Data Sheet 1_Insights from DCE-MRI: blood–brain barrier permeability in the context of MS relapses and methylprednisolone treatment.pdf
Background<p>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...
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2025
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| Summary: | Background<p>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 (K<sub>i</sub>) 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).</p>Objectives<p>To investigate the predictive value of K<sub>i</sub> in relation to clinical MS relapses and MP treatment, comparing its performance with traditional MRI markers.</p>Methods<p>We studied 20 RRMS subjects admitted for possible relapse, using DCE-MRI on admission to assess K<sub>i</sub> in normal-appearing white matter (NAWM) via the Patlak model. Mixed-effects modeling compared the predictive accuracy of K<sub>i</sub>, 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 K<sub>i</sub>, 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.</p>Results<p>Superior predictive accuracy was demonstrated with the inclusion of EBL and K<sub>i</sub>, 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 K<sub>i</sub> in enhancing predictive reliability.</p>Conclusion<p>These results support the use of K<sub>i</sub> alongside conventional MRI imaging metrics, to improve clinical relapse prediction in RRMS. The findings underscore the utility of K<sub>i</sub> 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.</p> |
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