  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.
