Introduction
Inflammation of the synovial membrane (synovitis) is common in OA, with MRI-detected synovitis occurring in up to 90% of OA knees [
1,
2]. It can be detected, both histologically and on imaging, from the early stages of the disease [
3]. Strong cross-sectional associations exist between the presence of synovitis and the severity of knee pain [
2]. Longitudinal associations have been demonstrated between the presence and severity of synovitis and both symptomatic and structural OA progression [
4‐
6]. There is therefore a strong rationale for therapeutic targeting of synovitis to provide disease modification, particularly in patients with mild to moderate disease where disease-modifying and regenerative approaches are targeted [
7].
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) aims to characterise the uptake and washout of gadolinium-based contrast agents (GBCA) in tissues of interest, providing biomarkers of tissue perfusion, capillary permeability and blood and interstitial volume. These parameters are known to change in the synovium in OA [
8]. DCE-MRI has been used to assess synovitis in early-phase clinical trials of rheumatoid arthritis and has demonstrated superiority over semiquantitative assessments in this setting [
9,
10]. The promise of DCE-MRI in OA has been illustrated by several studies demonstrating changes in DCE-MRI biomarkers following intra-articular corticosteroid treatment with improved responsiveness compared to alternative semiquantitative and qualitative assessments of synovitis [
11,
12].
DCE-MRI biomarkers are of particular interest in early-phase experimental medicine studies which aim to establish early proof-of-concept evidence of efficacy of novel treatments, streamline the treatment development process and reduce late-stage failure rates. They could improve outcome assessment in studies of synovitis-targeted therapies by quantifying response to treatment and are likely to be more robust than relying on qualitative or semiquantitative assessment. There may also be a role in selecting which patients are suitable for entry into studies of synovitis-targeted treatments.
However, to increase confidence in the utility of DCE-MRI biomarkers in these settings, technical and clinical validation is essential [
13]. This includes an assessment of test-retest repeatability, ability to discriminate between knee OA and normal ageing and expected changes over relevant follow-up periods.
Therefore, the purpose of this study was to evaluate the test-retest repeatability, ability to discriminate between osteoarthritic and healthy participants and sensitivity to change over 6 months, of DCE-MRI biomarkers in knee OA.
Discussion
This study suggests that Ktrans is the optimum of the evaluated DCE-MRI biomarkers for use in experimental medicine studies, with the best test-retest repeatability, best discrimination between OA and HV participants and greatest sensitivity to change as judged by the number of participants showing detectable changes over a 6-month period.
Several previous studies have used DCE-MRI to evaluate synovitis in knee OA, including describing cross-sectional associations with symptoms and longitudinal association with response to treatment [
12,
27]. Novel contributions of the current work include (1) improved synovial segmentation leading to more precise parameter estimates, (2) assessment of test-retest repeatability which is required for the interpretation of change at an individual level, (3) assessment of inter-observer reproducibility and (4) comparison of DCE-MRI biomarker values between OA and healthy knees which is needed to assess discriminative validity and also to inform effect size estimations for interventional studies.
Biomarkers that assess the intensity of synovitis (
Ktrans and IAUC
60) performed better than VEP, which reflects the extent of synovitis, across all assessment domains. This finding agrees with a previous knee OA study which suggested improved sensitivity to change of ‘intensive’ vs ‘extensive’ biomarkers of synovitis [
12]. One possible explanation for the superiority of intensive biomarkers is the fact that synovial tissue may enhance despite not being actively inflamed, for example in areas of fibrosis related to previous inflammation [
3]. The extensive biomarker can therefore be hypothesised to measure both active and inactive disease. However, such areas are likely to demonstrate different kinetic characteristics to areas of active inflammation, allowing intensive biomarkers to more accurately reflect disease activity at the time of the scan. DCE-MRI biomarkers derived from semiautomatic segmentation performed better than those derived from manual segmentation across the majority of assessment domains. Previous studies have demonstrated reduction in time taken for analysis with semiautomatic approaches but with similar repeatability and reproducibility to manual approaches [
28,
29]. One plausible explanation for the demonstrated superiority of our semiautomatic approach is the fact that we used shuffle subtraction prior to our thresholding step, in contrast to approaches which attempt to threshold from the post-contrast images alone.
Interestingly, test-retest repeatability metrics for manual synovial segmentation were better than those for the semiautomatic approach. This probably relates to the fact that the manual segmentation was created to provide a rough mask of the location of the synovium which is then used by the semiautomatic method to identify enhancing voxels within the masked region. It is relatively straightforward for an expert radiologist to provide this initial rough mask as evidenced by the good intra and inter-observer reproducibility of manual segmentation. However, the manual method does not capture the variability in the volume of actual enhancing synovial tissue, in contrast to the semiautomatic method. The volume of enhancing synovial tissue (rather than the approximate region within which it is located) is more likely to undergo biological variation during the test-retest interval. Intra-observer reproducibility was similar for the two methods, but with superior inter-observer reproducibility for semiautomatic segmentation.
The design of our study assumes a natural history of OA with negligible change over one month (repeatability), but with the possibility of disease progression over 6 months. This is a short interval relative to the conventional concept of OA as a slowly progressive condition developing and progressing over years. However, experimental medicine studies are typically of short duration and so to be useful in this setting, an imaging biomarker has to be sensitive enough to detect changes over short intervals. We therefore chose a 6-month interval as a reasonable trade-off between the requirements of experimental medicine studies against the expected relatively slow change in disease.
There was a wide range of 6-month changes in DCE-MRI biomarkers in both positive and negative directions in OA participants. This may reflect the fluctuating nature of synovitis in OA, which is well recognised clinically [
30]. Several participants demonstrated 6
-month changes greater than the SDD (particularly for
Ktrans) suggesting that sensitivity to change is adequate for experimental medicine studies performed over this interval. A possible counter-argument is that this sensitivity to change indicates that the background variability is too high to expect to be able to detect additive effects of therapy. Moreover, more participants demonstrated significant decreases rather than significant increases in
Ktrans, likely related to regression to the mean. However, it should be noted that the majority of participants did not demonstrate significant reductions in DCE-MRI biomarkers and typically had higher values than age-matched controls suggesting that there is potential for improvement in these biomarkers with treatment. Moreover, the group mean 6-month changes in DCE-MRI biomarkers for OA participants was close to 0, after adjustment for baseline values (data not shown). This suggests that the effects of treatment may also be detectable at a group as well as at an individual level.
Our results suggest that DCE-MRI biomarkers are likely to be of use in experimental medicine studies featuring putative anti-inflammatory and immunomodulatory disease-modifying treatments. The data presented can be used to inform sample size calculation for further interventional studies. For example, using the observed standard deviation of 6-month change in
Ktrans in this study (~ 0.015 min
-1), a group-averaged reduction of 50% of the difference between OA and HV mean values (~ 0.01 min
-1) could be detected with 80% power and a type 1 error rate of 5% (one-sided) with a sample size of 24 participants per group, assuming an active treatment vs placebo repeated-measures study design. This is a clinically feasible reduction relative to a previous study of change in
Ktrans following intra-articular steroid administration [
12].
Limitations of this study include the long test-retest interval (1 month) relative to the time over which clinical fluctuations in synovitis occur in OA. Therefore, the measured variability is likely to include contributions from both methodological and biological sources, and true methodological variability is likely to be lower. A second limitation is that the results presented are from a single centre and obtained with meticulous quality control; therefore, extrapolation to multi-centre studies should be done with caution. However, previous work suggests that DCE-MRI biomarkers can be used in such a setting with appropriate training, calibration and quality control [
31]. In particular, the use of a semiautomated pipeline as described in this study for defining the synovial ROI is likely to improve robustness in the multi-centre setting compared with manual methods [
32]. Finally, the number of included participants was low. While this was to some extent limited intentionally to mimic the conditions of an experimental medicine study, it does limit the precision of biomarker performance metric estimates. There is no ‘magic number’ of participants required for a repeatability study [
25]. However, we would contend that the uncertainty in our repeatability estimates is low enough to allow them to be used for sample size calculation and interpretation of change at the individual level in future interventional studies.
In conclusion, this study has assessed the test-retest repeatability, discrimination between OA and ‘normal’ tissue characteristics and sensitivity to change of DCE-MRI biomarkers. Ktrans demonstrates the best performance across these domains and is therefore the most likely to be useful in experimental medicine studies and other future therapeutic trials.
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