Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine.
Department of Orthopaedic Surgery, Tohoku University Graduate School of Medicine.
Magn Reson Med Sci. 2020 Aug 3;19(3):176-183. doi: 10.2463/mrms.mp.2019-0026. Epub 2019 Jul 9.
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) e, 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) is the rate of signal increase, and β (s) 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 (T) 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 T 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 T obtained throughout the kinetic curve might be effective to predict the clinical disease activity.
评估经验数学模型(EMM)拟合手部和腕部滑膜炎动态对比增强 MRI(DCE-MRI)数据的可行性,以及 EMM 参数与类风湿关节炎(RA)患者临床疾病活动是否存在显著相关性。
31 例连续的 RA 患者前瞻性地接受了机构审查委员会(IRB)批准的 DCE-MRI 扫描,使用 1.5T 系统的时间分辨率为 20 秒。ROI 放置在信号升高最高的位置,并使用 EMM 分析动力学曲线:ΔS(t)=A(1-e)e,其中ΔS 为相对增强,t 为从基线(ΔS=0)开始首次观察到信号升高时的时间,A 为信号强度上限,α(s)为信号增加速率,β(s)为洗脱期间信号降低速率。计算动力学曲线的初始斜率(Aα)、初始曲线下面积(AUC30)、动力学曲线达到峰值的时间(T)和信号增强比(SER),定义为初始和延迟时间点(分别为 t=60 和 300 秒)之间的信号强度变化。评估有无对比剂的 RA 磁共振成像评分(RAMRIS)。比较这些参数或评分与疾病活动评分(DAS)28-红细胞沉降率(ESR)。
A 与 DAS28-ESR 呈显著相关(r=0.58;P=0.0005)。β、AUC30 和 T 与 DAS28-ESR 也呈显著相关,但相关性稍低(r=0.49;P=0.0051,r=0.50;P=0.0038 和 r=-0.51;P=0.0028),而α、Aα、SER 和 RAMRIS 则没有。
EMM 可拟合手部和腕部滑膜炎的 DCE-MRI 数据。动力学曲线摄取阶段获得的 AUC30 以及整个动力学曲线获得的 A、β 和 T 可能是预测临床疾病活动的有效指标。