Guo Yingwei, Yang Yingjian, Wang Mingming, Luo Yu, Guo Jia, Cao Fengqiu, Lu Jiaxi, Zeng Xueqiang, Miao Xiaoqiang, Zaman Asim, Kang Yan
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Life (Basel). 2022 Nov 11;12(11):1847. doi: 10.3390/life12111847.
Accurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features (DRFs) in the whole brain, DRFs in local ischemic lesions, and their combination in predicting functional outcomes of ischemic stroke patients. First, the DRFs in the whole brain and the DRFs in local lesions of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) images are calculated. Second, the least absolute shrinkage and selection operator (Lasso) is used to generate four groups of DRFs, including the outstanding DRFs in the whole brain (Lasso (WB)), the outstanding DRFs in local lesions (Lasso (LL)), the combination of them (combined DRFs), and the outstanding DRFs in the combined DRFs (Lasso (combined)). Then, the performance of the four groups of DRFs is evaluated to predict the functional recovery in three months. As a result, Lasso (combined) in the four groups achieves the best AUC score of 0.971, which improves the score by 8.9% compared with Lasso (WB), and by 3.5% compared with Lasso (WB) and combined DRFs. In conclusion, the outstanding combined DRFs generated from the outstanding DRFs in the whole brain and local lesions can predict functional outcomes in ischemic stroke patients better than the single DRFs in the whole brain or local lesions.
准确可靠的预后预测有助于评估缺血性中风患者的功能恢复情况，并辅助制定治疗方案。鉴于恢复因素可能隐藏在全脑特征中，本研究旨在验证动态影像组学特征（DRF）在全脑、局部缺血性病变中的DRF及其组合在预测缺血性中风患者功能预后中的作用。首先，计算动态磁敏感对比增强灌注加权成像（DSC-PWI）图像全脑的DRF和局部病变的DRF。其次，使用最小绝对收缩和选择算子（Lasso）生成四组DRF，包括全脑中突出的DRF（Lasso（WB））、局部病变中突出的DRF（Lasso（LL））、它们的组合（组合DRF）以及组合DRF中突出的DRF（Lasso（组合））。然后，评估这四组DRF预测三个月功能恢复的性能。结果，四组中的Lasso（组合）获得了最佳的AUC分数0.971，与Lasso（WB）相比提高了8.9%，与Lasso（WB）和组合DRF相比提高了3.5%。总之，由全脑和局部病变中突出的DRF生成的突出组合DRF在预测缺血性中风患者功能预后方面比全脑或局部病变中的单一DRF更好。
