Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls

BackgroundThis study aimed to investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls.MethodsA total of 347 subjects who...

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Bibliographic Details
Main Authors: Kai Liao, Huanhua Wu, Yuanfang Jiang, Chenchen Dong, Hailing Zhou, Biao Wu, Yongjin Tang, Jian Gong, Weijian Ye, Youzhu Hu, Qiang Guo, Hao Xu
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-04-01
Series:Frontiers in Neurology
Subjects:
temporal lobe epilepsy
positron emission tomography (PET)
radiomics
machine learning
18F-FDG
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2024.1377538/full

Internet

https://www.frontiersin.org/articles/10.3389/fneur.2024.1377538/full

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