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Open Access Original research

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

Frederique Archambaud1345, Viviane Bouilleret1345, Lucie Hertz-Pannier1234, Philippe Chaumet-Riffaud5, Sebastian Rodrigo134, Olivier Dulac1246, Francine Chassoux12347 and Catherine Chiron12346*

Author Affiliations

1 Inserm, U663, Service de Neurologie et Métabolisme, Hôpital Necker, 149 rue de Sèvres, Paris, 75015, France

2 University Paris Descartes, Paris, 75005, France

3 CEA, I2BM, Service Hospitalier Frédéric Joliot, Orsay Cedex, 91401, France

4 IFR 49 (Institut Federatif de Recherche), Gif sur Yvette Cedex, 91191, France

5 Assistance Publique-Hôpitaux de Paris (AP-HP), Service de Biophysique et Médecine Nucleaire, Hôpital le Kremlin Bicêtre, Paris, 94275, France

6 AP-HP, Service de Neuropédiatrie, Hôpital Necker, Paris, 75015, France

7 Service de Neurochirurgie, Hôpital Sainte Anne, Paris, 75014, France

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EJNMMI Research 2013, 3:2  doi:10.1186/2191-219X-3-2

Published: 4 January 2013

Abstract

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.

Keywords:
PET; FDG; SPM; Children; Epilepsy