A machine learning algorithm can use data from brain MRI to identify subtypes of MS, according to a study published in Nature Communications. Researchers applied unsupervised machine learning to brain MRI scans from previously published studies to classify MS subtypes based on pathological features. The training dataset of 6,322 patients with MS was used to define MRI-based subtypes, and an independent validation cohort consisted of 3,068 patients. Based on the earliest abnormalities, the researchers defined MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. The highest risk for confirmed disability progression and the highest relapse rate were seen among people with the lesion-led subtype. However, patients with the lesion-led MS subtype also showed positive treatment response in selected clinical trials. “MS is unpredictable and different for everyone, and we know one of our community’s main concerns is how their condition might develop,” said the MS Society’s Clare Walton, PhD, in a statement. “Having an MRI-based model to help predict future progression and tailor your treatment plan accordingly could be hugely reassuring to those affected. These findings also provide valuable insight into what drives progression in MS, which is crucial to finding new treatments.”
