文献类型：期刊文献

英文题名：Automated quantification of the Alberta Stroke Programme Early CT Score (ASPECTS) and infarct core on diffusion-weighted imaging in acute ischaemic stroke: multi-centre validation and severity stratification

作者：Wei, Lai[1];Xiao, Ting[2];Wang, Hao[2];Shi, Lei[2];Xi, Qian[3];Liu, Ming[4];Xu, Huali[5];Zhang, Kangwei[6,7];Zhou, Xiang[6,7];Luo, Yu[1];Wang, Peijun[6,7]

机构：[1]Tongji Univ, Shanghai Peoples Hosp 4, Sch Med, Dept Radiol, Shanghai 200434, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200235, Peoples R China;[3]Tongji Univ, Shanghai East Hosp, Dept Radiol, Sch Med, Shanghai 200120, Peoples R China;[4]Shanghai Jiao Tong Univ, Dept Radiol, Xinhua Hosp, Sch Med, Shanghai 200092, Peoples R China;[5]Shanghai Univ Tradit Chinese Med, Putuo Hosp, Dept Radiol, Shanghai 200062, Peoples R China;[6]Tongji Univ, Tongji Hosp, Sch Med, Dept Med Imaging, Shanghai 200065, Peoples R China;[7]Tongji Univ, Inst Med Imaging Artificial Intelligence, Sch Med, Shanghai 200065, Peoples R China

年份：2026

卷号：8

期号：2

外文期刊名：BRAIN COMMUNICATIONS

收录：WOS:【ESCI(收录号:WOS:001709079200001)】；

基金：This study was supported by the Major project of the National Natural Science Foundation of China (Grant No. 82227807 and 82572180), the Research Project of Shanghai Municipal Health Commission (Grant No. 2024ZZ1018, 2024ZDXK0066 and 2022JC017) and the National Key R&D Program of the Ministry of Science and Technology of China (Grant No.2022YFC2009904).

语种：英文

外文关键词：acute ischaemic stroke (AIS); diffusion-weighted imaging (DWI); the Alberta Stroke Programme Early CT Score (ASPECTS); corevolume; deep learning (DL)

摘要：The purpose of this study was to develop and validate a deep learning framework for simultaneous automated quantification of diffusion-weighted imaging-the Alberta Stroke Programme early CT score (DWI-ASPECTS) and infarct core volume in middle cerebral artery acute ischaemic stroke (MCA-AIS) and to evaluate its clinical utility for severity stratification in multi-centre settings. A cohort of 738 patients diagnosed with MCA-AIS from four centres was divided into a train set (n = 408), a validation set (n = 116), an internal test set (n = 60) and an external test set (n = 154). A 3D U-Net architecture was trained for simultaneous infarct segmentation and the ASPECTS region analysis. Model performance was compared against expert neuroradiologists using intraclass correlation coefficients and the Spearman correlation coefficient. Furthermore, we investigated the correlation between ASPECTS deduction frequency, ASPECTS score, core volume and the severity of MCA-AIS. The 3D U-Net model showed a high correlation with manual segmentation, achieving a Dice coefficient of 0.801 and a Spearman correlation coefficient of 0.988 for volume measurements in the external test set. In both the internal and external test sets, the automated ASPECTS by the deep learning (DL) model (Automated), manual ASPECTS by raters (Raters) and manual ASPECTS by raters on DWI images registered with the template (Raters_template) exhibited a strong correlation and excellent agreement. The cortical regions (M1-M6) were particularly relevant in patients classified into the moderate-severe group. The threshold values for the mild group and moderate-severe group on receiver operating characteristic curve analysis for DWI-ASPECTS and Core volume, were 6 and 27.86 mL, respectively. The DL model demonstrated comparable performance to neuroradiologists' evaluation, potentially serving as an ancillary tool for physicians in making urgent clinical decisions. The severity of MCA-AIS was significantly associated with the specific ASPETCS regions and core volume, which may aid in identifying moderate-severe MCA-AIS. Wei et al. report that a novel deep learning model fully automates the Alberta Stroke Program Early CT Score (ASPECTS) scoring and infarct core volume measurement from diffuse-weighted imaging (DWI) scans. This tool was validated on multi-centre data and may aid in rapid stroke assessment.