Deep learning detection of acute and sub-acute lesion activity from single-timepoint conventional brain MRI in multiple sclerosis

Abstract

Multiple sclerosis (MS) is a chronic inflammatory disease characterized by demyelinating lesions in the central nervous system. Cross-sectional measurements of acute inflammatory lesion activity are typically obtained by detecting the presence of gadolinium enhancement in lesions, which typically lasts 3-6 weeks. We formulate the novel and clinically relevant task of quantification of recent acute lesion activity from the past 24 weeks (6 months) using single-timepoint conventional brain magnetic resonance imaging (MRI). We develop and compare several deep learning (DL) methods for estimating this brain-level acuteness score and show that a 2D-UNet can accurately predict acute disease activity at the patient-level while outperforming transformers and ensemble approaches. In the context of identifying subjects with acute (less than 6 months-old) lesion activity, our 2D-UNet achieves an area under the receiver-operating curve in the range $80-84\text{\%}$ on independent relapsing-remitting MS cohorts. When used in conjunction with measurements of gadolinium-enhancing lesion activity, our model significantly improves the prognostication of future acute lesion activity (over the next 6 months). This model could thus be leveraged for population recruitment in clinical trials to identify a higher number of patients with acute inflammatory activity than current standard approaches (e.g., gadolinium positivity) with a predictable precision/recall trade-off.