Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, United Kingdom.
Victor Horsley Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom.
PLoS One. 2023 Jun 1;18(6):e0286485. doi: 10.1371/journal.pone.0286485. eCollection 2023.
Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm.
42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk.
Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3 of blood (OR = 1.069, 95% CI: 1.021-1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value.
Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.
脑动脉瘤性蛛网膜下腔出血（aSAH）后的血管痉挛是一种与不良神经预后相关的严重并发症。我们提出了一种新的、半自动的管道，该管道在开源医学影像分析软件 ITK-SNAP 中实现，用于从初始 CT 头部（CTH）扫描中分割蛛网膜下腔血液量，并使用该方法预测未来的放射学血管痉挛。
2020 年 2 月至 2021 年 12 月期间，我们的三级神经科学中心收治了 42 名患者，回顾性队列研究使用了他们的初始转诊 CTH 扫描。使用 ITK-SNAP 中的半自动随机森林分类器和主动轮廓演化对血液负荷进行分割。从电子医疗记录中提取临床数据，以拟合旨在预测放射学血管痉挛风险的模型。
半自动分割与手动、专家衍生的体积具有极好的一致性（平均骰子系数=0.92）。首次就诊时从 CTH 图像中提取的总归一化血液量与放射学血管痉挛的可能性显著相关，每增加 1cm3 的血液，发生血管痉挛的几率大约增加 7%（OR=1.069，95%CI：1.021-1.120；p<0.005）。更大的血量也与更高的林德加德比率、更长的持续时间和更多的离散发作的血管痉挛显著相关。与改良费希尔量表相比，总血量对放射学血管痉挛的预测具有更高的准确性（AUC=0.86 与 0.70），并且具有独立的预测价值。
半自动方法为 aSAH 的 CT 头部图像中的血液分割提供了一种可行的管道，总血量是放射学血管痉挛的一个强大、可扩展的预测因子，优于改良费希尔量表。更大的蛛网膜下腔血量显著增加了随后发生血管痉挛的几率、其时间过程和严重程度。
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