Compositional Magnetic Resonance Imaging Measures of Cartilage — Endpoints for Clinical Trials of Disease-modifying Osteoarthritis Drugs?
Bibliographic record
Abstract
Because compositional magnetic resonance imaging (MRI) techniques enable detection of biochemical and microstructural changes in the cartilage extracellular matrix before gross morphological changes occur, they may be useful as outcome measures for clinical trials focusing on early and potentially reversible disease stages1. To date, many of these compositional magnetic resonance imaging (MRI) techniques have not been thoroughly validated in human patients with osteoarthritis (OA) and thus are not presently in routine clinical use. Therefore compositional MRI techniques have rarely been applied in clinical trials2, but they have been used with increasing frequency in OA research for “premorphologic” evaluation of cartilage. Techniques comprise relaxometry measurements (T2, T2* and T1rho mapping) including T2* mapping with ultrashort echo-time imaging, sodium imaging, delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), magnetization transfer contrast and glycosaminoglycan (GAG)-specific chemical exchange saturation transfer (gagCEST), diffusion-weighted imaging, and diffusion tensor imaging. Moreover, they seem to have the potential to serve as quantitative, reproducible, noninvasive, and objective endpoints for OA research, particularly in early and preradiographic stages of the disease. Table 1 summarizes available compositional MRI techniques in the context of OA research. Below we describe recent evidence regarding their potential utility. View this table: Table 1. Summary of compositional MRI techniques. Numerous clinical studies using T2 mapping have shown that subjects with knee pain have elevated T2 values3. Other studies have also associated T2 values with risk factors for OA including age, sex, obesity, and physical activity levels4. One study suggested that addition of a T2 mapping sequence to a routine MRI protocol at 3.0 T improved sensitivity in the detection of cartilage lesions in the knee joint, with only a slight reduction in specificity5. A more recent study showed that higher T2 values at baseline predicted disease onset in a cohort of subjects at … Address correspondence to Dr. A. Guermazi, Boston University School of Medicine, 820 Harrison Ave., FGH Building, 3rd Floor, Boston, Massachusetts 02118, USA; E-mail: guermazi{at}bu.edu
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".