AUTHOR=Cramer Stig P. , Hamrouni Nizar , Simonsen Helle J. , Vestergaard Mark B. , Varatharaj Aravinthan , Galea Ian , Lindberg Ulrich , Frederiksen Jette Lautrup , Larsson Henrik B. W. 

TITLE=Insights from DCE-MRI: blood–brain barrier permeability in the context of MS relapses and methylprednisolone treatment

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1546236

DOI=10.3389/fnins.2025.1546236

ISSN=1662-453X

ABSTRACT=BackgroundDetecting 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).ObjectivesTo investigate the predictive value of Ki in relation to clinical MS relapses and MP treatment, comparing its performance with traditional MRI markers.MethodsWe 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 modeling 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.ResultsSuperior 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 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.ConclusionThese 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.