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基于多模态影像的难治性颞叶癫痫分型研究
引用本文:崔娅,黄慧,张淼,袁思宇,蔡冰洋,李纪伟,刘伟,罗洁. 基于多模态影像的难治性颞叶癫痫分型研究[J]. 生物医学工程学进展, 2022, 0(4): 188-198
作者姓名:崔娅  黄慧  张淼  袁思宇  蔡冰洋  李纪伟  刘伟  罗洁
作者单位:上海交通大学生物医学工程学院;上海交通大学医学院附属瑞金医院核医学科;上海交通大学医学院附属瑞金医院功能神经外科
摘    要:约1/4的难治性颞叶癫痫患者的发作无法通过常规前颞叶切除术得到有效控制,这可能是因为疾病存在多种复杂的亚型,且部分亚型涉及颞叶外的脑区病变。基于多模态脑影像特征,使用隐含狄利克雷分布模型可以估计潜在疾病因子及各因子在患者大脑内的表达程度,有助于个体精准分型。该研究通过PET/MR同步一体扫描仪采集了86名难治性颞叶癫痫患者的T1加权影像和FDG PET影像,并采集了39名健康志愿者的T1加权影像和36名健康志愿者的FDG PET影像作为对照。该研究提取与癫痫病理相关的灰质体积和葡萄糖摄取值,使用隐含狄利克雷分布进行分型,将疾病因子的数量分别设为3、4、5,进行多次实验,并对四因子模型中的各疾病因子与临床指标之间的关系进行统计分析。研究发现,选取不同数量的疾病因子进行分型时得到的结果具有一定的相似性。在四因子模型中存在与病灶偏侧性、癫痫发作年限和认知能力显著相关(P<0.05)的疾病因子。该研究对颞叶癫痫分型具有一定的参考价值。

关 键 词:难治性颞叶癫痫;隐含狄利克雷分布;疾病分型
收稿时间:2022-11-27
修稿时间:2023-01-15

Research on subtypes of refractory temporal lobe epilepsy based on multi-model images
Ya Cui,Hui Huang,Miao Zhang,Siyu Yuan,Binyang Cai,Jiwei Li,Wei Zhang and Jie Luo. Research on subtypes of refractory temporal lobe epilepsy based on multi-model images[J]. Progress in Biomedical Engineering, 2022, 0(4): 188-198
Authors:Ya Cui  Hui Huang  Miao Zhang  Siyu Yuan  Binyang Cai  Jiwei Li  Wei Zhang  Jie Luo
Affiliation:College of Biomedical Engineering, Shanghai Jiao Tong University;Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University;Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University
Abstract:One-quarter of patients with refractory temporal lobe epilepsy cannot be effectively controlled by conventional anterior temporal lobectomy, which may be due to the existence of a variety of complex disease''s subtypes, and some subtypes involve brain lesions outside the temporal lobe. Based on the characteristics of multi-modal brain imaging, using Latent Dirichlet Allocation model can estimate the potential disease factors and the probability of each factor in the brain of patients, which is conducive to the accurate classification of individuals. 86 refractory temporal lobe epilepsy patients were recruited and were scanned with a hybrid PET/MR scanner to collect T1-weighted images and FDG PET images. 39 healthy volunteers with T1-weighted images and 36 healthy volunteers with FDG PET images were also recruited as controls. In this study, the gray matter volume and glucose uptake values were extracted, and the Latent Dirichlet Allocation was used for typing.The number of disease factors was set as 3, 4 and 5 respectively for multiple experiments. Then a four-factor model was used to statistically analyze the relationship between disease factors and clinical indicators. It is found that the results obtained by selecting different number of disease factors for classification had certain similarity. In the four-factor model, there were disease factors that were significantly related to the laterality of lesions, disease duration and cognitive ability (P<0.05). This research has reference value for the classification of temporal lobe epilepsy.
Keywords:Refractory Temporal Lobe Epilepsy   Latent Dirichlet Allocation   Disease Classification
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