MRI of Rhabdomyosarcoma and Other Soft-Tissue Sarcomas in Children
Bibliographic record
Abstract
Soft-tissue sarcomas in children comprise a heterogeneous group of entities with variable manifestation depending on the age of the patient and the location of the tumor. MRI is the modality of choice for evaluating musculoskeletal soft-tissue tumors and plays a paramount role in both initial diagnosis and assessment of tumor response during and after treatment. Conventional MRI sequences, such as T1- and T2-weighted imaging, offer morphologic information, which is important for localizing the lesion and describing anatomic relationships but not accurate for determining its malignant or benign nature and may be limited in differentiating tumor response from therapy-related changes. Advanced multiparametric MRI offers further functional information that can help with these tasks by using different imaging sequences and biomarkers. The authors present the role of MRI in rhabdomyosarcoma and other soft-tissue sarcomas in children, emphasizing a multiparametric approach with focus on the utility and potential added value of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI in characterization and staging, determination of pretreatment extent, and evaluation of tumor response and recurrence after treatment. ©RSNA, 2020
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".