Symmetricity-Driven Learning Framework for Pediatric Temporal Lobe Epilepsy Detection Using 18F-FDG-PET Imaging
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 201
- 试验地点
- 1
- 主要终点
- The 'area under curve' (AUC ) of our model in detection performance
研究概览
简要总结
This study aims to use radiomics analysis and deep learning approaches for seizure focus detection in pediatric patients with temporal lobe epilepsy (TLE). Ten positron emission tomograph (PET) radiomics features related to pediatric temporal bole epilepsy are extracted and modelled, and the Siamese network is trained to automatically locate epileptogenic zones for assistance of diagnosis.
详细描述
Purpose:The key to successful epilepsy control involves locating epileptogenic focus before treatment. 18F-FDG PET has been considered as a powerful neuroimaging technology used by physicians to assess patients for epilepsy. However, imaging quality, viewing angles, and experiences may easily degrade the consistency in epilepsy diagnosis. In this work, the investigators develop a framework that combines radiomics analysis and deep learning techniques to a computer-assisted diagnosis (CAD) method to detect epileptic foci of pediatric patients with temporal lobe epilepsy (TLE) using PET images.
Methods:Ten PET radiomics features related to pediatric temporal bole epilepsy are first extracted and modelled. Then a neural network called Siamese network is trained to quanti-fy the asymmetricity and automatically locate epileptic focus for diagnosis.The performance of the proposed framework was tested and compared with both the state-of-art clinician software tool and human physicians with different levels of experiences to validate the accuracy and consistency.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 6 Years 至 18 Years（Child, Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Clinical diagnosis of temporal lobe epilepsy.
- •Age range from six to eighteen years old.
- •Underwent PET, EEG, computed tomography (CT) and MRI.
排除标准
- •Image quality is unsatisfactory (e.g. severe image artifacts due to head movement).
- •18F-FDG PEG examination is negative.
- •Clinical data is incomplete.
- •EEG or MRI report is missing.
结局指标
主要结局
The 'area under curve' (AUC ) of our model in detection performance
时间窗: Through study completion, about 1 year
To evaluate the performance of our model, the investigators calculated the AUC of our model for normal or abnormal classification campared with different methods and and physicians with different levels.
次要结局
- The 'dice similarity coefficient' (DSC) of our model in detection performance(Through study completion, about 3 months)