A Deep Learning Framework for Pediatric TLE Detection Using 18F-FDG-PET Imaging

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Symmetricity-Driven Learning Framework for Pediatric Temporal Lobe Epilepsy Detection Using 18F-FDG-PET Imaging

Conditions
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Epilepsy, Temporal Lobe

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Clinical diagnosis of temporal lobe epilepsy. Age range from six to eighteen years old. Underwent PET, EEG, computed tomography (CT) and MRI. Exclusion Criteria: 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.

Ages Eligible for Study
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Minimum Age: 6 Years
Maximum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental Group<br>The experimental group received 18F-FDG PET examination | |
| Control Group<br>The control group received 18F-FDG PET examination | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The 'area under curve' (AUC ) of our model in detection performance | 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. | Through study completion, about 1 year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The 'dice similarity coefficient' (DSC) of our model in detection performance | 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. | Through study completion, about 3 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Deep Learning, Temporal Lobe Epilepsy, Positron-Emission Tomography