Clinical trial NCT04169581: A Deep Learning Framework for Pediatric TLE Detection Using 18F-FDG-PET Imaging
Official title: Symmetricity-Driven Learning Framework for Pediatric Temporal Lobe Epilepsy Detection Using 18F-FDG-PET Imaging
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Epilepsy, Temporal Lobe
Keywords: Deep Learning
Temporal Lobe Epilepsy
Positron-Emission Tomography
Lead sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Sponsor class: OTHER
First posted: 2019-11-20
Enrollment: 201
Sex: ALL
Minimum age (years): 6
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Pediatric patients with Temporal Lobe Epilepsy

Brief 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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: The 'area under curve' (AUC ) of our model in detection performance
timeFrame: Through study completion, about 1 year
Secondary outcomes:
measure: The 'dice similarity coefficient' (DSC) of our model in detection performance
timeFrame: Through study completion, about 3 months
Other outcomes:
Locations:
city: Hangzhou
country: China
facility: Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University
state: Zhejiang
Study officials:
References:
citation: Zhang Q, Liao Y, Wang X, Zhang T, Feng J, Deng J, Shi K, Chen L, Feng L, Ma M, Xue L, Hou H, Dou X, Yu C, Ren L, Ding Y, Chen Y, Wu S, Chen Z, Zhang H, Zhuo C, Tian M. A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy. Eur J Nucl Med Mol Imaging. 2021 Jul;48(8):2476-2485. doi: 10.1007/s00259-020-05108-y. Epub 2021 Jan 9.