Department of Radiology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310000, Zhejiang, China.
Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, 701 Yunjin Road, Shanghai, 200030, China.
BMC Med Imaging. 2024 Sep 16;24(1):246. doi: 10.1186/s12880-024-01424-z.
This study aims to utilize the deep learning method of VB-Net to locate and segment the trigeminal nerve, and employ radiomics methods to distinguish between CTN patients and healthy individuals.
A total of 165 CTN patients and 175 healthy controls, matched for gender and age, were recruited. All subjects underwent magnetic resonance scans. VB-Net was used to locate and segment the bilateral trigeminal nerve of all subjects, followed by the application of radiomics methods for feature extraction, dimensionality reduction, feature selection, model construction, and model evaluation.
On the test set for trigeminal nerve segmentation, our segmentation parameters are as follows: the mean Dice Similarity Coefficient (mDCS) is 0.74, the Average Symmetric Surface Distance (ASSD) is 0.64 mm, and the Hausdorff Distance (HD) is 3.34 mm, which are within the acceptable range. Analysis of CTN patients and healthy controls identified 12 features with larger weights, and there was a statistically significant difference in Rad_score between the two groups (p < 0.05). The Area Under the Curve (AUC) values for the three models (Gradient Boosting Decision Tree, Gaussian Process, and Random Forest) are 0.90, 0.87, and 0.86, respectively. After testing with DeLong and McNemar methods, these three models all exhibit good performance in distinguishing CTN from normal individuals.
Radiomics can aid in the clinical diagnosis of CTN, and it is a more objective approach. It serves as a reliable neurobiological indicator for the clinical diagnosis of CTN and the assessment of changes in the trigeminal nerve in patients with CTN.
本研究旨在利用 VB-Net 深度学习方法定位和分割三叉神经，并采用放射组学方法区分 CTN 患者和健康个体。
共纳入 165 例 CTN 患者和 175 名性别和年龄匹配的健康对照者，所有受试者均接受磁共振扫描。采用 VB-Net 定位并分割所有受试者双侧三叉神经，然后应用放射组学方法进行特征提取、降维、特征选择、模型构建和模型评估。
在三叉神经分割测试集中，我们的分割参数如下：平均 Dice 相似系数（mDCS）为 0.74，平均对称表面距离（ASSD）为 0.64mm，Hausdorff 距离（HD）为 3.34mm，均在可接受范围内。CTN 患者和健康对照组分析确定了 12 个权重较大的特征，两组之间的 Rad_score 存在统计学差异（p<0.05）。三种模型（梯度提升决策树、高斯过程和随机森林）的曲线下面积（AUC）值分别为 0.90、0.87 和 0.86。经过 Delong 和 McNemar 方法测试，这三种模型在区分 CTN 与正常个体方面均表现出良好的性能。
放射组学有助于 CTN 的临床诊断，是一种更客观的方法。它是 CTN 临床诊断和评估 CTN 患者三叉神经变化的可靠神经生物学指标。
