Validating an Empirical Mathematical Model for Dynamic Contrast-enhanced MR Imaging of Hand and Wrist Synovitis in Rheumatoid Arthritis: Correlation of Model Parameters with Clinical Disease Activity.
To evaluate the feasibility of an empirical mathematical model (EMM) to fit dynamic contrast-enhanced MRI (DCE-MRI) data of hand and wrist synovitis and whether parameters of EMM are significantly correlated with clinical disease activity in patients with rheumatoid arthritis (RA). Thirty-one consecutive patients with RA prospectively underwent Institutional Review Board (IRB)-approved DCE-MRI scans with temporal resolution of 20 s using a 1.5T system. ROIs were placed where the highest signal increase was observed and the kinetic curves were analyzed using an EMM: ΔS(t) = A(1 - e-α t) e-βt, where ΔS is relative enhancement, t is time from when the signal increase was first observed, starting from baseline (ΔS = 0), A is the upper limit of signal intensity, α (s-1) is the rate of signal increase, and β (s-1) is the rate of signal decrease during washout. The initial slope of the kinetic curve (Aα), the initial area under the curve (AUC30), the time at which the kinetic curve reached its peak (Tpeak) and the signal enhancement ratio (SER) defined as the change in signal intensity between the initial and delayed time points (t = 60 and 300 s, respectively) were calculated. RA magnetic resonance imaging scores (RAMRIS) with and without contrast media were evaluated. These parameters or scores were compared with the Disease Activity Score (DAS) 28-erythrocyte sedimentation rate (ESR). A showed a significant correlation with DAS28-ESR (r = 0.58; P = 0.0005). β, AUC30 and Tpeak were also significantly correlated with DAS28-ESR with a lesser degree (r = 0.49; P = 0.0051, r = 0.50; P = 0.0038 and r = -0.51; P = 0.0028, respectively), whereas α, Aα, SER and RAMRIS were not. EMM could fit the DCE-MRI data of hand and wrist synovitis. AUC30 obtained from the uptake phase of the kinetic curve as well as A, β and Tpeak obtained throughout the kinetic curve might be effective to predict the clinical disease activity.