Insights from DCE-MRI: Blood-Brain Barrier Permeability in the Context of MS Relapses and Methylprednisolone Treatment

Stig Præstekjær Cramer ^1* Nizar B Hamrouni ¹ Helle J Simonsen ¹ Mark B. Vestergaard ¹ Aravinthan Varatharaj ^2,3 Ian Galea ^2,3 Ulrich Lindberg ¹ Jette LB Frederiksen ^4,5 Henrik BW Larsson ^1,5*

• ¹ Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
• ² Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, University Road, Southampton, SO17 1BJ, United Kingdom, Southampton, United Kingdom
• ³ Wessex Neurological Centre, University Hospital Southampton NHS Foundation Trust, Tremona Road, SO16 6YD, Southampton, UK, Southampton, United Kingdom
• ⁴ Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Capital Region of Denmark, Denmark
• ⁵ University of Copenhagen, Department of Clinical Medicine, Copenhagen, Denmark, Copenhagen, Denmark

Detecting multiple sclerosis (MS) relapses remains challenging due to symptom variability and confounding factors, such as flare-ups and infections. Methylprednisolone (MP) is used for severe relapses, decreasing the number of contrast-enhancing lesions on MRI. The influx constant (Ki) derived from dynamic contrast-enhanced MRI (DCE-MRI), a marker of blood-brain barrier (BBB) permeability, has shown promise as a predictor of disease activity in relapsing-remitting MS (RRMS).To investigate the predictive value of Ki in relation to clinical MS relapses and MP treatment, comparing its performance with traditional MRI markers.We studied 20 RRMS subjects admitted for possible relapse, using DCE-MRI on admission to assess Ki in normal-appearing white matter (NAWM) via the Patlak model. Mixed-effects modelling compared the predictive accuracy of Ki, the presence of contrast-enhancing lesions (CEL), evidence of brain lesions (EBL; defined as the presence of CEL or new T2 lesions), and MP treatment on clinical relapse events. Five models were evaluated, including combinations of Ki, CEL, EBL, and MP, to determine the most robust predictors of clinical relapse. Model performance was assessed using accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), with bootstrapped confidence intervals.Superior predictive accuracy was demonstrated with the inclusion of EBL and Ki, alongside MP treatment (AIC = 66.12, p = 0.006), outperforming other models with a classification accuracy of 4 83% (CI: 73-92%), sensitivity of 78% (CI: 60-94%), and specificity of 86% (CI: 74-97%). This model showed the highest combined PPV (78%, CI: 60-94%) and NPV (86%, CI: 74-98%) compared to models with EBL or CEL alone, suggesting an added value of Ki in enhancing predictive reliability.These results support the use of Ki alongside conventional MRI imaging metrics, to improve clinical relapse prediction in RRMS. The findings underscore the utility of Ki as a marker of MS-related neuroinflammation, with potential for integration into relapse monitoring protocols. Further validation in larger cohorts is recommended to confirm the model's generalizability and clinical application.