A Deep Learning Framework for Pediatric TLE Detection Using 18F-FDG-PET Imaging

NCT ID: NCT04169581

Last Updated: 2020-01-02

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 201 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-06-01
• Study Completion Date: 2019-04-30

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.

Conditions

Epilepsy, Temporal Lobe

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Experimental Group

The experimental group received 18F-FDG PET examination

No interventions assigned to this group

Control Group

The control group received 18F-FDG PET examination

No interventions assigned to this group

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.

• Minimum Eligible Age: 6 Years
• Maximum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Second Affiliated Hospital, School of Medicine, Zhejiang University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

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

Hangzhou, Zhejiang, China

Countries

China

References

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.

Reference Type: DERIVED

PMID: 33420912 (View on PubMed)

Other Identifiers

2019-124

Identifier Type: -

Identifier Source: org_study_id