Non-Enhancing Tissue on Diabetic Foot Magnetic Resonance Imaging in Relation to Osteomyelitis Investigation: Magnetic Resonance Imaging Performance, Pitfalls and Clinical Considerations
Bibliographic record
Abstract
Background: Geographic non-enhancing zones in diabetic foot magnetic resonance imaging (MRI) were first described in 2002. No previous report has described the impact and clinical significance of geographic non-enhancing tissue seen in the evaluation of diabetic foot MRI. Purpose: To evaluate the prevalence of devascularization areas on contrast-enhanced MRI in diabetic patients suspected of having foot osteomyelitis, the impact on the performance of the MRI assessment, and the possible pitfalls. Methods: A retrospective study was conducted between January 2016 and December 2017 during which 72 CE-MRIs of 1.5 and 3T were reviewed by 2 musculoskeletal radiologists for the presence of non-enhancing tissue areas and for osteomyelitis. A blinded third party collected clinical data including pathology reports, revascularization procedures, and surgical interventions. The prevalence of devascularization was calculated. Results: Among the 72 CE-MRIs (54 men, 18 women; mean age 64), 28 demonstrated non-enhancing areas (39%). All but 6 patients were found to have been correctly diagnosed on imaging (3 false positives, 2 false negatives, and 1 non-diagnostic). A greater discordance was also observed between the radiological and pathological diagnoses in the MRIs which showed non-enhancing tissue. Conclusion: Non-enhancing tissue is found in a non-negligible portion of diabetic foot MRIs and affects its diagnostic performance when looking for osteomyelitis. The recognition of these areas of devascularization may be helpful for the physician in planning the best treatment option for the patient.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".