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SAT0651 Quantification of dynamic mri examinations in juvenile idiopathic arthritis
  1. N Tzaribachev1,
  2. R Hagoug2,
  3. P Louka2,
  4. J Islam2,
  5. M Hinton2,
  6. O Kubassova2,
  7. M Boesen3
  1. 1Pri - Pediatric Rheumatology Research Institute, Bad Bramstedt, Germany
  2. 2Image Analysis Group, London, United Kingdom
  3. 3Radiology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Bispebjerg, Denmark


Background In chronic inflammatory conditions, the need for a more objective measurement of disease activity has been identified. Imaging biomarkers as outcome measurements based on the automated quantification of dynamic contrast enhanced magnetic resonance images (DCE-MRI) have been studied in adult patients with rheumatoid arthritis (RA)1. In children with juvenile idiopathic arthritis (JIA) similar knowledge is very limited.

Objectives To compare treatment related changes of clinical scores in patients with JIA and automated DCE-MRI quantitative parameters analyzed with a dedicated software Dynamikatm also compared to clinical outcomes of the patients.

Methods In patients with polyarticular JIA with insufficient (≥3 affected joints) response or intolerance to ≥3 months Methotrexate, Etanercept was started. Six Slice Axial DCE-MRI of the metacarpophaleangeal (MCP) 2–5 joints in the clinically most affected hand was performed at 3 time points: baseline (BL), month 3 and 6 of treatment using a 0.2 Tesla Esaote C-Scan. Clinical scores included active joint (AJ) counts. Clinical response was considered a state of ≤3 AJ. DCE-MRI was analyzed using regions of interest (ROI) covering synovium in slices where MCPs 2–5 were visible. Output parameters included dynamic MRI quantification scores (DEMRIQvol) corresponding to the volume of enhancing voxels within the synovial ROIs alone or multiplied with the mean of the maximum enhancement (ME) or the initial rate of enhancement (IRE). Differences in DEMRIQvol scores between visits were analyzed using t-test (p<0.05* = statistically significant, p<0.25** = clinically meaningful). Concordance between clinical and DEMRIQvol scores were described.

Results 18 Caucasian patients (12 girls, median age 12,6 years, median disease duration 1,2 years) were included in the study. Two patients discontinued imaging after BL but continued treatment. In all but 3 of the remaining patients statistically significant and/or clinically meaningful changes were documented for DEMRIQvol ME between visits.

In 4 patients clinical and DEMRIQvol scores showed corresponded changes but these were non-concordant in all others patients.

Based on DEMRIQvol change (irrespective of the clinical scores) the outcome of the patient could be predicted:

  • in 5 patients improvement of DEMRIQvol scores predicted response to treatment (within 2–6 months after last MRI examination)

  • in 4 patients the increase or the persistence of a high DEMRIQvol predicted non-response to treatment

  • in 7 patients increase in DEMRIQvol (after initial decrease) or persistence of a high DEMRIQ vol predicted flare (in 3 of the patients flare occurred after treatment discontinuation)

In all patients subclinical disease could be detected on MRI in clinically unaffected joints.

Conclusions Dynamika based scores appear to be useful for depicting disease activity in JIA and seem to support clinical examination by detecting subclinical inflammation. More over, in the present study DEMRIQvol scores were predictive for the outcome of the patients and were able to “foresee” response to treatment, flare of disease, non-response to treatment in most patients possibly making DEMRIQvol scores supportive in research and clinical decision taking.


  1. Kubassova O et al; Eur J Radiol. 2010 Jun;74(3):e67–72.


Acknowledgements The study was supported by Pfizer.

Disclosure of Interest N. Tzaribachev: None declared, R. Hagoug Employee of: Image Analysis Group, P. Louka Employee of: Image Analysis Group, J. Islam Employee of: Image Analysis Group, M. Hinton Employee of: Image Analysis Group, O. Kubassova Employee of: Image Analysis Group, M. Boesen Shareholder of: Image Analysis Group

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