Xu Bintao, Tao Li, Gui Honge, Xiao Pan, Zhao Xiaole, Wang Hongyu, Chen Huiyue, Wang Hansheng, Lv Fajin, Luo Tianyou, Cheng Oumei, Luo Jing, Man Yun, Xiao Zheng, Fang Weidong
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Neurol. 2024 Aug 27;15:1460041. doi: 10.3389/fneur.2024.1460041. eCollection 2024.
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.
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.
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.
由于缺乏生物标志物，在实际临床中，原发性震颤（ET）与其他震颤疾病以及生理性震颤增强的误诊情况非常普遍。基于扩散张量成像（DTI）和三维T1加权成像（3D-T1）的联合放射组学与机器学习（ML）相结合，为在个体水平上识别原发性震颤（ET）并进一步揭示潜在的影像学生物标志物提供了一种非常有前景的方法。
从103例ET患者以及103例年龄和性别匹配的健康对照（HC）的3D-T1和DTI中提取放射组学特征。在进行数据降维和特征选择后，采用包括支持向量机（SVM）、随机森林（RF）、逻辑回归（LR）、极端梯度提升（XGBoost）和多层感知器（MLP）在内的五种分类器，将ET与HC进行区分。采用曲线下面积均值（mAUC）和准确率来评估模型性能。此外，对最具鉴别力的特征与临床震颤特征进行了相关性分析。
所有分类器均取得了良好的分类性能（测试集中的mAUC分别为0.987、0.984、0.984、0.988和0.981）。最强大的鉴别特征主要位于小脑-丘脑-皮质（CTC）和视觉通路。此外，相关性分析显示，一些放射组学特征与ET患者的临床震颤特征显著相关。
这些结果表明，将放射组学与ML算法相结合，不仅可以在识别ET方面实现高分类准确率，还有助于我们揭示ET患者潜在的脑微结构发病机制。
