Radiomics features of DSC-PWI in time dimension may provide a new chance to identify ischemic stroke - Guo, Yingwei; Yang, Yingjian; Cao, Fengqiu; Liu, Yang; Li, Wei; Yang, Chaoran; Feng, Mengting; Luo, Yu; Cheng, Lei; Li, Qiang; Zeng, Xueqiang; Miao, Xiaoqiang; et al. (Frontiers in Neurology, 2022)
Ischemic stroke has become a severe disease endangering human life. However, few studies analyzed the imaging features of great clinical significance for the diagnosis, treatment, and prognosis of patients with ischemic stroke. Due to sufficient cerebral blood flow information in DSC-PWI images, this study aims to find the critical features hidden in DSC-PWI images to characterize HA and NA and identify HA from NA in ischemic stroke images. This study retrospectively analyzed 80 DSC-PWI data of 56 patients with ischemic stroke from 2013 to 2016. For exploring features in HA and NA, 13 feature sets (Fmethod) were obtained from different feature selection algorithms. Furthermore, these 13 Fmethod were validated on two aspects, identifying HA and NA and distinguishing the proportion of ischemic lesions in brain tissue, based on the ten learning models. In identifying HA and NA, the composite score (CS) of the 13 Fmethod ranged from 0.624 to 0.925. FLasso in the 13 Fmethod achieved the best performance with mAcc of 0.958, mPre of 0.96, mAuc of 0.982, mF1 of 0.959, and mRecall of 0.96. As to classifying the proportion of the ischemic region, the best CS was 0.786, with Acc of 0.888 and Pre of 0.863. When RT was less than 0.25, the classification ability was relatively stable. Otherwise, the performance will gradually decrease. These results showed that radiomics features extracted from the Lasso algorithms accurately reflect cerebral blood flow changes and exactly classify HA and NA. Besides, In the event of ischemic stroke, the ability of radiomics features to distinguish the proportion of ischemic areas needs to be improved. Further research should be conducted on feature engineering, model optimization, and the universality of the algorithm in the future.