Pediatric Soft-Tissue Tumors and Pseudotumors: MR Imaging Features with Pathologic Correlation
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
In the final part of this two-part review article on soft-tissue masses in children, the magnetic resonance (MR) imaging features, clinical findings, and pathologic findings in a wide variety of tumors, including those of fibroblastic/myofibroblastic origin, so-called fibrohistiocytic tumors, smooth-muscle tumors, skeletal-muscle tumors, tumors of uncertain differentiation, and lymphoma, are described. Other neoplasms that are not included in the World Health Organization classification of soft-tissue tumors but may be seen clinically as soft-tissue masses, specifically dermatofibrosarcoma protuberans, neurogenic tumors and pilomatricoma, are also included. In contrast to the tumors reviewed in Part 1 of this review, the MR imaging features and clinical findings of the tumors included here are largely nonspecific. However, MR imaging is useful in determining site of tumor origin, extent of disease, and relation of tumor to adjacent anatomic structures, and for follow-up after therapy. In some of these entities, the combination of findings may aid in narrowing the differential diagnosis, such as persistent low signal intensity on T1- and T2-weighted images in some fibroblastic lesions, identification of hemosiderin and a synovial origin in pigmented villonodular synovitis, or the presence of multiple target signs on T2-weighted images in deep plexiform neurofibroma. In a large number of cases, however, tissue biopsy is required for final diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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