Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China.
Cereb Cortex. 2020 Mar 14;30(3):1117-1128. doi: 10.1093/cercor/bhz152.
The aim of this study was to develop and validate a method of disease classification for bipolar disorder (BD) by functional activity and connectivity using radiomics analysis. Ninety patients with unmedicated BD II as well as 117 healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI). A total of 4 types of 7018 features were extracted after preprocessing, including mean regional homogeneity (mReHo), mean amplitude of low-frequency fluctuation (mALFF), resting-state functional connectivity (RSFC), and voxel-mirrored homotopic connectivity (VMHC). Then, predictive features were selected by Mann-Whitney U test and removing variables with a high correlation. Least absolute shrinkage and selection operator (LASSO) method was further used to select features. At last, support vector machine (SVM) model was used to estimate the state of each subject based on the selected features after LASSO. Sixty-five features including 54 RSFCs, 7 mALFFs, 1 mReHo, and 3 VMHCs were selected. The accuracy and area under curve (AUC) of the SVM model built based on the 65 features is 87.3% and 0.919 in the training dataset, respectively, and the accuracy and AUC of this model validated in the validation dataset is 80.5% and 0.838, respectively. These findings demonstrate a valid radiomics approach by rs-fMRI can identify BD individuals from healthy controls with a high classification accuracy, providing the potential adjunctive approach to clinical diagnostic systems.
本研究旨在通过放射组学分析，利用功能活动和连通性为双相障碍（BD）开发和验证一种疾病分类方法。90 名未经药物治疗的 BD II 患者和 117 名健康对照者接受了静息态功能磁共振成像（rs-fMRI）检查。预处理后提取了 4 种共 7018 种特征，包括平均局部一致性（mReHo）、低频波动幅度（mALFF）、静息态功能连接（RSFC）和体素镜像同伦连接（VMHC）。然后，通过曼-惠特尼 U 检验和去除高相关变量选择预测特征。最小绝对值收缩和选择算子（LASSO）方法进一步用于选择特征。最后，基于 LASSO 后选择的特征，使用支持向量机（SVM）模型估计每个受试者的状态。共选择了 65 个特征，包括 54 个 RSFC、7 个 mALFF、1 个 mReHo 和 3 个 VMHC。基于 65 个特征构建的 SVM 模型在训练数据集中的准确率和曲线下面积（AUC）分别为 87.3%和 0.919，在验证数据集中的准确率和 AUC 分别为 80.5%和 0.838。这些发现表明，基于 rs-fMRI 的放射组学方法可以以较高的分类准确率识别 BD 个体与健康对照者，为临床诊断系统提供了潜在的辅助方法。
