Radiomics based on diffusion tensor imaging and 3D T1-weighted MRI for essential tremor diagnosis

作者：Bintao Xu, Li Tao, Honge Gui, Xiao Pan, Xiaole Zhao, Hongyu Wang, Huiyue Chen, Hansheng Wang, Fajin Lv, Tianyou Luo, Oumei Cheng, Jing Luo, Yun Man, Zheng Xiao, Weidong Fang · 发表于：Frontiers in Neurology · 年份：2024 · DOI：10.3389/fneur.2024.1460041 · 被引用次数：4 · 研究领域：Neurological disorders and treatments、Advanced Neuroimaging Techniques and Applications、Transcranial Magnetic Stimulation Studies

Background: Due to the absence of biomarkers, the misdiagnosis of essential tremor (ET) with other tremor diseases and enhanced physiologic tremor is very common in practice. Combined radiomics based on diffusion tensor imaging (DTI) and three-dimensional T1-weighted imaging (3D-T1) with machine learning (ML) give a most promising way to identify essential tremor (ET) at the individual level and further reveal the potential imaging biomarkers. Methods: Radiomics features were extracted from 3D-T1 and DTI in 103 ET patients and 103 age-and sex-matched healthy controls (HCs). After data dimensionality reduction and feature selection, five classifiers, including the support vector machine (SVM), random forest (RF), logistic regression (LR), extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP), were adopted to discriminate ET from HCs. The mean values of the area under the curve (mAUC) and accuracy were used to assess the model's performance. Furthermore, a correlation analysis was conducted between the most discriminative features and clinical tremor characteristics. Results: All classifiers achieved good classification performance (with mAUC at 0.987, 0.984, 0.984, 0.988 and 0.981 in the test set, respectively). The most powerful discriminative features mainly located in the cerebella-thalamo-cortical (CTC) and visual pathway. Furthermore, correlation analysis revealed that some radiomics features were significantly related to the clinical tremor characteristics...