Introduction Quantitative magnetic resonance imaging (MRI) analysis is currently used in multiple sclerosis (MS) clinical trials. Quantitative MRI (QMRI) data derived using formal analysis techniques is not used in routine MS clinical practice and its effect on clinical decision-making is unknown. The study objective is to explore the influence that QMRI data has on clinical decision-making in real-world MS patients.

Methods Clinical MS brain MRI scans (separated by one-year minimum, acquired on the same scanner from the same patient) were evaluated. All patients were on the same disease-modifying therapy (DMT) six months prior to and during the study. QMRI analyses were performed on scan pairs by; imaging analysts using specialised software [semi-automated], and MSmetrix [fully-automated]. Data was presented in two separate reports; local QMRI (semi-automated) and centralised QMRI (MSmetrix). Questionnaires were completed by the same neurologist for each subject using clinical data and standard MRI and QMRI reports.

Results 31 relapsing-MS patients (77.4% female), with baseline age 42.14 [10.70] years, disease duration 7.68 [4.89] years and EDSS score 1.40 (1.36), were evaluated. Injectable, oral and infusion DMTs were administered in 29.0%, 61.3% and 9.7% of patients respectively. According to questionnaire responses, 83.9% were predicted to have stable disease over the next year based on clinical reports alone and 67.7% with the addition of QMRI report data. DMT change would be considered in 16.1% based on clinical reports and 32.3% with QMRI report inclusion. Earlier clinical ±MRI follow up was considered in 51.6% (MRI only 41.9%;both 9.7%) when QMRI reports were reviewed.

Conclusion This preliminary retrospective study indicates that QMRI report data has the potential to influence clinical decision-making in relapsing-MS patients on DMT regarding disease stability assessment, therapy change contemplation, and consideration of earlier follow-up. This work supports a role for formal QMRI analysis and reporting as a clinical-decision support system in MS.