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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
Sponsor:
Information provided by (Responsible Party):
Second Affiliated Hospital, School of Medicine, Zhejiang University

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.

Condition or disease
Epilepsy, Temporal Lobe

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.


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Study Type : Observational
Actual Enrollment : 201 participants
Observational Model: Cohort
Time Perspective: Retrospective
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

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Epilepsy

Group/Cohort
Experimental Group
The experimental group received 18F-FDG PET examination
Control Group
The control group received 18F-FDG PET examination



Primary Outcome Measures :
  1. 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.


Secondary Outcome Measures :
  1. 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.



Information from the National Library of Medicine

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Ages Eligible for Study:   6 Years to 18 Years   (Child, Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Pediatric patients with Temporal Lobe Epilepsy
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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04169581


Locations
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China, Zhejiang
Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, China, 310009
Sponsors and Collaborators
Second Affiliated Hospital, School of Medicine, Zhejiang University

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Responsible Party: Second Affiliated Hospital, School of Medicine, Zhejiang University
ClinicalTrials.gov Identifier: NCT04169581     History of Changes
Other Study ID Numbers: 2019-124
First Posted: November 20, 2019    Key Record Dates
Last Update Posted: November 27, 2019
Last Verified: June 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Second Affiliated Hospital, School of Medicine, Zhejiang University:
Deep Learning
Temporal Lobe Epilepsy
Positron-Emission Tomography
Additional relevant MeSH terms:
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Epilepsy
Epilepsy, Temporal Lobe
Brain Diseases
Central Nervous System Diseases
Nervous System Diseases
Epilepsies, Partial
Epileptic Syndromes
Fluorodeoxyglucose F18
Radiopharmaceuticals
Molecular Mechanisms of Pharmacological Action