Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
Authors: Daniel C Alexander
Alan J Thompson
Arman Eshaghi
Ferran Prados
Sridar Narayanan
Olga Ciccarelli
Peter A Wijeratne
Douglas L Arnold
Charles R G Guttmann
Alexandra L Young
Declan Chard
Frederik Barkhof
Journal: Nature Communications (2021)
Discipline: Multidisciplinary
Keywords: Multiple sclerosis; phenotypes; pathophysiological boundaries; machine learning; MRI scans
Affiliations: Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK. ., Centre for Medical Image Computing (CMIC), Department of Computer Science, Faculty of Engineering Sciences, University College London, London, UK. ., Centre for Medical Image Computing (CMIC), Department of Computer Science, Faculty of Engineering Sciences, University College London, London, UK., Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK., Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK., e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain., McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada., Center for Neurological Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., VU University Medical Centre, Amsterdam, The Netherlands., Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK., National Institute for Health Research University College London Hospitals, Biomedical Research Centre, London, UK.

Abstract:  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. 

Retracted: No

PubPeer comments:
 The authors are to be commended for an impressive piece of work. I would just note that there may be a mistake in Figure 2. It appears that the data used to make panel C might have been reused to make panel D. ![file] As can be seen, the bars appear identical and the counts in panel D would not (if I understand correctly) be possible for the validation set, where there are only 3068 participants in total. , 
 Thank you for bringing this mistake to my attention. You are spot-on. We have requested a correction. ,