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Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients

(18)F-FDG PET is widely used in epilepsy surgery. We established a robust quantitative algorithm for the lateralization of epileptogenic foci and examined the value of machine learning of (18)F-FDG PET data in medial temporal lobe epilepsy (MTLE) patients. PATIENTS AND METHODS: We retrospectively re...

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Autores principales: Shih, Yen-Cheng, Lee, Tse-Hao, Yu, Hsiang-Yu, Chou, Chien-Chen, Lee, Cheng-Chia, Lin, Po-Tso, Peng, Syu-Jyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8884180/
https://www.ncbi.nlm.nih.gov/pubmed/35085166
http://dx.doi.org/10.1097/RLU.0000000000004072
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author Shih, Yen-Cheng
Lee, Tse-Hao
Yu, Hsiang-Yu
Chou, Chien-Chen
Lee, Cheng-Chia
Lin, Po-Tso
Peng, Syu-Jyun
author_facet Shih, Yen-Cheng
Lee, Tse-Hao
Yu, Hsiang-Yu
Chou, Chien-Chen
Lee, Cheng-Chia
Lin, Po-Tso
Peng, Syu-Jyun
author_sort Shih, Yen-Cheng
collection PubMed
description (18)F-FDG PET is widely used in epilepsy surgery. We established a robust quantitative algorithm for the lateralization of epileptogenic foci and examined the value of machine learning of (18)F-FDG PET data in medial temporal lobe epilepsy (MTLE) patients. PATIENTS AND METHODS: We retrospectively reviewed patients who underwent surgery for MTLE. Three clinicians identified the side of MTLE epileptogenesis by visual inspection. The surgical side was set as the epileptogenic side. Two parcellation paradigms and corresponding atlases (Automated Anatomical Labeling and FreeSurfer aparc + aseg) were used to extract the normalized PET uptake of the regions of interest (ROIs). The lateralization index of the MTLE-associated regions in either hemisphere was calculated. The lateralization indices of each ROI were subjected for machine learning to establish the model for classifying the side of MTLE epileptogenesis. RESULT: Ninety-three patients were enrolled for training and validation, and another 11 patients were used for testing. The hit rate of lateralization by visual analysis was 75.3%. Among the 23 patients whose MTLE side of epileptogenesis was incorrectly determined or for whom no conclusion was reached by visual analysis, the Automated Anatomical Labeling and aparc + aseg parcellated the associated ROIs on the correctly lateralized MTLE side in 100.0% and 82.6%. In the testing set, lateralization accuracy was 100% in the 2 paradigms. CONCLUSIONS: Visual analysis of (18)F-FDG PET to lateralize MTLE epileptogenesis showed a lower hit rate compared with machine-assisted interpretation. While reviewing (18)F-FDG PET images of MTLE patients, considering the regions associated with MTLE resulted in better performance than limiting analysis to hippocampal regions.
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spelling pubmed-88841802022-03-03 Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients Shih, Yen-Cheng Lee, Tse-Hao Yu, Hsiang-Yu Chou, Chien-Chen Lee, Cheng-Chia Lin, Po-Tso Peng, Syu-Jyun Clin Nucl Med Original Articles (18)F-FDG PET is widely used in epilepsy surgery. We established a robust quantitative algorithm for the lateralization of epileptogenic foci and examined the value of machine learning of (18)F-FDG PET data in medial temporal lobe epilepsy (MTLE) patients. PATIENTS AND METHODS: We retrospectively reviewed patients who underwent surgery for MTLE. Three clinicians identified the side of MTLE epileptogenesis by visual inspection. The surgical side was set as the epileptogenic side. Two parcellation paradigms and corresponding atlases (Automated Anatomical Labeling and FreeSurfer aparc + aseg) were used to extract the normalized PET uptake of the regions of interest (ROIs). The lateralization index of the MTLE-associated regions in either hemisphere was calculated. The lateralization indices of each ROI were subjected for machine learning to establish the model for classifying the side of MTLE epileptogenesis. RESULT: Ninety-three patients were enrolled for training and validation, and another 11 patients were used for testing. The hit rate of lateralization by visual analysis was 75.3%. Among the 23 patients whose MTLE side of epileptogenesis was incorrectly determined or for whom no conclusion was reached by visual analysis, the Automated Anatomical Labeling and aparc + aseg parcellated the associated ROIs on the correctly lateralized MTLE side in 100.0% and 82.6%. In the testing set, lateralization accuracy was 100% in the 2 paradigms. CONCLUSIONS: Visual analysis of (18)F-FDG PET to lateralize MTLE epileptogenesis showed a lower hit rate compared with machine-assisted interpretation. While reviewing (18)F-FDG PET images of MTLE patients, considering the regions associated with MTLE resulted in better performance than limiting analysis to hippocampal regions. Lippincott Williams & Wilkins 2022-04 2022-01-25 /pmc/articles/PMC8884180/ /pubmed/35085166 http://dx.doi.org/10.1097/RLU.0000000000004072 Text en Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Original Articles
Shih, Yen-Cheng
Lee, Tse-Hao
Yu, Hsiang-Yu
Chou, Chien-Chen
Lee, Cheng-Chia
Lin, Po-Tso
Peng, Syu-Jyun
Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients
title Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients
title_full Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients
title_fullStr Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients
title_full_unstemmed Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients
title_short Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients
title_sort machine learning quantitative analysis of fdg pet images of medial temporal lobe epilepsy patients
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8884180/
https://www.ncbi.nlm.nih.gov/pubmed/35085166
http://dx.doi.org/10.1097/RLU.0000000000004072
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