PET/MRI of metabolic activity in osteoarthritis: A feasibility study
Bibliographic record
Abstract
Purpose To evaluate positron emission tomography / magnetic resonance imaging (PET/MRI) knee imaging to detect and characterize osseous metabolic abnormalities and correlate PET radiotracer uptake with osseous abnormalities and cartilage degeneration observed on MRI. Materials and Methods Both knees of 22 subjects with knee pain or injury were scanned at one timepoint, without gadolinium, on a hybrid 3.0T PET‐MRI system following injection of 18 F‐fluoride or 18 F‐fluorodeoxyglucose (FDG). A musculoskeletal radiologist identified volumes of interest (VOIs) around bone abnormalities on MR images and scored bone marrow lesions (BMLs) and osteophytes using a MOAKS scoring system. Cartilage appearance adjacent to bone abnormalities was graded with MRI‐modified Outerbridge classifications. On PET standardized uptake values (SUV) maps, VOIs with SUV greater than 5 times the SUV in normal‐appearing bone were identified as high‐uptake VOI (VOI High ). Differences in 18 F‐fluoride uptake between bone abnormalities, BML, and osteophyte grades and adjacent cartilage grades on MRI were identified using Mann–Whitney U ‐tests. Results SUV max in all subchondral bone lesions (BML, osteophytes, sclerosis) was significantly higher than that of normal‐appearing bone on MRI ( P < 0.001 for all). Of the 172 high‐uptake regions on 18 F‐fluoride PET, 63 (37%) corresponded to normal‐appearing subchondral bone on MRI. Furthermore, many small grade 1 osteophytes (40 of 82 [49%]), often described as the earliest signs of osteoarthritis (OA), did not show high uptake. Lastly, PET SUV max in subchondral bone adjacent to grade 0 cartilage was significantly lower compared to that of grades 1–2 ( P < 0.05) and grades 3–4 cartilage ( P < 0.001). Conclusion PET/MRI can simultaneously assess multiple early metabolic and morphologic markers of knee OA across multiple tissues in the joint. Our findings suggest that PET/MR may detect metabolic abnormalities in subchondral bone, which appear normal on MRI. Level of Evidence : 2 Technical Efficacy : Stage 1 J. MAGN. RESON. IMAGING 2017;45:1736–1745
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".