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Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy

OBJECTIVE: Normal interictal [(18)F]FDG‐PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co‐registered to MRI) can then be used to compare epilepsy patients' predicted and clinical PET. We ass...

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Autores principales: Flaus, Anthime, Jung, Julien, Ostrowky‐Coste, Karine, Rheims, Sylvain, Guénot, Marc, Bouvard, Sandrine, Janier, Marc, Yaakub, Siti N., Lartizien, Carole, Costes, Nicolas, Hammers, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690662/
https://www.ncbi.nlm.nih.gov/pubmed/37602538
http://dx.doi.org/10.1002/epi4.12820
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author Flaus, Anthime
Jung, Julien
Ostrowky‐Coste, Karine
Rheims, Sylvain
Guénot, Marc
Bouvard, Sandrine
Janier, Marc
Yaakub, Siti N.
Lartizien, Carole
Costes, Nicolas
Hammers, Alexander
author_facet Flaus, Anthime
Jung, Julien
Ostrowky‐Coste, Karine
Rheims, Sylvain
Guénot, Marc
Bouvard, Sandrine
Janier, Marc
Yaakub, Siti N.
Lartizien, Carole
Costes, Nicolas
Hammers, Alexander
author_sort Flaus, Anthime
collection PubMed
description OBJECTIVE: Normal interictal [(18)F]FDG‐PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co‐registered to MRI) can then be used to compare epilepsy patients' predicted and clinical PET. We assessed the ability of SIPCOM to identify the Resection Zone (RZ) in patients with drug‐resistant epilepsy (DRE) with reference to visual and statistical parametric mapping (SPM) analysis. METHODS: Patients with complete presurgical work‐up and subsequent SEEG and cortectomy were included. RZ localisation, the reference region, was assigned to one of eighteen anatomical brain regions. SIPCOM was implemented using healthy controls to train a GAN. To compare, the clinical PET coregistered to MRI was visually assessed by two trained readers, and a standard SPM analysis was performed. RESULTS: Twenty patients aged 17‐50 (32 ± 7.8) years were included, 14 (70%) with temporal lobe epilepsy (TLE). Eight (40%) were MRI‐negative. After surgery, 14 patients (70%) had a good outcome (Engel I‐II). RZ localisation rate was 60% with SIPCOM vs 35% using SPM (P = 0.015) and vs 85% using visual analysis (P = 0.54). Results were similar for Engel I‐II patients, the RZ localisation rate was 64% with SIPCOM vs 36% with SPM. With SIPCOM localisation was correct in 67% in MRI‐positive vs 50% in MRI‐negative patients, and 64% in TLE vs 43% in extra‐TLE. The average number of false‐positive clusters was 2.2 ± 1.3 using SIPCOM vs 2.3 ± 3.1 using SPM. All RZs localized with SPM were correctly localized with SIPCOM. In one case, PET and MRI were visually reported as negative, but both SIPCOM and SPM localized the RZ. SIGNIFICANCE: SIPCOM performed better than the reference computer‐assisted method (SPM) for RZ detection in a group of operated DRE patients. SIPCOM's impact on epilepsy management needs to be prospectively validated.
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spelling pubmed-106906622023-12-02 Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy Flaus, Anthime Jung, Julien Ostrowky‐Coste, Karine Rheims, Sylvain Guénot, Marc Bouvard, Sandrine Janier, Marc Yaakub, Siti N. Lartizien, Carole Costes, Nicolas Hammers, Alexander Epilepsia Open Original Articles OBJECTIVE: Normal interictal [(18)F]FDG‐PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co‐registered to MRI) can then be used to compare epilepsy patients' predicted and clinical PET. We assessed the ability of SIPCOM to identify the Resection Zone (RZ) in patients with drug‐resistant epilepsy (DRE) with reference to visual and statistical parametric mapping (SPM) analysis. METHODS: Patients with complete presurgical work‐up and subsequent SEEG and cortectomy were included. RZ localisation, the reference region, was assigned to one of eighteen anatomical brain regions. SIPCOM was implemented using healthy controls to train a GAN. To compare, the clinical PET coregistered to MRI was visually assessed by two trained readers, and a standard SPM analysis was performed. RESULTS: Twenty patients aged 17‐50 (32 ± 7.8) years were included, 14 (70%) with temporal lobe epilepsy (TLE). Eight (40%) were MRI‐negative. After surgery, 14 patients (70%) had a good outcome (Engel I‐II). RZ localisation rate was 60% with SIPCOM vs 35% using SPM (P = 0.015) and vs 85% using visual analysis (P = 0.54). Results were similar for Engel I‐II patients, the RZ localisation rate was 64% with SIPCOM vs 36% with SPM. With SIPCOM localisation was correct in 67% in MRI‐positive vs 50% in MRI‐negative patients, and 64% in TLE vs 43% in extra‐TLE. The average number of false‐positive clusters was 2.2 ± 1.3 using SIPCOM vs 2.3 ± 3.1 using SPM. All RZs localized with SPM were correctly localized with SIPCOM. In one case, PET and MRI were visually reported as negative, but both SIPCOM and SPM localized the RZ. SIGNIFICANCE: SIPCOM performed better than the reference computer‐assisted method (SPM) for RZ detection in a group of operated DRE patients. SIPCOM's impact on epilepsy management needs to be prospectively validated. John Wiley and Sons Inc. 2023-08-29 /pmc/articles/PMC10690662/ /pubmed/37602538 http://dx.doi.org/10.1002/epi4.12820 Text en © 2023 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Flaus, Anthime
Jung, Julien
Ostrowky‐Coste, Karine
Rheims, Sylvain
Guénot, Marc
Bouvard, Sandrine
Janier, Marc
Yaakub, Siti N.
Lartizien, Carole
Costes, Nicolas
Hammers, Alexander
Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy
title Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy
title_full Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy
title_fullStr Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy
title_full_unstemmed Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy
title_short Deep‐learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co‐registered to MRI to identify the epileptogenic zone in focal epilepsy
title_sort deep‐learning predicted pet can be subtracted from the true clinical fluorodeoxyglucose pet co‐registered to mri to identify the epileptogenic zone in focal epilepsy
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690662/
https://www.ncbi.nlm.nih.gov/pubmed/37602538
http://dx.doi.org/10.1002/epi4.12820
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