A Deep Learning Approach to Predicting Disease Progression in Multiple Sclerosis Using Magnetic Resonance Imaging.

OBJECTIVES: Magnetic resonance imaging (MRI) is an important tool for diagnosis and monitoring of disease course in multiple sclerosis (MS). However, its prognostic value for predicting disease worsening is still being debated. The aim of this study was to propose a deep learning algorithm to predict disease worsening at 2 years of follow-up on a multicenter cohort of MS patients collected from the Italian Neuroimaging Network Initiative using baseline MRI, and compare it with 2 expert physicians. MATERIALS AND METHODS: For 373 MS patients, baseline T2-weighted and T1-weighted brain MRI scans, as well as baseline and 2-year clinical and cognitive assessments, were collected from the Italian Neuroimaging Network Initiative repository. A deep learning architecture based on convolutional neural networks was implemented to predict: (1) clinical worsening (Expanded Disability Status Scale [EDSS]-based model), (2) cognitive deterioration (Symbol Digit Modalities Test [SDMT]-based model), or (3) both (EDSS + SDMT-based model). The method was tested on an independent data set and compared with the performance of 2 expert physicians. RESULTS: For the test set, the convolutional neural network model showed high predictive accuracy for clinical (83.3%) and cognitive (67.7%) worsening, although the highest accuracy was reached when training the algorithm using both EDSS and SDMT information (85.7%). Artificial intelligence classification performance exceeded that of 2 expert physicians (70% of accuracy for the human raters). CONCLUSIONS: We developed a robust and accurate model for predicting clinical and cognitive worsening of MS patients after 2 years, based on conventional T2-weighted and T1-weighted brain MRI scans obtained at baseline. This algorithm may be valuable for supporting physicians in their clinical practice for the earlier identification of MS patients at risk of disease worsening.

Guidelines on the clinical use for the detection of neutralizing antibodies (NAbs) to IFN beta in multiple sclerosis therapy: report from the Italian Multiple Sclerosis Study group.

Interferon beta (IFNß) was the first specific disease-modifying treatment licensed for relapsing-remitting multiple sclerosis, and is still one of the most commonly prescribed treatments. A strong body of evidence supports the effectiveness of IFNß preparations in reducing the annual relapse rate, magnetic resonance (MRI) disease activity and disease progression. However, the development of binding/neutralizing antibodies (BAbs/NAbs) during treatment negatively affects clinical and MRI outcomes. Therefore, guidelines for the clinical use for the detection of NAbs in MS may result in better treatment of these patients. In October 2012, a panel of Italian neurologists from 17 MS clinics convened in Milan to review and discuss data on NAbs and their clinical relevance in the treatment of MS. In this paper, we report the panel's recommendations for the use of IFNß Nabs detection in the early identification of IFNß non-responsiveness and the management of patients on IFNß treatment in Italy, according to a model of therapeutically appropriate care.