Support vector machine classification of 18F-FDG PET scans across subtypes of amyotrophic lateral sclerosis.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Dec 22, 2025

Abstract

PURPOSE: While 18F-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 18F-FDG PET as a diagnostic tool in ALS. In this study, we employed support vector machines (SVM) to further explore the diagnostic potential of 18F-FDG PET in ALS, alongside its ability to classify between different genetic subtypes or clinical phenotypes. METHODS: 18F-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 (ALSC9orf72RE) 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. RESULTS: 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 ALSC9orf72RE 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 ALSC9orf72RE vs. sALS, and 0.59 for bulbar vs. spinal. CONCLUSION: 18F-FDG brain PET imaging, combined with SVM and age-matching, can distinguish between ALSC9orf72RE and sALS with good accuracy, but lacks sufficient discriminative power to differentiate between ALS and Mimics and between different sites of onset.

Authors

  • Chunmeng Tang
    Department of Informatics, Technische Universität München, Munich, Germany.
  • Juliette Foucher
    Department of Clinical Neuroscience, Karolinska Institutet, Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
  • Linn Öijerstedt
    Department of Clinical Neuroscience, Karolinska Institutet, Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
  • Fouke Ombelet
    Laboratory of Neurobiology, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven, Neurology department, University Hospitals, Leuven, Belgium.
  • Caroline Ingre
    Department of Clinical Neuroscience, Karolinska Institutet, Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
  • Philip van Damme
    Department of Medical Informatics, Amsterdam Public Health research institute, Academic Medical Center, University of Amsterdam, The Netherlands. Electronic address: [email protected].
  • Koen Van Laere
    a Division of Nuclear Medicine and Department of Imaging and pathology , University Hospitals Leuven and KU Leuven , Leuven , Belgium.
  • Joke De Vocht
    e Department of Neurology , University Hospitals Leuven and Laboratory of Neurobiology, Center for Brain & Disease Research KU Leuven and VIB , Leuven , Belgium.
  • Michel Koole
    Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.

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