Pediatric Soft-Tissue Tumors and Pseudo-tumors: MR Imaging Features with Pathologic Correlation
Bibliographic record
Abstract
A wide spectrum of entities may give rise to soft-tissue masses in children, including benign and malignant tumors, pseudotumors, and both neoplastic and nonneoplastic vascular lesions. Because of its excellent tissue contrast, multiplanar capability, and lack of ionizing radiation, magnetic resonance (MR) imaging has become the modality of choice in the evaluation of deep and large soft-tissue masses in children. In the vast majority of cases, however, accurate interpretation of the MR imaging findings requires correlation with the clinical findings. For example, in most posttraumatic and inflammatory pseudotumors, the clinical history is fundamental to establishing the diagnosis. In the evaluation of periarticular cysts, the location of the mass and its relationship to a joint are crucial for diagnosis, whereas in the evaluation of vascular lesions, including hemangiomas and vascular malformations, clinical findings combined with MR imaging findings are needed for accurate diagnosis in most cases. The identification of fat within adipocytic tumors is useful, but tissue biopsy may be required for final diagnosis. Nevertheless, MR imaging is useful in determining the origin and character of pediatric soft-tissue masses, defining their extent and their relationship to adjacent structures, and performing posttherapy follow-up.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".