Pediatric Soft-Tissue Tumors and Pseudotumors: MR Imaging Features with Pathologic Correlation
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.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".