Magnetic resonance imaging for adult idiopathic inflammatory myopathies: A scoping review of protocols, grading systems and applications
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) has emerged as a key non-invasive tool for the evaluation of idiopathic inflammatory myopathies (IIM); however, heterogeneity in techniques, protocols, and grading systemics impedes standardization. This scoping review systematically examined the MRI techniques, protocols, and grading systems reported in the adult IIM literature. A systematic search of PubMed, EMBASE, and Cochrane databases was conducted from 2000 to 2024 using keywords related to IIM and MRI. Studies involving adults with IIM who underwent MRI were screened and reviewed for inclusion. Forty-nine studies were included in the analysis, 13 of which evaluated whole-body MRI and 36 evaluated dedicated body-part MRI, collectively reporting data from 2810 IIM patients. A wide range of imaging protocols was observed with variations in scanner type, field strength, sequence combinations, and anatomical coverage. Semi-quantitative visual grading was the most commonly used assessment method (31/49, 63.2 %), with binary scoring in 23/31 and software-assisted or automated techniques in 8/31. Six studies used descriptive analysis alone. Inter-rater agreement was reported in 15 studies, with variable reliability observed for both muscle edema (intraclass correlation coefficient [ICC] range: 0.78-1.00; kappa range: 0.30-1.00) and replacement of skeletal muscle by fat (ICC range: 0.77-0.97; kappa range: 0.54-0.93). Several studies have reported that WB-MRI patterns correlate with clinical measures of disease activity and can discriminate between myopathic diseases and IIM subtypes. In summary, despite the clinical utility of MRI for IIM, significant methodological variability remains. Future research should focus on standardizing protocols and grading systems to enhance the consistency and reliability of MRI assessments for IIM.
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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 | 0.037 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".