Wang Fuxu, Ma Anbang, Wu Zeyu, Xie Mingchen, Lun Peng, Sun Peng
Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, China.
Shanghai Xunshi Technology Co., Ltd., Shanghai, China.
Front Neurosci. 2023 Oct 9;17:1188590. doi: 10.3389/fnins.2023.1188590. eCollection 2023.
The study aims to develop a magnetic resonance imaging (MRI)-based radiomics model for the diagnosis of classic trigeminal neuralgia (cTN). This study involved 350 patients with cTN and 100 control participants. MRI data were collected retrospectively for all the enrolled subjects. The symptomatic side trigeminal nerve regions of patients and both sides of the trigeminal nerve regions of control participants were manually labeled on MRI images. Radiomics features of the areas labeled were extracted. Principle component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were utilized as the preliminary feature reduction methods to decrease the high dimensionality of radiomics features. Machine learning methods were established, including LASSO logistic regression, support vector machine (SVM), and Adaboost methods, evaluating each model's diagnostic abilities using 10-fold cross-validation. All the models showed excellent diagnostic ability in predicting trigeminal neuralgia. A prospective study was conducted, 20 cTN patients and 20 control subjects were enrolled to validate the clinical utility of all models. Results showed that the radiomics models based on MRI can predict trigeminal neuralgia with high accuracy, which could be used as a diagnostic tool for this disorder.
本研究旨在开发一种基于磁共振成像（MRI）的放射组学模型，用于诊断经典三叉神经痛（cTN）。本研究纳入了350例cTN患者和100名对照参与者。对所有纳入的受试者进行回顾性MRI数据收集。在MRI图像上手动标记患者有症状一侧的三叉神经区域以及对照参与者两侧的三叉神经区域。提取标记区域的放射组学特征。主成分分析（PCA）和最小绝对收缩和选择算子（LASSO）回归被用作初步的特征降维方法，以降低放射组学特征的高维度。建立机器学习方法，包括LASSO逻辑回归、支持向量机（SVM）和Adaboost方法，使用10折交叉验证评估每个模型的诊断能力。所有模型在预测三叉神经痛方面均显示出优异的诊断能力。进行了一项前瞻性研究，纳入20例cTN患者和20名对照受试者，以验证所有模型的临床实用性。结果表明，基于MRI的放射组学模型能够高精度地预测三叉神经痛，可作为该疾病的诊断工具。
