Predicting future disease activity and treatment responders for multiple sclerosis patients using a bag-of-lesions brain representation

Abstract

The growth of lesions and the development of new lesions in MRI are markers of new disease activity in Multiple Sclerosis (MS) patients. Successfully predicting future lesion activity could lead to a better understanding of disease worsening, as well as prediction of treatment efficacy. We introduce the first, fully automatic, probabilistic framework for the prediction of future lesion activity in relapsing-remitting MS patients, based only on baseline multi-modal MRI, and use it to successfully identify responders to two different treatments. We develop a new Bag-of-Lesions (BoL) representation for patient images based on a variety of features extracted from lesions. A probabilistic codebook of lesion types is created by clustering features using Gaussian mixture models. Patients are represented as a probabilistic histogram of lesion-types. A Random Forest classifier is trained to automatically predict future MS activity up to two years ahead based on the patient’s baseline BoL representation. The framework is trained and tested on a large, proprietary, multi-centre, multi-modal clinical trial dataset consisting of 1048 patients. Testing based on 50-fold cross validation shows that our framework compares favourably to several other classifiers. Automated identification of responders in two different treated groups of patients leads to sensitivity of 82% and 84% and specificity of 92% and 94% respectively, showing that this is a very promising approach towards personalized treatment for MS patients.