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

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.

Study Overview

• Status: Completed
• Conditions: 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.

• Study Type: Observational
• Enrollment (Actual): 201

Contacts and Locations

Study Locations

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

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 6 years to 18 years (Child, Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Pediatric patients with Temporal Lobe Epilepsy

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Retrospective

Number of groups / cohorts

2

Cohorts and Interventions

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University

Publications and helpful links

General 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.

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2018
• Primary Completion (Actual): February 28, 2019
• Study Completion (Actual): April 30, 2019

Study Registration Dates

• First Submitted: November 13, 2019
• First Submitted That Met QC Criteria: November 17, 2019
• First Posted (Actual): November 20, 2019

Study Record Updates

• Last Update Posted (Actual): January 2, 2020
• Last Update Submitted That Met QC Criteria: December 30, 2019
• Last Verified: June 1, 2019

More Information

Terms related to this study

• Keywords: Positron-Emission Tomography; Deep Learning; Temporal Lobe Epilepsy
• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Epileptic Syndromes; Epilepsies, Partial; Epilepsy; Epilepsy, Temporal Lobe
• Other Study ID Numbers: 2019-124

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No