Lu Jiaxi, Yassin Mazen M, Guo Yingwei, Yang Yingjian, Cao Fengqiu, Fang Jiajing, Zaman Asim, Hassan Haseeb, Zeng Xueqiang, Miao Xiaoqiang, Yang Huihui, Cao Anbo, Huang Guangtao, Han Taiyu, Luo Yu, Kang Yan
School of Applied Technology, Shenzhen University, Shenzhen, China.
College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China.
Front Neurol. 2024 Jul 16;15:1441055. doi: 10.3389/fneur.2024.1441055. eCollection 2024.
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.
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 -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.
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.
The proposed method effectively assesses the degree of neurological impairment in AIS patients, providing an objective auxiliary assessment tool for clinical diagnosis.
准确评估神经功能缺损对于急性缺血性卒中（AIS）患者的临床治疗和预后至关重要。然而，原始灌注参数缺乏用于表征神经功能缺损的深度信息，导致难以进行准确评估。鉴于放射组学技术在特征表征方面的优势，该技术应为表征神经功能缺损提供更多信息。因此，本研究以其严谨的方法，通过探索缺血灌注放射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意义。
本研究采用了严谨的方法，首先通过动态磁敏感对比灌注加权成像（DSC-PWI）生成灌注参数图，并基于这些图和设定的阈值确定缺血区域。然后从缺血区域提取放射组学特征，并使用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，使用选定的放射组学特征和机器学习技术评估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、梗死和缺氧区域的放射组学特征及其组合，分别达到了0.926的准确率、0.923的灵敏度、0.929的特异性、0.923的阳性预测值、0.929的阴性预测值和0.923的曲线下面积。
所提出的方法有效地评估了AIS患者的神经功能缺损程度，为临床诊断提供了一种客观的辅助评估工具。
