A deep learning framework for <sup>18</sup>F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy

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Abstract

Purpose

Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, ¹⁸F-fluorodeoxyglucose positron emission tomography ( ¹⁸F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE).

Methods

We retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent ¹⁸F-FDG PET-CT studies. ¹⁸F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis.

Results

The proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31–0.44, P = 0.005–0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01).

Conclusion

The proposed deep learning framework for ¹⁸F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients.

Trial registration

NCT04169581. Registered November 13, 2019

Public site: https://clinicaltrials.gov/ct2/show/NCT04169581

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-020-05108-y.

Related collections

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Author and article information

Contributors

Hong Zhang: hzhang21@zju.edu.cn

Cheng Zhuo: czhuo@zju.edu.cn

Mei Tian:

ORCID: http://orcid.org/0000-0002-1587-2114

meitian@zju.edu.cn

Journal

Journal ID (nlm-ta): Eur J Nucl Med Mol Imaging

Journal ID (iso-abbrev): Eur J Nucl Med Mol Imaging

Title: European Journal of Nuclear Medicine and Molecular Imaging

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 1619-7070

ISSN (Electronic): 1619-7089

Publication date (Electronic): 9 January 2021

Publication date PMC-release: 9 January 2021

Publication date (Print): 2021

Volume: 48

Issue: 8

Pages: 2476-2485

Affiliations

[1 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Department of Nuclear Medicine and PET-CT Center, , The Second Hospital of Zhejiang University School of Medicine, ; Hangzhou, Zhejiang China

[2 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, College of Information Science & Electronic Engineering, , Zhejiang University, ; Hangzhou, Zhejiang China

[3 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Key Laboratory for Biomedical Engineering of Ministry of Education, , Zhejiang University, ; Hangzhou, Zhejiang China

[4 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Department of Pediatrics, , The Second Hospital of Zhejiang University School of Medicine, ; Hangzhou, Zhejiang China

[5 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Department of Neurology, Epilepsy Center, , The Second Hospital of Zhejiang University School of Medicine, ; Hangzhou, Zhejiang China

[6 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Center of Clinical Epidemiology & Biostatistics, , The Second Hospital of Zhejiang University School of Medicine, ; Hangzhou, Zhejiang China

[7 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, College of Biomedical Engineering and Instrument Science, , Zhejiang University, ; Hangzhou, Zhejiang China

Author information

Mei Tian http://orcid.org/0000-0002-1587-2114

Article

Publisher ID: 5108

DOI: 10.1007/s00259-020-05108-y

PMC ID: 8241642

PubMed ID: 33420912

SO-VID: 9bc75bbb-5405-4e92-9097-8d18b9545049

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 29 May 2020

Date accepted : 8 November 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 81725009

Award Recipient : Mei Tian

Custom metadata

ScienceOpen disciplines: Radiology & Imaging

Keywords: deep learning,epilepsy,pediatrics,positron emission tomography (pet),glucose metabolism

Data availability:

ScienceOpen disciplines: Radiology & Imaging

Keywords: deep learning, epilepsy, pediatrics, positron emission tomography (pet), glucose metabolism