PDF(Pubmed)
Abstract：
UNASSIGNED: Accurate neurological impairment assessment is crucial for the clinical treatment and prognosis of patients with acute ischemic stroke (AIS). However, the original perfusion parameters lack the deep information for characterizing neurological impairment, leading to difficulty in accurate assessment. Given the advantages of radiomics technology in feature representation, this technology should provide more information for characterizing neurological impairment. Therefore, with its rigorous methodology, this study offers practical implications for clinical diagnosis by exploring the role of ischemic perfusion radiomics features in assessing the degree of neurological impairment.
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.
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.
摘要：
■准确的神经功能缺损评估对急性缺血性卒中（AIS）患者的临床治疗和预后至关重要。然而,原始灌注参数缺乏表征神经功能缺损的深层信息，导致难以准确评估。鉴于影像组学技术在特征表示方面的优势，这项技术应该为描述神经功能缺损提供更多信息.因此,凭借其严谨的方法论，本研究通过探讨缺血灌注影像组学特征在评估神经功能缺损程度中的作用,为临床诊断提供了实际启示.
■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。
■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。
