Rajashekar Deepthi, Mouchès Pauline, Fiehler Jens, Menon Bijoy K, Goyal Mayank, Demchuk Andrew M, Hill Michael D, Dukelow Sean P, Forkert Nils D
Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.
Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Int J Stroke. 2020 Dec;15(9):965-972. doi: 10.1177/1747493020915251. Epub 2020 Mar 31.
Clinical assessment scores in acute ischemic stroke are only moderately correlated with lesion volume since lesion location is an important confounding factor. Many studies have investigated gray matter indicators of stroke severity, but the understanding of white matter tract involvement is limited in the early phase after stroke. This study aimed to measure and model the involvement of white matter tracts with respect to 24-h post-stroke National Institutes of Health Stroke Scale (NIHSS).
A total of 96 patients (50 females, mean age 66.4 ± 14.0 years, median NIHSS 5, interquartile range: 2-9.5) with follow-up fluid-attenuated inversion recovery magnetic resonance imaging data sets acquired one to seven days after acute ischemic stroke onset due to proximal anterior circulation occlusion were included. Lesions were semi-automatically segmented and non-linearly registered to a common reference atlas. The lesion overlap and tract integrity were determined for each white matter tract in the AALCAT atlas and used to model NIHSS outcomes using a supervised linear-kernel support vector regression method, which was evaluated using leave-one-patient-out cross validation.
The support vector regression model using the tract integrity and tract lesion overlap measurements predicted the 24-h NIHSS score with a high correlation value of r = 0.7. Using the tract overlap and tract integrity feature improved the modeling accuracy of NIHSS significantly by 6% (p < 0.05) compared to using overlap measures only.
White matter tract integrity and lesion load are important predictors for clinical outcome after an acute ischemic stroke as measured by the NIHSS and should be integrated for predictive modeling.
急性缺血性卒中的临床评估评分与病灶体积仅呈中度相关，因为病灶位置是一个重要的混杂因素。许多研究调查了卒中严重程度的灰质指标，但对卒中后早期白质纤维束受累情况的了解有限。本研究旨在测量并建立白质纤维束受累情况与卒中后24小时美国国立卫生研究院卒中量表（NIHSS）之间的模型。
纳入96例患者（50例女性，平均年龄66.4±14.0岁，NIHSS中位数为5，四分位间距：2 - 9.5），这些患者因近端前循环闭塞导致急性缺血性卒中发作，在发病后1至7天获得了随访的液体衰减反转恢复磁共振成像数据集。病灶通过半自动分割，并非线性配准到一个通用参考图谱。在AALCAT图谱中确定每个白质纤维束的病灶重叠和纤维束完整性，并使用监督线性核支持向量回归方法建立NIHSS结果模型，该方法通过留一患者交叉验证进行评估。
使用纤维束完整性和纤维束病灶重叠测量的支持向量回归模型预测24小时NIHSS评分，相关系数r = 0.7，相关性较高。与仅使用重叠测量相比，使用纤维束重叠和纤维束完整性特征显著提高了NIHSS的建模准确性，提高了6%（p < 0.05）。
白质纤维束完整性和病灶负荷是急性缺血性卒中后通过NIHSS测量的临床结局的重要预测指标，应将其整合用于预测建模。
