Quantitative MRI Dixon signal drop and fat fraction for differentiating bone marrow lesions: a two-center prospective analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Bone marrow (BM) lesion differentiation remains challenging, and quantitative magnetic resonance imaging (MRI) may enhance accuracy over conventional methods. We evaluated the diagnostic value and inter-reader reliability of Dixon-based signal drop (%drop) and fat fraction percentage (%fat) as adjuncts to existing protocols. MATERIALS AND METHODS: In this prospective two-center study, 172 patients with BM signal abnormalities underwent standardized 1.5-T MRI protocols, including Dixon sequences. Two musculoskeletal radiologists independently evaluated images and performed quantitative measurements of %drop and %fat. Final diagnoses were established through histopathology (n = 96) or imaging follow-up (n = 76). Diagnostic value was assessed using area under the receiver operating characteristic curve (AUROC), inter-reader reliability using Cohen's κ coefficient. RESULTS: The consensus optimal cutoff was for %drop ≤ 19.8%, yielding 87.2% accuracy, 95.3% sensitivity, and 73.8% specificity, and that for %fat was ≤ 18.3%, achieving 86.6% accuracy, 96.3% sensitivity, and 70.8% specificity. Both metrics showed high diagnostic performance (AUROC 0.824-0.863) and excellent inter-reader reliability (κ > 0.93, p < 0.001). Multivariate analysis identified %drop ≤ 19.8% and %fat ≤ 18.3% as the strongest independent predictors of malignancy, with odds ratio (OR) being 9.38 and 8.85, respectively (p < 0.001). Signal characteristics on Dixon sequences provided additional diagnostic value, with signal voids on fat-only images (OR 7.14) and high signals on water-only images (OR 5.46). CONCLUSION: Quantitative MRI Dixon imaging parameters demonstrated high diagnostic accuracy and excellent inter-reader reliability in differentiating benign and malignant BM lesions, supporting their implementation in clinical practice protocols as a reproducible adjunct to conventional MRI. RELEVANCE STATEMENT: Quantitative Dixon MRI provides reproducible, noninvasive differentiation of bone marrow lesions with high diagnostic accuracy across anatomical sites, enhancing clinical decision-making with standardized thresholds while demonstrating excellent inter-center consistency. KEY POINTS: Quantitative Dixon MRI thresholds of %drop ≤ 19.8% and %fat ≤ 18.3% were established as reliable predictors of malignancy in bone marrow lesions. Dixon metrics demonstrated superior diagnostic accuracy (86.6-87.2%), compared to conventional T1-weighted sequences (79.2%). Excellent inter-reader reliability (κ = 0.895-0.943) supports the reproducibility of quantitative Dixon MRI in clinical practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it