A Deep Learning Framework for Pediatric TLE Detection Using 18F-FDG-PET Imaging
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|ClinicalTrials.gov Identifier: NCT04169581|
Recruitment Status : Completed
First Posted : November 20, 2019
Last Update Posted : November 27, 2019
|Condition or disease|
|Epilepsy, Temporal Lobe|
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.
|Study Type :||Observational|
|Actual Enrollment :||201 participants|
|Official Title:||Symmetricity-Driven Learning Framework for Pediatric Temporal Lobe Epilepsy Detection Using 18F-FDG-PET Imaging|
|Actual Study Start Date :||June 1, 2018|
|Actual Primary Completion Date :||February 28, 2019|
|Actual Study Completion Date :||April 30, 2019|
The experimental group received 18F-FDG PET examination
The control group received 18F-FDG PET examination
- The 'area under curve' (AUC ) of our model in detection performance [ Time Frame: Through study completion, an average of 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.
- The 'dice similarity coefficient' (DSC) of our model in detection performance [ Time Frame: Through study completion, an average of 3 months ]The accuracy of focus lesion detection is quantitatively measured through the metric of 'dice similarity coefficient' (DSC) by comparing the spatial overlap between the marked regions between the reference standard and the subject method under test.
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Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04169581
|Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University|
|Hangzhou, Zhejiang, China, 310009|