit (HU) measurements were performed on CTASI for all regions of the Alberta Stroke Program Early CT score (ASPECTS). We derived different parameters from raw measurements and analyzed their performance to identify patients with LVO using receiver operating characteristic curve analysis.

Results

The retrospective analysis included 145 patients, 79 patients with LVO stroke and 66 patients without stroke. The parameters hemispheric asymmetry ratio (AR), ratio between highest and lowest regional AR and M2-territory AR produced area under the curve (AUC) values from 0.95–0.97 (all p < 0.001) for detecting presence of LVO in the total population. Resulting sensitivity (sens)/specificity (spec) defined by the Youden index were 0.87/0.97–0.99. Maximum sens/spec defined by the specificity threshold ≥0.70 were 0.91–0.96/0.77–0.83. Performance in a small number of patients with isolated M2 occlusion was lower (AUC: 0.72–0.85).

Conclusion

Automated attenuation measurements on CTASI identify proximal LVO stroke patients with high sensitivity and specificity. This technique can aid in accurate and timely patient selection for thrombectomy, especially in primary stroke centers without CTP capacity.

中文翻译：

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CT 血管造影上识别大血管闭塞中风的自动衰减测量性能。

 目的

计算机断层扫描血管造影 (CTA) 通常用于检测疑似急性缺血性中风患者的大血管闭塞 (LVO)；然而，视觉分析既耗时又容易出错。为了评估支持影像分类的解决方案，我们测试了 CTA 源图像自动分析 (CTASI) 在识别 LVO 患者方面的性能。

 方法

患有 LVO 的中风患者是从前瞻性队列中选出的。对照组选自连续患有临床疑似中风且 CT 灌注 (CTP) 上没有缺血迹象或随访时没有梗死迹象的患者。在 CTASI 上对艾伯塔省中风计划早期 CT 评分 (ASPECTS) 的所有区域进行基于软件的自动分割和亨斯菲尔德单位 (HU) 测量。我们从原始测量中得出不同的参数，并使用受试者工作特征曲线分析分析其性能，以识别 LVO 患者。

 结果

回顾性分析包括 145 名患者，其中 79 名 LVO 卒中患者和 66 名无卒中患者。参数半球不对称比 (AR)、最高和最低区域 AR 与 M2 区域 AR 之间的比率产生的曲线下面积 (AUC) 值为 0.95–0.97（所有p < 0.001），用于检测总人群中 LVO 的存在。由 Youden 指数定义的最终灵敏度 (sens)/特异性 (spec) 为 0.87/0.97–0.99。由特异性阈值≥0.70定义的最大sens/spec为0.91–0.96/0.77–0.83。少数患有孤立性 M2 闭塞的患者的表现较低（AUC：0.72-0.85）。

 结论

CTASI 的自动衰减测量可识别近端 LVO 中风患者，具有高敏感性和特异性。该技术可以帮助准确、及时地选择患者进行血栓切除术，特别是在没有 CTP 能力的初级卒中中心。