Mathematical model for histogram analysis of dynamic contrast-enhanced MRI: A method to evaluate the drug treatment response in rheumatoid arthritis. Issue 141 (August 2021)

• Record Type: Journal Article
• Authors: Mori, Yu; Mori, Naoko; Izumiyama, Takuya; Inoue, Asami; Takase, Kei; Aizawa, Toshimi
• Is Part Of: European journal of radiology. Issue 141(2021)
• Publication Date: 2021-08
• Subjects: Dynamic contrast-enhanced magnetic resonance imaging -- Mathematical model -- Rheumatoid arthritis -- Synovitis -- Treatment response; AUC area under the curve -- DAS disease activity score -- DCE-MRI dynamic contrast-enhanced magnetic resonance imaging -- ICCs intraclass correlation coefficients -- RA rheumatoid arthritis -- RAMRIS Rheumatoid Arthritis Magnetic Resonance Imaging Score -- ROI region of interest -- SER signal enhancement ratio -- VAS visual analog scale; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109831
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Mathematical model parameters of dynamic MRI were useful for RA monitoring. Signal enhancement ratio after treatment is effective in determining responders. Difference in synovium volume was also effective in determination of responders. Signal enhancement ratio after treatment may contribute without baseline MRI. Abstract: Purpose: To evaluate the effectiveness of a mathematical model for histogram analysis of DCE-MRI in distinguishing responders from non-responders during RA drug treatment. Method: Twenty-three consecutive RA patients with clinically active inflammation prospectively underwent DCE-MRI at baseline and after treatment. Manual segmentation of the enhanced synovium was performed on the last phase of DCE-MRI. The voxel-based contrast enhancement was calculated in each phase to obtain 75th percentile values. Kinetic curves made from the 75th percentile values were fitted to mathematical model as follows, ΔS(t) = A (1 – e −αt ) e -βt, where A is the upper limit of signal intensity (%), α (sec −1 ) is the rate of signal increase, and β (sec −1 ) is the rate of signal decrease during washout. AUC30 was calculated by integration of 30 s. SER was calculated as the signal intensity at the initial time point (t = 60) relative to the delayed time point (t = 300). The volumes of enhanced synovium (sum of the number of voxels) were also calculated. Results: After treatment, α, Aα, AUC30 and SER were significantly lower in the responder group than in the non-responder group ( p = 0.033, 0.024, 0.015, and 0.007). The p value of SER was lowest. Aα, AUC30, and the volume of enhanced synovium had significantly larger changes from baseline to after treatment in the responder group than in the non-responder group ( p = 0.045, 0.017, and 0.008). The volume of enhanced synovium had the lowest p value. Conclusions: SER after treatment and change in the volume of enhanced synovium might be effective for distinguishing responders from non-responders.