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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1460041

Radiomics Based on Diffusion Tensor Imaging and 3D T1weighted MRI for Essential Tremor Diagnosis

Provisionally accepted
Bintao Xu Bintao Xu Li Tao Li Tao *Honge Gui Honge Gui *Pan Xiao Pan Xiao Xiaole Zhao Xiaole Zhao *Hongyu Wang Hongyu Wang *Huiyue Chen Huiyue Chen *Hansheng Wang Hansheng Wang *Fajin Lv Fajin Lv Tianyou Luo Tianyou Luo *Oumei Cheng Oumei Cheng *Jing Luo Jing Luo *Yun Man Yun Man *Zheng Xiao Zheng Xiao Weidong Fang Weidong Fang *
  • Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China

The final, formatted version of the article will be published soon.

    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 T1weighted 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 in ET patients.These results demonstrated that combining radiomics with ML algorithms could not only achieve high classification accuracy for identifying ET but also help us to reveal the potential brain microstructure pathogenesis in ET patients.

    Keywords: Essential Tremor, machine learning, Radiomics, Diffusion Tensor Imaging, 3D T1-weighted MRI

    Received: 05 Jul 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Xu, Tao, Gui, Xiao, Zhao, Wang, Chen, Wang, Lv, Luo, Cheng, Luo, Man, Xiao and Fang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Li Tao, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Honge Gui, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Xiaole Zhao, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Hongyu Wang, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Huiyue Chen, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Hansheng Wang, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Tianyou Luo, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Oumei Cheng, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Jing Luo, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Yun Man, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
    Weidong Fang, Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China

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