# Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence

## TL;DR

This paper introduces a convolutional neural network-based method with segmentation confidence to detect lesion load changes in multiple sclerosis, outperforming traditional volume-based measures in identifying disease progression.

## Contribution

It proposes a novel approach that identifies high-confidence lesion changes, enabling better separation of progressive and stable MS cases compared to existing volume-based methods.

## Key findings

- High discrimination (AUC=0.99) in separating progressive from stable cases.
- Method generalizes well, achieving 83% accuracy on external data.
- Volume and count changes are less effective for individual patient assessment.

## Abstract

The detection of new or enlarged white-matter lesions in multiple sclerosis is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate progressive from stable patients, despite this being a pressing clinical use-case. In this paper we show that change in volumetric measurements of lesion load alone is not a good method for performing this separation, even for highly performing segmentation methods. Instead, we propose a method for identifying lesion changes of high certainty, and establish on a dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable timepoints with a very high level of discrimination (AUC = 0.99), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on a second external dataset confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracy of 83% in separating stable and progressive timepoints. Both lesion volume and count have previously been shown to be strong predictors of disease course across a population. However, we demonstrate that for individual patients, changes in these measures are not an adequate means of establishing no evidence of disease activity. Meanwhile, directly detecting tissue which changes, with high confidence, from non-lesion to lesion is a feasible methodology for identifying radiologically active patients.