dirk.smeets@icometrix.com

The accurate and reproducible assessment of MRI lesions and brain atrophy in MS is not only clinically important, but also has major health economic benefits thanks to the reduction of medication costs. [1] Sa et al. 2015 [2] Gauthier et al., Journal Neurological Sciences 2009 [3] Giovannoni et al. Brain Health 2015 [4] Hartung et al., Neurology. 2015 26 [5] Rio et al., EJN 2012 [6] Rojas et al., Neurological Research 2014

Since Multiple Sclerosis (MS) is an incurable chronic disease, the focus of therapy is to slow down the relapses and disability progression. Currently, over 10 disease modifying treatments are clinically approved for relapsing remitting MS. However, providing the best treatment for each patient remains a major challenge as over 25-30% of treatments have a suboptimal effect [1,2]. It is shown that personalized and accurate monitoring of MRI lesions and brain atrophy allows the prediction of disability progression, relapses and treatment effect [3]. This work assesses the health economic benefit.

A two scenario decision-tree model is used comparing the situation with and without accurate monitoring of MRI lesions (T2/Gd enhancing and evolution) and brain atrophy. We estimate that yearly 10000 therapy initiations or switches take place in the US, with an average medication cost of $60k [4]. Considering that 26% of these treatments are suboptimal [1] for 3.9 years [5], current costs of failing treatments are about $600M. With an accurate monitoring of MRI lesions and atrophy, the probability to detect treatment failure is 3.1 times higher [6], reducing the average time on suboptimal treatment to 1.3 years. This results in a cost saving of about $400M (67%).

In order to realize such a significant cost saving, efforts are required to introduce accurate and reproducible assessments of MRI lesions and atrophy into the radiological reporting [3]. Today, counting lesions and identifying new (and enlarging) lesions is time consuming and prone to variability (intra-rater and inter-rater), and accurately assessing brain atrophy is not even possible by human eye. Automated quantification by software imposes itself as a solution.