Can Experienced Observers Differentiate between Lipoma and Well-Differentiated Liposarcoma Using Only MRI?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Well-differentiated liposarcoma represents a radiographic diagnostic dilemma. To determine the accuracy, interrater reliability, and relationship of stranding, nodularity, and size in the MRI differentiation of lipoma and well-differentiated liposarcoma, MRI scans of 60 patients with large (>5 cm), deep, pathologically proven lipomas or well-differentiated liposarcomas were examined by 10 observers with subspecialty training blinded to diagnosis. Observers indicated whether the amount of stranding, nodularity, and size of each tumor suggested a benign or malignant diagnosis and rendered a diagnosis of lipoma or well-differentiated liposarcoma. The accuracy, reliability, and relationship of stranding, nodularity, and size to diagnosis were calculated for all samples. 69% of reader MRI diagnoses agreed with final pathology diagnosis (95% CI 65-73%). Readers tended to err choosing a diagnosis of liposarcoma, correctly identifying lipomas in 63% of cases (95% CI 58-69%) and liposarcomas in 75% of cases (95% CI 69-80%). Assessment of the relationship of stranding, nodularity, and size to correct diagnosis showed that the presence of each was associated with a decreased likelihood of a lipoma pathological diagnosis (P < 0.01). While the radiographic diagnosis of lipoma or well-differentiated liposarcoma cannot be made with 100% certainty, experienced observers have a 69% chance of rendering a correct diagnosis.
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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) | 0.001 | 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) | 0.001 | 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 it