Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
J Neurointerv Surg. 2022 Aug;14(8):794-798. doi: 10.1136/neurintsurg-2021-017842. Epub 2021 Aug 19.
Machine learning algorithms hold the potential to contribute to fast and accurate detection of large vessel occlusion (LVO) in patients with suspected acute ischemic stroke. We assessed the diagnostic performance of an automated LVO detection algorithm on CT angiography (CTA).
Data from the MR CLEAN Registry and PRESTO were used including patients with and without LVO. CTA data were analyzed by the algorithm for detection and localization of LVO (intracranial internal carotid artery (ICA)/ICA terminus (ICA-T), M1, or M2). Assessments done by expert neuroradiologists were used as reference. Diagnostic performance was assessed for detection of LVO and per occlusion location by means of sensitivity, specificity, and area under the curve (AUC).
We analyzed CTAs of 1110 patients from the MR CLEAN Registry (median age (IQR) 71 years (60-80); 584 men; 1110 with LVO) and of 646 patients from PRESTO (median age (IQR) 73 years (62-82); 358 men; 141 with and 505 without LVO). For detection of LVO, the algorithm yielded a sensitivity of 89% in the MR CLEAN Registry and a sensitivity of 72%, specificity of 78%, and AUC of 0.75 in PRESTO. Sensitivity per occlusion location was 88% for ICA/ICA-T, 94% for M1, and 72% for M2 occlusion in the MR CLEAN Registry, and 80% for ICA/ICA-T, 95% for M1, and 49% for M2 occlusion in PRESTO.
The algorithm provided a high detection rate for proximal LVO, but performance varied significantly by occlusion location. Detection of M2 occlusion needs further improvement.
机器学习算法有可能有助于快速准确地检测疑似急性缺血性脑卒中患者的大血管闭塞（LVO）。我们评估了一种自动 LVO 检测算法在 CT 血管造影（CTA）上的诊断性能。
该研究使用了 MR CLEAN 登记处和 PRESTO 的数据，包括有和无 LVO 的患者。算法分析 CTA 以检测和定位 LVO（颅内颈内动脉（ICA）/ICA 终点（ICA-T）、M1 或 M2）。专家神经放射学家的评估结果被用作参考。通过敏感度、特异度和曲线下面积（AUC）评估 LVO 检测和每个闭塞部位的诊断性能。
我们分析了来自 MR CLEAN 登记处的 1110 例患者的 CTA（中位数年龄（IQR）71 岁（60-80）；584 名男性；1110 例 LVO）和来自 PRESTO 的 646 例患者的 CTA（中位数年龄（IQR）73 岁（62-82）；358 名男性；141 例有和 505 例无 LVO）。对于 LVO 的检测，该算法在 MR CLEAN 登记处的敏感度为 89%，在 PRESTO 的敏感度为 72%，特异度为 78%，AUC 为 0.75。每个闭塞部位的敏感度为 ICA/ICA-T 为 88%，M1 为 94%，M2 为 72%，ICA/ICA-T 为 80%，M1 为 95%，M2 为 49%。
该算法对近端 LVO 的检测率较高，但闭塞部位的表现差异很大。M2 闭塞的检测需要进一步改进。
