Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis. Issue 5 (26th May 2020)

• Record Type: Journal Article
• Authors: Mangeat, Gabriel; Ouellette, Russell; Wabartha, Maxime; De Leener, Benjamin; Plattén, Michael; Danylaité Karrenbauer, Virginija; Warntjes, Marcel; Stikov, Nikola; Mainero, Caterina; Cohen‐Adad, Julien; Granberg, Tobias
• Is Part Of: Journal of neuroimaging. Volume 30:Issue 5(2020)
• Page Start: 674
• Page End: 682
• Publication Date: 2020-05-26
• Subjects: Differential diagnosis -- hereditary diffuse leukoencephalopathy with spheroids -- multiple sclerosis -- quantitative MRI; Diagnostic imaging -- Periodicals; Nervous system -- Diseases -- Diagnosis -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Système nerveux -- Maladies -- Diagnostic -- Périodiques; Imagerie médicale; Neuroimagerie; Neurologie; Système nerveux; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.804754
• DOI: 10.1111/jon.12725
• Languages: English
• ISSNs: 1051-2284

Abstract

ABSTRACT: BACKGROUND AND PURPOSE: Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. HDLS is a rare disorder compared to MS, which has led to occurrent misdiagnosis of HDLS as MS. That is problematic since their prognosis and treatment differ. Both disorders are investigated by MRI, which could help to identify patients with high probability of having HDLS, which could guide targeted genetic testing to confirm the HDLS diagnosis. METHODS: Here, we present a machine learning method based on quantitative MRI that can achieve a robust classification of HDLS versus MS. Four HDLS and 14 age‐matched MS patients underwent a quantitative brain MRI protocol (synthetic MRI) at 3 Tesla (T) (scan time <7 minutes). We also performed a repeatability analysis of the predicting features to assess their generalizability by scanning a healthy control with five scan‐rescans at 3T and 1.5T. RESULTS: Our predicting features were measured with an average confidence interval of 1.7% ( P = .01), at 3T and 2.3% ( P = .01) at 1.5T. The model gave a 100% correct classification of the cross‐validation data when using 5‐11 predicting features. When the maximum measurement noise was inserted in the model, the true positive rate of HDLS was 97.2%, while the true positive rate of MS was 99.6%. CONCLUSIONS: This study suggests that computer‐assistance in combination with quantitative MRI may be helpful in aiding the challenging differential diagnosis of HDLS versus MS.