Purpose: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with on average a 1-year delay between symptom onset and diagnosis. Studies have demonstrated the value of [18F]-FDG PET as a sensitive diagnostic biomarker, but the discriminatory potential to differentiate ALS from patients with symptoms mimicking ALS has not been investigated. We investigated the combination of brain and spine [18F]-FDG PET-CT for differential diagnosis between ALS and ALS mimics in a real-life clinical diagnostic setting.
Methods: Patients with a suspected diagnosis of ALS (n = 98; 64.8 ± 11 years; 61 M) underwent brain and spine [18F]-FDG PET-CT scans. In 62 patients, ALS diagnosis was confirmed (67.8 ± 10 years; 35 M) after longitudinal follow-up (average 18.1 ± 8.4 months). In 23 patients, another disease was diagnosed (ALS mimics, 60.9 ± 12.9 years; 17 M) and 13 had a variant motor neuron disease, primary lateral sclerosis (PLS; n = 4; 53.6 ± 2.5 years; 2 M) and progressive muscular atrophy (PMA; n = 9; 58.4 ± 7.3 years; 7 M). Spine metabolism was determined after manual and automated segmentation. VOI- and voxel-based comparisons were performed. Moreover, a support vector machine (SVM) approach was applied to investigate the discriminative power of regional brain metabolism, spine metabolism and the combination of both.
Results: Brain metabolism was very similar between ALS mimics and ALS, whereas cervical and thoracic spine metabolism was significantly different (in standardised uptake values; cervical: ALS 2.1 ± 0.5, ALS mimics 1.9 ± 0.4; thoracic: ALS 1.8 ± 0.3, ALS mimics 1.5 ± 0.3). As both brain and spine metabolisms were very similar between ALS mimics and PLS/PMA, groups were pooled for accuracy analyses. Mean discrimination accuracy was 65.4%, 80.0% and 81.5%, using only brain metabolism, using spine metabolism and using both, respectively.
Conclusion: The combination of brain and spine FDG PET-CT with SVM classification is useful as discriminative biomarker between ALS and ALS mimics in a real-life clinical setting.
Keywords: ALS mimics; Amyotrophic lateral sclerosis; Automated spinal cord segmentation; Brain and spinal [18F]-FDG PET-CT; Convolutional neural network; Support vector machine.