title: Prospective Validation of 18F-FDG Brain PET Discriminant Analysis Methods in the Diagnosis of Amyotrophic Lateral Sclerosis.
abstract: An objective biomarker for early identification and accurate differential diagnosis of amyotrophic lateral sclerosis (ALS) is lacking. (18)F-FDG PET brain imaging with advanced statistical analysis may provide a tool to facilitate this. The objective of this work was to validate volume-of-interest (VOI) and voxel-based (using a support vector machine [SVM] approach) (18)F-FDG PET analysis methods to differentiate ALS from controls in an independent prospective large cohort, using a priori-derived classifiers. Furthermore, the prognostic value of (18)F-FDG PET was evaluated.
  
  
  METHODS
  A prospective cohort of patients with a suspected diagnosis of a motor neuron disorder (n = 119; mean age ± SD, 61 ± 12 y; 81 men and 38 women) was recruited. One hundred five patients were diagnosed with ALS (mean age ± SD, 61.0 ± 12 y; 74 men and 31 women) (group 2), 10 patients with primary lateral sclerosis (mean age ± SD, 55.5 ± 12 y; 3 men and 7 women), and 4 patients with progressive muscular atrophy (mean age ± SD, 59.2 ± 5 y; 4 men). The mean disease duration of all patients was 15.0 ± 13.4 mo at diagnosis, with PET conducted 15.2 ± 13.3 mo after the first symptoms. Data were compared with a previously gathered dataset of 20 screened healthy subjects (mean age ± SD, 62.4 ± 6.4 y; 12 men and 8 women) and 70 ALS patients (mean age ± SD, 62.2 ± 12.5 y; 44 men and 26 women) (group 1). Data were spatially normalized and analyzed on a VOI basis (statistical software (using the Hammers atlas) and voxel basis using statistical parametric mapping. Discriminant analysis and SVM were used to classify new cases based on the classifiers derived from group 1.
  
  
  RESULTS
  Compared with controls, ALS patients showed a nearly identical pattern of hypo- and hypermetabolism in groups 1 and 2. VOI-based discriminant analysis resulted in an 88.8% accuracy in predicting the new ALS cases. For the SVM approach, this accuracy was 100%. Brain metabolism between ALS and primary lateral sclerosis patients was nearly identical and not separable on an individual basis. Extensive frontotemporal hypometabolism was predictive for a lower survival using a Kaplan-Meier survival analysis (P < 0.001).
  
  
  CONCLUSIONS
  On the basis of a previously acquired training set, (18)F-FDG PET with advanced discriminant analysis methods is able to accurately distinguish ALS from controls and aids in assessing individual prognosis. Further validation on multicenter datasets and ALS-mimicking disorders is needed to fully assess the general applicability of this approach.
authors.affiliation: Division of Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, University Hospitals Leuven and KU Leuven, Leuven, Belgium.
authors.forename: Donatienne
authors.initials: D
authors.lastname: Van Weehaeghe
authors.affiliation: Division of Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, University Hospitals Leuven and KU Leuven, Leuven, Belgium.
authors.forename: Jenny
authors.initials: J
authors.lastname: Ceccarini
authors.affiliation: Department of Neurology, University Hospitals Leuven, Leuven, Belgium.
authors.forename: Aline
authors.initials: A
authors.lastname: Delva
authors.affiliation: Department of Neurology, University Hospitals Leuven, Leuven, Belgium Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven-University of Leuven, Leuven, Belgium; and.
authors.forename: Wim
authors.initials: W
authors.lastname: Robberecht
authors.affiliation: Department of Neurology, University Hospitals Leuven, Leuven, Belgium Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven-University of Leuven, Leuven, Belgium; and Laboratory of Neurobiology, VIB, Vesalius Research Center, Leuven, Belgium.
authors.forename: Philip
authors.initials: P
authors.lastname: Van Damme
authors.forename: Koen
authors.initials: K
authors.lastname: Van Laere
mesh_terms: D000690:Amyotrophic Lateral Sclerosis / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D011446:Prospective Studies; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
keywords: ALS; PET; discriminant analysis; prognosis; support vector machine