基于 VB-Net 技术和放射组学的典型三叉神经痛诊断应用研究

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-09-16 DOI:10.1186/s12880-024-01424-z
Lei Pan, Xuechun Wang, Xiuhong Ge, Haiqi Ye, Xiaofen Zhu, Qi Feng, Haibin Wang, Feng Shi, Zhongxiang Ding
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引用次数: 0

摘要

本研究旨在利用 VB-Net 的深度学习方法来定位和分割三叉神经,并采用放射组学方法来区分 CTN 患者和健康人。研究共招募了 165 名 CTN 患者和 175 名健康对照者,他们的性别和年龄均匹配。所有受试者均接受了磁共振扫描。使用 VB-Net 对所有受试者的双侧三叉神经进行定位和分割,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:平均骰子相似系数(mDCS)为 0.74,平均对称面距离(ASSD)为 0.64 毫米,豪斯多夫距离(HD)为 3.34 毫米,均在可接受范围内。对 CTN 患者和健康对照组的分析发现,有 12 个特征的权重较大,两组之间的 Rad_score 差异有统计学意义(P < 0.05)。三个模型(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 方法的测试,这三个模型在区分 CTN 和正常人方面都表现出良好的性能。放射组学有助于 CTN 的临床诊断,是一种更为客观的方法。它是临床诊断 CTN 和评估 CTN 患者三叉神经变化的可靠神经生物学指标。
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Application research on the diagnosis of classic trigeminal neuralgia based on VB-Net technology and radiomics
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.
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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