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Semi-quantitative Assessment Using [18F]FDG Tracer in Patients with Severe Brain Injury
Published on: November 9, 2018
Functional pattern of brain FDG-PET in amyotrophic lateral sclerosis
Marco Pagani1, Adriano Chiò2, Maria Consuelo Valentini2
1From the Institute of Cognitive Sciences and Technologies (M.P., A. Cistaro), CNR, Rome, Italy; Departments of Nuclear Medicine (M.P.) and Hospital Physics (J.O.), Karolinska Hospital, Stockholm, Sweden; ALS Center (A. Chiò, A. Calvo, C.M., D.B.), "Rita Levi Montalcini" Department of Neuroscience, University of Turin, Italy; Neuroscience Institute of Turin (A. Chiò), Italy; Department of Neuroradiology (M.C.V.), CTO Hospital, Turin, Italy; Clinical Neurology Unit (F.N.), Department of Neurosciences, Ophthalmology and Genetics (DINOGMI) and Department of Health Sciences (S.M.), Nuclear Medicine Unit, Department of Internal Medicine, University of Genoa, Italy; Institute of Molecular Bioimaging and Physiology (F.D.), CNR Genoa Unit, Italy; and Positron Emission Tomography Center IRMET S.p.A. (P.F., A. Cistaro), Euromedic Inc., Turin, Italy. marco.pagani@istc.cnr.it.
Objective:
We investigated a large sample of patients with amyotrophic lateral sclerosis (ALS) at rest in order to assess the value of (18)F-2-fluoro-2-deoxy-d-glucose ((18)F-FDG) PET as a biomarker to discriminate patients from controls.
Methods:
A total of 195 patients with ALS and 40 controls underwent brain (18)F-FDG-PET, most within 5 months of diagnosis. Spinal and bulbar subgroups of ALS were also investigated. Twenty-five bilateral cortical and subcortical volumes of interest and cerebellum were taken into account, and (18)F-FDG uptakes were individually normalized by whole-brain values. Group analyses investigated the ALS-related metabolic changes. Discriminant analysis investigating sensitivity and specificity was performed using the 51 volumes of interest as well as age and sex. Metabolic connectivity was explored by voxel-wise interregional correlation analysis.
Results:
Hypometabolism was found in frontal, motor, and occipital cortex and hypermetabolism in midbrain, temporal pole, and hippocampus in patients with ALS compared to controls. A similar metabolic pattern was also found in the 2 subgroups. Discriminant analysis showed a sensitivity of 95% and a specificity of 83% in separating patients from controls. Connectivity analysis found a highly significant positive correlation between midbrain and white matter in corticospinal tracts in patients with ALS.
Conclusions:
(18)F-FDG distribution changes in ALS showed a clear pattern of hypometabolism in frontal and occipital cortex and hypermetabolism in midbrain. The latter might be interpreted as the neurobiological correlate of diffuse subcortical gliosis. Discriminant analysis resulted in high sensitivity and specificity in differentiating patients with ALS from controls. Once validated by diseased-control studies, the present methodology might represent a potentially useful biomarker for ALS diagnosis.
Classificaton Of Evidence:
This study provides Class III evidence that (18)F-FDG-PET accurately distinguishes patients with ALS from normal controls (sensitivity 95.4%, specificity 82.5%).
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