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Optimizing statistical parametric mapping analysis of (18)F-FDG PET in children

BACKGROUND: Statistical parametric mapping (SPM) procedure is an objective tool to analyze 18F-fluoro-2-deoxy-d-glucose-positron-emission tomography (FDG-PET) images and a useful complement to visual analysis. However, SPM requires a comparison to control data set that cannot be obtained in healthy...

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Autores principales: Archambaud, Frederique, Bouilleret, Viviane, Hertz-Pannier, Lucie, Chaumet-Riffaud, Philippe, Rodrigo, Sebastian, Dulac, Olivier, Chassoux, Francine, Chiron, Catherine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3558387/
https://www.ncbi.nlm.nih.gov/pubmed/23289862
http://dx.doi.org/10.1186/2191-219X-3-2
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author Archambaud, Frederique
Bouilleret, Viviane
Hertz-Pannier, Lucie
Chaumet-Riffaud, Philippe
Rodrigo, Sebastian
Dulac, Olivier
Chassoux, Francine
Chiron, Catherine
author_facet Archambaud, Frederique
Bouilleret, Viviane
Hertz-Pannier, Lucie
Chaumet-Riffaud, Philippe
Rodrigo, Sebastian
Dulac, Olivier
Chassoux, Francine
Chiron, Catherine
author_sort Archambaud, Frederique
collection PubMed
description BACKGROUND: Statistical parametric mapping (SPM) procedure is an objective tool to analyze 18F-fluoro-2-deoxy-d-glucose-positron-emission tomography (FDG-PET) images and a useful complement to visual analysis. However, SPM requires a comparison to control data set that cannot be obtained in healthy children for ethical reasons. Using adults as controls showed some limitations. The purpose of the present study was to generate and validate a group of pseudo-normal children as a control group for FDG-PET studies in pediatrics. METHODS: FDG-PET images of 47 children (mean ± SD age 10.2 ± 3.1 years) with refractory symptomatic (MRI-positive, n = 20) and cryptogenic (MRI-negative, n = 27) focal epilepsy planned for surgery were analyzed using visual and SPM analysis. Performances of SPM analysis were compared using two different control groups: (1) an adult control group consisting of healthy young adults (n = 25, 30.5 ± 5.8 years, adult PET template) and (2) a pediatric pseudo-control group consisting of patients (n = 24, 10.6 ± 3.1 years, children PET template) with refractory focal epilepsy but with negative MRI and with PET considered normal not only on visual analysis but also on SPM. RESULTS: Among the 47 children, visual analysis succeeded detecting at least one hypometabolic area in 87% of the cases (interobserver kappa = 0.81). Regarding SPM analysis, the best compromise between sensitivity and specificity was obtained with a threshold of p less than 0.001 as an extent of more than 40 voxels. There was a significant concordance to detect hypometabolic areas between both SPM analyses [kappa (K) = 0.59; p < 0.005] and between both SPM and visual analyses (K = 0.45; p < 0.005), in symptomatic (K = 0.74; p < 0.005) as in cryptogenic patients (K = 0.26; p < 0.01). The pediatric pseudo-control group dramatically improved specificity (97% vs. 89%; p < 0.0001) by increasing the positive predictive value (86% vs. 65%). Sensitivity remained acceptable although it was not better (79% vs. 87%, p = 0.039). The main impact was to reduce by 41% the number of hypometabolic cortical artifacts detected by SPM, especially in the younger epileptic patients, which is a key point in clinical practice. CONCLUSIONS: This age-matched pseudo-control group is a way to optimize SPM analysis of FDG-PET in children with epilepsy. It might also be considered for other brain pathologies in pediatrics in the future.
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spelling pubmed-35583872013-01-31 Optimizing statistical parametric mapping analysis of (18)F-FDG PET in children Archambaud, Frederique Bouilleret, Viviane Hertz-Pannier, Lucie Chaumet-Riffaud, Philippe Rodrigo, Sebastian Dulac, Olivier Chassoux, Francine Chiron, Catherine EJNMMI Res Original Research BACKGROUND: Statistical parametric mapping (SPM) procedure is an objective tool to analyze 18F-fluoro-2-deoxy-d-glucose-positron-emission tomography (FDG-PET) images and a useful complement to visual analysis. However, SPM requires a comparison to control data set that cannot be obtained in healthy children for ethical reasons. Using adults as controls showed some limitations. The purpose of the present study was to generate and validate a group of pseudo-normal children as a control group for FDG-PET studies in pediatrics. METHODS: FDG-PET images of 47 children (mean ± SD age 10.2 ± 3.1 years) with refractory symptomatic (MRI-positive, n = 20) and cryptogenic (MRI-negative, n = 27) focal epilepsy planned for surgery were analyzed using visual and SPM analysis. Performances of SPM analysis were compared using two different control groups: (1) an adult control group consisting of healthy young adults (n = 25, 30.5 ± 5.8 years, adult PET template) and (2) a pediatric pseudo-control group consisting of patients (n = 24, 10.6 ± 3.1 years, children PET template) with refractory focal epilepsy but with negative MRI and with PET considered normal not only on visual analysis but also on SPM. RESULTS: Among the 47 children, visual analysis succeeded detecting at least one hypometabolic area in 87% of the cases (interobserver kappa = 0.81). Regarding SPM analysis, the best compromise between sensitivity and specificity was obtained with a threshold of p less than 0.001 as an extent of more than 40 voxels. There was a significant concordance to detect hypometabolic areas between both SPM analyses [kappa (K) = 0.59; p < 0.005] and between both SPM and visual analyses (K = 0.45; p < 0.005), in symptomatic (K = 0.74; p < 0.005) as in cryptogenic patients (K = 0.26; p < 0.01). The pediatric pseudo-control group dramatically improved specificity (97% vs. 89%; p < 0.0001) by increasing the positive predictive value (86% vs. 65%). Sensitivity remained acceptable although it was not better (79% vs. 87%, p = 0.039). The main impact was to reduce by 41% the number of hypometabolic cortical artifacts detected by SPM, especially in the younger epileptic patients, which is a key point in clinical practice. CONCLUSIONS: This age-matched pseudo-control group is a way to optimize SPM analysis of FDG-PET in children with epilepsy. It might also be considered for other brain pathologies in pediatrics in the future. Springer 2013-01-04 /pmc/articles/PMC3558387/ /pubmed/23289862 http://dx.doi.org/10.1186/2191-219X-3-2 Text en Copyright ©2013 Archambaud et al.; licensee Springer. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Archambaud, Frederique
Bouilleret, Viviane
Hertz-Pannier, Lucie
Chaumet-Riffaud, Philippe
Rodrigo, Sebastian
Dulac, Olivier
Chassoux, Francine
Chiron, Catherine
Optimizing statistical parametric mapping analysis of (18)F-FDG PET in children
title Optimizing statistical parametric mapping analysis of (18)F-FDG PET in children
title_full Optimizing statistical parametric mapping analysis of (18)F-FDG PET in children
title_fullStr Optimizing statistical parametric mapping analysis of (18)F-FDG PET in children
title_full_unstemmed Optimizing statistical parametric mapping analysis of (18)F-FDG PET in children
title_short Optimizing statistical parametric mapping analysis of (18)F-FDG PET in children
title_sort optimizing statistical parametric mapping analysis of (18)f-fdg pet in children
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3558387/
https://www.ncbi.nlm.nih.gov/pubmed/23289862
http://dx.doi.org/10.1186/2191-219X-3-2
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