Mathematical Model Analysis in Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Predictive Approach for Joint Deformity Progression in Rheumatoid Arthritis.

Abstract

Objectives: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers quantitative insights into synovitis by evaluating vascular changes. However, its potential to predict progressive bone destruction in rheumatoid arthritis (RA) remains unclear. This study aimed to identify DCE-MRI parameters that predict joint deformity progression and establish their clinical relevance.

Methods: This prospective cohort study included 24 RA patients undergoing DCE-MRI at baseline and after treatment initiation. Radiographic progression was assessed using the modified total Sharp score 1 year after the second DCE-MRI. Histogram analysis of the enhanced synovium was performed using a mathematical model to derive parameters, including β (washout rate) and signal enhancement ratio (SER). The differences in mathematical parameters between the groups were statistically evaluated using the Mann-Whitney U test.

Results: Clinical factors, including 28-joint disease activity score (DAS28)-erythrocyte sedimentation rate and visual analog scale scores, were elevated in the progression group (p = 0.001 and p = 0.02, respectively). Patients with progressive bone destruction exhibited significantly higher posttreatment β and SER values (p = 0.023 and p = 0.03, respectively), reflecting delayed-phase curve patterns associated with angiogenesis and increased vascular permeability. No significant differences in the volume of enhanced synovium or Rheumatoid Arthritis Magnetic Resonance Imaging Score synovitis scores were observed. There was no difference between the groups in the change in clinical parameters.

Conclusion: Posttreatment β and SER values derived from DCE-MRI may serve as predictive markers of future bone destruction in RA. These findings highlight the potential of DCE-MRI in guiding therapeutic decisions. Future studies with larger cohorts and automated analysis methods are warranted to validate these results and enhance clinical feasibility.