MRI of atypical lipomatous tumor: does contrast help? A multicenter study
Bibliographic record
Abstract
OBJECTIVE: To compare the diagnostic performance of non-contrast MRI versus MRI with contrast for differentiating atypical lipomatous tumors (ALT) from lipomas. MATERIALS AND METHODS: This multicenter retrospective study included subjects with a histopathologic diagnosis of lipoma or ALT and a preoperative MRI study with contrast. An experienced musculoskeletal radiologist reviewed the images in two sessions, the first session with non-contrast only, and the second session, including postcontrast sequences. In each session, the radiologist assigned a binary classification (lipoma or ALT) and a 5-point diagnostic score to reflect clinical reporting. Pathology reports served as the reference standard. McNemar's test was used to evaluate the statistical significance of the differences in sensitivity and specificity, while intraclass correlation coefficients (ICC) compared ordinal diagnostic scores. RESULTS: Four-hundred and forty-one patients (219 ALT, 222 lipoma) were eligible for analysis. In the first session, 76.1% of the lesions were classified as true negative and 76.3% as true positive. In the second session, 74.8% of lesions were assessed as true negative and 77.2% as true positive. There were no significant differences between the two readings in terms of sensitivity or specificity. The agreement in assigning the exact score between the two sessions, as measured by ICC, was 0.878 (95%CI: 0.855-0.898). CONCLUSION: Our study found no significant difference between the radiological readings of MRIs that used only precontrast sequences were used for the evaluation of lipoma and ALT, and those that included both pre- and postcontrast-enhanced sequences.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".