Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

• Published in: Nature communications Vol. 12; no. 1; p. 2078
• Main Authors: Eshaghi, Arman, Young, Alexandra L., Wijeratne, Peter A., Prados, Ferran, Arnold, Douglas L., Narayanan, Sridar, Guttmann, Charles R. G., Barkhof, Frederik, Alexander, Daniel C., Thompson, Alan J., Chard, Declan, Ciccarelli, Olga
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.04.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/116/2396; 631/1647/245/1627; 631/378/1689/1666; 692/699/375/1411/1666; Abnormalities; Adult; Algorithms; Clinical trials; Databases as Topic; Disease Progression; Female; Health services; Humanities and Social Sciences; Humans; Learning algorithms; Lesions; Machine learning; Magnetic Resonance Imaging; Male; Medical imaging; Middle Aged; Models, Biological; Multidimensional data; multidisciplinary; Multiple sclerosis; Multiple Sclerosis - diagnosis; Multiple Sclerosis - diagnostic imaging; Neuroimaging; Patients; Phenotypes; Placebos; Randomized Controlled Trials as Topic; Recurrence; Reproducibility of Results; Science; Science (multidisciplinary); Substantia alba; Unsupervised learning; Unsupervised Machine Learning
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-22265-2

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials. Multiple sclerosis is a heterogeneous progressive disease. Here, the authors use an unsupervised machine learning algorithm to determine multiple sclerosis subtypes, progression, and response to potential therapeutic treatments based on neuroimaging data.