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Purpose <p>While <sup>18</sup>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 <sup>18</sup>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 <sup>18</sup>F-FDG PET in ALS, alongside its ability to classify between different genetic subtypes or clinical phenotypes.</p> Methods <p><sup>18</sup>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 <i>C9orf72</i> hexanucleotide repeat expansion (ALS<sup><i>C9orf72</i>RE</sup>) 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.</p> Results <p>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<sup><i>C9orf72</i>RE</sup> 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<sup><i>C9orf72</i>RE</sup> vs. sALS, and 0.59 for bulbar vs. spinal.</p> Conclusion <p><sup>18</sup>F-FDG brain PET imaging, combined with SVM and age-matching, can distinguish between ALS<sup><i>C9orf72</i>RE</sup> and sALS with good accuracy, but lacks sufficient discriminative power to differentiate between ALS and Mimics and between different sites of onset.</p>

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

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Chunmeng Tang,
  • Juliette Foucher,
  • Linn Öijerstedt,
  • Fouke Ombelet,
  • Caroline Ingre,
  • Philip Van Damme,
  • Koen Van Laere,
  • Joke De Vocht,
  • Michel Koole

摘要

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.