Epilepsy, Temporal LobeNCT04169581
A Deep Learning Framework for Pediatric TLE Detection Using 18F-FDG-PET Imaging
An observational study of Epilepsy, Temporal Lobe, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 1 site in China.
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… Read more about this study
- Who is it for?
- Ages 6 to 18 years
- Conditions the study is for
- Epilepsy, Temporal Lobe
- How many people
- 201 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2018to 30 Apr 2019
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 6 to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
Study population: Pediatric patients with Temporal Lobe Epilepsy
Who may be able to join
- Clinical diagnosis of temporal lobe epilepsy.
- Age range from six to eighteen years old.
- Underwent PET, EEG, computed tomography (CT) and MRI.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, 310009
About this study
Official title: Symmetricity-Driven Learning Framework for Pediatric Temporal Lobe Epilepsy Detection Using 18F-FDG-PET Imaging
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.
Read the 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.
- Conditions
- Epilepsy, Temporal Lobe
- Keywords
- Deep Learning, Temporal Lobe Epilepsy, Positron-Emission Tomography
- Medical subject headings
- Epilepsy, Temporal Lobe
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Experimental Group | The experimental group received 18F-FDG PET examination | |
| Control Group | The control group received 18F-FDG PET examination |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, about 1 year
Secondary outcomes (1)
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.
Measured: Through study completion, about 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Nov 2019
- Study start1 Jun 2018
- Primary completion28 Feb 2019
- Study completion30 Apr 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Epilepsy, Temporal LobeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 201 participants (actual)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04169581
- Sponsor's protocol ID
- 2019-124
- Status last verified
- Jun 2019
- First submitted
- 13 Nov 2019
- Last updated
- 2 Jan 2020
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 33420912
Study at a glance
From the registry record, last updated by the sponsor on 2 Jan 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Symmetricity-Driven Learning Framework for Pediatric Temporal Lobe Epilepsy Detection Using 18F-FDG-PET Imaging
- ClinicalTrials.gov ID
- NCT04169581
- Acronym / sponsor ID
- 2019-124
- Conditions
- Epilepsy, Temporal Lobe
- Keywords
- Deep Learning, Temporal Lobe Epilepsy, Positron-Emission Tomography
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 201 participants (actual)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Study start
- 1 Jun 2018 (actual)
- Primary completion
- 28 Feb 2019 (actual)
- Study completion
- 30 Apr 2019 (actual)
- First posted
- 20 Nov 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Experimental Group | – | –The experimental group received 18F-FDG PET examination |
| Control Group | – | –The control group received 18F-FDG PET examination |
Who may qualify
- Ages
- 6 to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- Study population
- Pediatric patients with Temporal Lobe Epilepsy
Key inclusion criteria (3 listed)
- Clinical diagnosis of temporal lobe epilepsy.
- Age range from six to eighteen years old.
- Underwent PET, EEG, computed tomography (CT) and MRI.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The 'area under curve' (AUC ) of our model in detection performance
Time frame: Through study completion, about 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.
1 secondary outcome
The 'dice similarity coefficient' (DSC) of our model in detection performance
Time frame: Through study completion, about 3 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Jan 2020.
- Derived from this study: PubMed 33420912Zhang 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Epilepsy, Temporal Lobe, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 2 Jan 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The 'area under curve' (AUC ) of our model in detection performance (time frame: Through study completion, about 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04169581, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Jan 2020; AzTrials last refreshed its copy on 27 Oct 2026.