Pan Jian, Lv Ruijuan, Zhou Guifei, Si Run, Wang Qun, Zhao Xiaobin, Liu Jiangang, Ai Lin
School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University; China National Clinical Research Center for Neurological Diseases, Beijing, China.
Front Neurol. 2022 May 30;13:812439. doi: 10.3389/fneur.2022.812439. eCollection 2022.
This study aims to detect the invisible metabolic abnormality in PET images of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis using a multivariate cross-classification method.
Participants were divided into two groups, namely, the training cohort and the testing cohort. The training cohort included 17 healthy participants and 17 patients with anti-LGI1 encephalitis whose metabolic abnormality was able to be visibly detected in both the medial temporal lobe and the basal ganglia in their PET images [completely detectable (CD) patients]. The testing cohort included another 16 healthy participants and 16 patients with anti-LGI1 encephalitis whose metabolic abnormality was not able to be visibly detected in the medial temporal lobe and the basal ganglia in their PET images [non-completely detectable (non-CD) patients]. Independent component analysis (ICA) was used to extract features and reduce dimensions. A logistic regression model was constructed to identify the non-CD patients.
For the testing cohort, the accuracy of classification was 90.63% with 13 out of 16 non-CD patients identified and all healthy participants distinguished from non-CD patients. The patterns of PET signal changes resulting from metabolic abnormalities related to anti-LGI1 encephalitis were similar for CD patients and non-CD patients.
This study demonstrated that multivariate cross-classification combined with ICA could improve, to some degree, the detection of invisible abnormal metabolism in the PET images of patients with anti-LGI1 encephalitis. More importantly, the invisible metabolic abnormality in the PET images of non-CD patients showed patterns that were similar to those seen in CD patients.
本研究旨在使用多变量交叉分类方法检测抗富含亮氨酸胶质瘤失活1（LGI1）脑炎患者PET图像中不可见的代谢异常。
参与者分为两组，即训练队列和测试队列。训练队列包括17名健康参与者和17名抗LGI1脑炎患者，其代谢异常在PET图像的内侧颞叶和基底神经节中均可明显检测到[完全可检测（CD）患者]。测试队列包括另外16名健康参与者和16名抗LGI1脑炎患者，其代谢异常在PET图像的内侧颞叶和基底神经节中无法明显检测到[非完全可检测（非CD）患者]。使用独立成分分析（ICA）提取特征并降维。构建逻辑回归模型以识别非CD患者。
对于测试队列，分类准确率为90.63%，16名非CD患者中有13名被识别，所有健康参与者与非CD患者区分开来。CD患者和非CD患者中与抗LGI1脑炎相关的代谢异常导致的PET信号变化模式相似。
本研究表明，多变量交叉分类结合ICA可以在一定程度上提高抗LGI1脑炎患者PET图像中不可见异常代谢的检测率。更重要的是，非CD患者PET图像中的不可见代谢异常显示出与CD患者相似的模式。
