Dynamic gadolinium-enhanced magnetic resonance imaging allows accurate assessment of the synovial inflammatory activity in rheumatoid arthritis knee joints: a comparison with synovial histology
Bibliographic record
Abstract
MB Axelsen*a, M Stoltenbergb, RP Poggenborga, O Kubassovac, M Boesende, H Bliddald, K Hørslev-Petersenf, LG Hansongh & M Østergaardaa Department of Rheumatology, Copenhagen University Hospital at Glostrup, Glostrup, Denmarkb Department of Rheumatology, Copenhagen University Hospital at Køge, Køge, Denmarkc Image Analysis Ltd, Leeds, UKd The Parker Institute, Copenhagen University Hospital at Frederiksberg, Frederiksberg, Denmarke Department of Radiology, Copenhagen University Hospital at Frederiksberg, Frederiksberg, Denmarkf King Christian X Hospital for Rheumatic Diseases, Graasten, Graasten, Denmarkg Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital at Hvidovre, Hvidovre, Denmarkh Department of Electrical Engineering, Technical University of Denmark, Lyngby, DenmarkMette Bjørndal Axelsen, Department of Rheumatology RM, Copenhagen University Hospital at Glostrup, Nordre Ringvej 57, DK-2600 Glostrup, Denmark. E-mail: mbaxelsen@gmail.comObjective: To determine whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluated using semi-automatic image processing software can accurately assess synovial inflammation in rheumatoid arthritis (RA) knee joints.Methods: In 17 RA patients undergoing knee surgery, the average grade of histological synovial inflammation was determined from four biopsies obtained during surgery. A preoperative series of T1-weighted dynamic fast low-angle shot (FLASH) MR images was obtained. Parameters characterizing contrast uptake dynamics, including the initial rate of enhancement (IRE), were generated by the software in three different areas: (I) the entire slice (Whole slice); (II) a manually outlined region of interest (ROI) drawn quickly around the joint, omitting large artefacts such as blood vessels (Quick ROI); and (III) a manually outlined ROI following the synovial capsule of the knee joint (Precise ROI). Intra- and inter-reader agreement was assessed using the intra-class correlation coefficient (ICC).Results: The IRE from the Quick ROI and the Precise ROI revealed high correlations to the grade of histological inflammation (Spearman’s correlation coefficient (rho) = 0.70, p = 0.001 and rho = 0.74, p = 0.001, respectively). Intra- and inter-reader ICCs were very high (0.93–1.00). No Whole slice parameters were correlated to histology.Conclusion: DCE-MRI provides fast and accurate assessment of synovial inflammation in RA patients. Manual outlining of the joint to omit large artefacts is necessary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".