Deep neural networks analysis of 18F-FDG PET imaging in postoperative patients with temporal lobe epilepsy
10.3760/cma.j.cn321828-20231228-00150
- VernacularTitle:基于深度神经网络的颞叶癫痫 18F－FDG PET术后复发预测研究
- Author:
Huanhua WU
1
;
Shaobo CHEN
;
Jingjie SHANG
;
Hailing ZHOU
;
Biao WU
;
Jian GONG
;
Xueying LING
;
Qiang GUO
;
Hao XU
Author Information
1. 暨南大学附属第一医院核医学科，广州 510630
- Keywords:
Epilepsy, temporal lobe;
Recurrence;
Neural networks (computer);
Positron-emission tomography;
Fluorodeoxyglucose F18;
Forecasting
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2024;44(4):220-224
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To predict the short-term postoperative recurrence status of patients with refractory temporal lobe epilepsy (TLE) by analyzing preoperative 18F-FDG PET images and patients′ clinical characteristics based on deep residual neural network (ResNet). Methods:Retrospective analysis was conducted on preoperative 18F-FDG PET images and clinical data of 220 patients with refractory TLE (132 males and 88 females, age 23.0(20.0, 30.2) years)) in the First Affiliated Hospital of Jinan University between January 2014 and June 2020. ResNet was used to perform high-throughput feature extraction on preprocessed PET images and clinical features, and to perform a postoperative recurrence prediction task for differentiating patients with TLE. The predictive performance of ResNet model was evaluated by ROC curve analysis, and the AUC was compared with that of classical Cox proportional risk model using Delong test. Results:Based on PET images combined with clinical feature training, AUCs of the ResNet in predicting 12-, 24-, and 36-month postoperative recurrence were 0.895±0.073, 0.861±0.058 and 0.754±0.111, respectively, which were 0.717±0.093, 0.697±0.081 and 0.645±0.087 for Cox proportional hazards model respectively ( z values: -3.00, -2.98, -1.09, P values: 0.011, 0.018, 0.310). The ResNet showed best predictive effect for recurrence events within 12 months after surgery. Conclusion:The ResNet model is expected to be used in clinical practice for postoperative follow-up of patients with TLE, helping for risk stratification and individualized management of postoperative patients.
