UNASSIGNED: This study employs a meticulous methodology, starting with generating perfusion parameter maps through Dynamic Susceptibility Contrast-Perfusion Weighted Imaging (DSC-PWI) and determining ischemic regions based on these maps and a set threshold. Radiomics features are then extracted from the ischemic regions, and the t-test and least absolute shrinkage and selection operator (Lasso) algorithms are used to select the relevant features. Finally, the selected radiomics features and machine learning techniques are used to assess the degree of neurological impairment in AIS patients.
UNASSIGNED: The results show that the proposed method outperforms the original perfusion parameters, radiomics features of the infarct and hypoxic regions, and their combinations, achieving an accuracy of 0.926, sensitivity of 0.923, specificity of 0.929, PPV of 0.923, NPV of 0.929, and AUC of 0.923, respectively.
UNASSIGNED: The proposed method effectively assesses the degree of neurological impairment in AIS patients, providing an objective auxiliary assessment tool for clinical diagnosis.
■这项研究采用了细致的方法,首先通过动态磁化率对比灌注加权成像(DSC-PWI)生成灌注参数图,并根据这些图和设定的阈值确定缺血区域。然后从缺血区域提取影像组学特征,并采用t检验和最小绝对收缩和选择算子(Lasso)算法选择相关特征。最后,选择的影像组学特征和机器学习技术用于评估AIS患者的神经功能缺损程度.
■结果表明,所提出的方法优于原始灌注参数,梗死和缺氧区域的影像组学特征,以及它们的组合,准确度为0.926,灵敏度为0.923,特异性为0.929,PPV为0.923,NPV为0.929,AUC为0.923。
■所提出的方法有效地评估了AIS患者的神经功能缺损程度,为临床诊断提供客观的辅助评估工具。