Dynamic contrast-enhanced MRI of synovitis in knee osteoarthritis: repeatability, discrimination and sensitivity to change in a prospective experimental study

Objectives Evaluate test-retest repeatability, ability to discriminate between osteoarthritic and healthy participants, and sensitivity to change over 6 months, of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) biomarkers in knee OA. Methods Fourteen individuals aged 40-60 with mild-moderate knee OA and 6 age-matched healthy volunteers (HV) underwent DCE-MRI at 3 T at baseline, 1 month and 6 months. Voxelwise pharmacokinetic modelling of dynamic data was used to calculate DCE-MRI biomarkers including K-trans and IAUC(60). Median DCE-MRI biomarker values were extracted for each participant at each study visit. Synovial segmentation was performed using both manual and semiautomatic methods with calculation of an additional biomarker, the volume of enhancing pannus (VEP). Test-retest repeatability was assessed using intraclass correlation coefficients (ICC). Smallest detectable differences (SDDs) were calculated from test-retest data. Discrimination between OA and HV was assessed via calculation of between-group standardised mean differences (SMD). Responsiveness was assessed via the number of OA participants with changes greater than the SDD at 6 months. Results K-trans demonstrated the best test-retest repeatability (K-trans/IAUC(60)/VEP ICCs 0.90/0.84/0.40, SDDs as % of OA mean 33/71/76%), discrimination between OA and HV (SMDs 0.94/0.54/0.50) and responsiveness (5/1/1 out of 12 OA participants with 6-month change > SDD) when compared to IAUC(60) and VEP. Biomarkers derived from semiautomatic segmentation outperformed those derived from manual segmentation across all domains. Conclusions K-trans demonstrated the best repeatability, discrimination and sensitivity to change suggesting that it is the optimal DCE-MRI biomarker for use in experimental medicine studies.

Automated summary

K-trans demonstrated the best test-retest repeatability, discrimination between OA and HV, and responsiveness compared to IAUC(60) and VEP. Biomarkers derived from semiautomatic segmentation outperformed those derived from manual segmentation in all aspects. These findings suggest that K-trans is the optimal DCE-MRI biomarker for use in experimental medicine studies.