Amygdala's T1-weighted image radiomics outperforms volume for differentiation of anxiety disorder and its subtype.
Amygdala's T1-weighted image radiomics outperforms volume for differentiation of anxiety disorder and its subtype.
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DOI：
10.3389/fpsyt.2023.1091730
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发表时间：
2023
影响因子：
4.7
通讯作者：
Hu, Zhishan
中科院分区：
文献类型：
--
作者：
Li, Qingfeng;Wang, Wenzheng;Hu, Zhishan
焦虑症是青少年中最常见的精神障碍，广泛性焦虑症(GAD)是焦虑症的常见亚型。目前的研究表明，与健康人相比，焦虑症患者的杏仁核功能异常。然而，焦虑症及其亚型的诊断仍然缺乏杏仁核T1加权结构磁共振(MR)成像的具体特征。本研究旨在探讨应用放射组学方法在杏仁核T1加权像上区分焦虑症及其亚型与正常对照的可行性，为焦虑症的临床诊断提供依据。在健康脑网络(HBN)数据集中获得了200名焦虑症患者(包括103名广泛性痴呆症患者)和138名健康对照的T1加权磁共振图像。我们分别提取了左杏仁核和右杏仁核的107个放射组学特征，然后使用10倍套索回归算法进行特征选择。对于所选择的特征，我们进行分组比较，并使用不同的机器学习算法，包括线性核支持向量机(SVM)，来实现患者和健康对照的分类。对于焦虑症患者与健康对照组的分类任务，分别从左侧和右侧杏仁核选取2个和4个放射组学特征，交叉验证实验中线性核支持向量机的受试者工作特征曲线下面积分别为左侧杏仁核特征0.6739±0.0708和右侧杏仁核特征0.6403±0.0519；广泛性痴呆患者与健康对照组的分类任务分别从左侧和右侧杏仁核选取7个和3个特征，交叉验证杏仁核特征曲线下面积分别为0.6755±0.0615和0.6966±0.0854。在两种分类任务中，与杏仁核体积相比，选定的杏仁核放射组学特征具有更高的区分性意义和效应大小。我们的研究表明，双侧杏仁核的放射组学特征可能成为焦虑症临床诊断的基础。
Anxiety disorder is the most common psychiatric disorder among adolescents, with generalized anxiety disorder (GAD) being a common subtype of anxiety disorder. Current studies have revealed abnormal amygdala function in patients with anxiety compared with healthy people. However, the diagnosis of anxiety disorder and its subtypes still lack specific features of amygdala from T1-weighted structural magnetic resonance (MR) imaging. The purpose of our study was to investigate the feasibility of using radiomics approach to distinguish anxiety disorder and its subtype from healthy controls on T1-weighted images of the amygdala, and provide a basis for the clinical diagnosis of anxiety disorder. T1-weighted MR images of 200 patients with anxiety disorder (including 103 GAD patients) as well as 138 healthy controls were obtained in the Healthy Brain Network (HBN) dataset. We extracted 107 radiomics features for the left and right amygdala, respectively, and then performed feature selection using the 10-fold LASSO regression algorithm. For the selected features, we performed group-wise comparisons, and use different machine learning algorithms, including linear kernel support vector machine (SVM), to achieve the classification between the patients and healthy controls. For the classification task of anxiety patients vs. healthy controls, 2 and 4 radiomics features were selected from left and right amygdala, respectively, and the area under receiver operating characteristic curve (AUC) of linear kernel SVM in cross-validation experiments was 0.6739±0.0708 for the left amygdala features and 0.6403±0.0519 for the right amygdala features; for classification task for GAD patients vs. healthy controls, 7 and 3 features were selected from left and right amygdala, respectively, and the cross-validation AUCs were 0.6755±0.0615 for the left amygdala features and 0.6966±0.0854 for the right amygdala features. In both classification tasks, the selected amygdala radiomics features had higher discriminatory significance and effect sizes compared with the amygdala volume. Our study suggest that radiomics features of bilateral amygdala potentially could serve as a basis for the clinical diagnosis of anxiety disorder.
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