While 18 F-FDG PET imaging has demonstrated diagnostic value in people with Amyotrophic Lateral Sclerosis (PwALS) and group-level differences were identified between different disease subtypes (e.g., genetic and clinical variants), refining and validating a machine-learning-based subject-level diagnostic algorithm may improve the general applicability and reliability of 18 F-FDG PET as a diagnostic tool in ALS. In this study, we employed support vector machines (SVM) to further explore the diagnostic potential of 18 F-FDG PET in ALS, alongside its ability to classify between different genetic subtypes or clinical phenotypes. 18 F-FDG PET data of 36 healthy volunteers (HV), 25 people with ALS-mimicking diseases (Mimics), and 167 PwALS, grouped by genetic status (e.g., sporadic (sALS) or carrying a C9orf72 hexanucleotide repeat expansion (ALS C9orf72 RE ) and onset (bulbar or spinal) type, acquired with Biograph ‘TruePoint’ PET/CT scanner, were included in the study (Dataset 1). A second dataset of 183 PwALS and 31 Mimics acquired with Biograph ‘HiRez’ scanner was included as an independent cross-validation set (Dataset 2). PET images were spatially normalised to MNI space to fit linear SVMs with cross-validation. Only age-matched groups were considered to eliminate age-related effects. For Dataset 1, the linear SVM resulted in an average accuracy of 0.86 for the classification of ALS vs. HV, 0.53 for ALS vs. Mimics, 0.83 for ALS C9orf72 RE vs. sALS, and 0.58 for bulbar vs. spinal onset. These findings were corroborated with Dataset2, with an accuracy of up to 0.76 for ALS C9orf72 RE vs. sALS, and 0.59 for bulbar vs. spinal. 18 F-FDG brain PET imaging, combined with SVM and age-matching, can distinguish between ALS C9orf72 RE and sALS with good accuracy, but lacks sufficient discriminative power to differentiate between ALS and Mimics and between different sites of onset.
Tang et al. (Mon,) studied this question.