Multicenter validation of [¹⁸F]-FDG PET and support-vector machine discriminant analysis in automatically classifying patients with amyotrophic lateral sclerosis versus controls

Abstract

Objective: ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) single-center studies using support vector machine (SVM) approach to differentiate amyotrophic lateral sclerosis (ALS) from controls have shown high overall accuracy on an individual patient basis using local a priori defined classifiers. The aim of the study was to validate the SVM accuracy on a multicentric level.

Methods: A previously defined Belgian (BE) group of 175 ALS patients (61.9 ± 12.2 years, 120M/55F) and 20 screened healthy controls (62.4 ± 6.4 years, 12M/8F) was used to classify another large dataset from Italy (IT), consisting of 195 patients (63.2 ± 11.6 years, 117M/78F) and 40 controls (62 ± 14.4 years; 29M/11F) free of any neurological and psychiatric disorder who underwent whole-body ¹⁸F-FDG PET-CT for lung cancer without any evidence of paraneoplastic symptoms. ¹⁸F-FDG within-center group comparisons based on statistical parametric mapping (SPM) were performed and SVM classifiers based on the local training sets were applied to differentiate ALS from controls from the other centers.

Results: SPM group analysis showed only minor differences between both ALS groups, indicating pattern consistency. SVM using BE data set as training, classified 183/193 ALS-IT correctly (accuracy of 94.8%). However, 35/40 CON-IT were misclassified as ALS (accuracy 12.5%). Furthermore, using IT data as training, ALS-BE could not be distinguished from CON-BE. Within-center SPM group analysis confirmed prefrontal hypometabolism in CON-IT versus CON-BE, indicating subclinical brain changes in patients undergoing oncological scanning.

Conclusion: This multicenter study confirms that the ¹⁸F-FDG ALS pattern is stable across centers. Furthermore, it highlights the importance of carefully selected controls, as subclinical frontal changes might be present in patients in an oncological setting.

KEYWORDS:

• Amyotrophic lateral sclerosis
• support vector machine
• 18F-FDG
• PET/CT
• multicenter
• diagnosis

Acknowledgements

The authors acknowledge the skilled help of the radiopharmacy, technologist, and medical physics team at UZ Leuven (Marva Bex, Tjibbe de Groot, Kim Serdons, Kwinten Porters, Jef Van Loock, Kristof Baete, Michel Koole, Johan Nuyts, and Jenny Ceccarini).

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

This work was supported by a grant from the Latran Foundation. Koen Van Laere and Philip Van Damme are senior clinical investigators of the Flemish Fund for Scientific Research (FWO-Vlaanderen). Philip Van Damme is supported by the Belgian ALS League. Donatienne Van Weehaeghe is a PhD fellow of FWO and Stefanie Willekens is a postdoctoral fellow of FWO.

Supplementary material available online

Additional information

Funding

This work was supported by a grant from the Latran Foundation. Koen Van Laere and Philip Van Damme are senior clinical investigators of the Flemish Fund for Scientific Research (FWO-Vlaanderen). Philip Van Damme is supported by the Belgian ALS League. Donatienne Van Weehaeghe is a PhD fellow of FWO and Stefanie Willekens is a postdoctoral fellow of FWO.