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

Epilepsy, Temporal Lobe Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04169581
• Other study ID #: 2019-124
• Status: Completed
• Start date: June 1, 2018
• Est. completion date: April 30, 2019

Study information

• Verified date: June 2019
• Source: Second Affiliated Hospital, School of Medicine, Zhejiang University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 201
• Est. completion date: April 30, 2019
• Est. primary completion date: February 28, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 6 Years to 18 Years

Eligibility

Inclusion Criteria:

1. Clinical diagnosis of temporal lobe epilepsy.

2. Age range from six to eighteen years old.

3. Underwent PET, EEG, computed tomography (CT) and MRI.

Exclusion Criteria:

1. Image quality is unsatisfactory (e.g. severe image artifacts due to head movement).

2. 18F-FDG PEG examination is negative.

3. Clinical data is incomplete.

4. EEG or MRI report is missing.

Related Conditions & MeSH terms

• Epilepsy
• Epilepsy, Temporal Lobe

Locations

Country	Name	City	State
China	Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University	Hangzhou	Zhejiang

Sponsors (1)

Lead Sponsor	Collaborator
Second Affiliated Hospital, School of Medicine, Zhejiang University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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