A Deep Learning Framework for Pediatric TLE Detection Using 18F-FDG-PET Imaging
Symmetricity-Driven Learning Framework for Pediatric Temporal Lobe Epilepsy Detection Using 18F-FDG-PET Imaging
1 other identifier
observational
201
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jun 2018
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
June 1, 2018
CompletedPrimary Completion
Last participant's last visit for primary outcome
February 28, 2019
CompletedStudy Completion
Last participant's last visit for all outcomes
April 30, 2019
CompletedFirst Submitted
Initial submission to the registry
November 13, 2019
CompletedFirst Posted
Study publicly available on registry
November 20, 2019
CompletedJanuary 2, 2020
June 1, 2019
9 months
November 13, 2019
December 30, 2019
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
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 Outcomes (1)
The 'dice similarity coefficient' (DSC) of our model in detection performance
Through study completion, about 3 months
Study Arms (2)
Experimental Group
The experimental group received 18F-FDG PET examination
Control Group
The control group received 18F-FDG PET examination
Eligibility Criteria
Pediatric patients with Temporal Lobe Epilepsy
You may qualify if:
- Clinical diagnosis of temporal lobe epilepsy.
- Age range from six to eighteen years old.
- Underwent PET, EEG, computed tomography (CT) and MRI.
You may not qualify if:
- Image quality is unsatisfactory (e.g. severe image artifacts due to head movement).
- F-FDG PEG examination is negative.
- Clinical data is incomplete.
- EEG or MRI report is missing.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, 310009, China
Related Publications (1)
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.
PMID: 33420912DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
November 13, 2019
First Posted
November 20, 2019
Study Start
June 1, 2018
Primary Completion
February 28, 2019
Study Completion
April 30, 2019
Last Updated
January 2, 2020
Record last verified: 2019-06
Data Sharing
- IPD Sharing
- Will not share