Non‐rhabdomyosarcoma soft tissue sarcomas diagnosed in patients at a young age. An overview of clinical, pathological, and molecular findings
Bibliographic record
Abstract
OBJECTIVE: Disease spectrum in pediatric sarcoma differs substantially from adults. We report a cohort of very young children with non-rhabdomyosarcoma soft tissue sarcoma (NRSTS) detailing their molecular features, treatment, and outcome. METHODS: We report features of consecutive children (age <2 years) with NRSTS (2000-2017). Archival pathological material was re-reviewed, with additional molecular techniques applied where indicated. RESULTS: Twenty-nine patients (16 females, 55%) were identified (median age 6 months; range 0-23). Most common diagnoses included infantile fibrosarcoma (IFS, n = 14, 48%), malignant rhabdoid tumor (MRT, n = 4, 14%), and undifferentiated sarcoma (n = 4, 14%). Twenty-seven of 29 (93%) had tumor molecular characterization to confirm diagnosis. Clinical presentation included a swelling/mass (n = 23, 79%). Disease extent was localized (n = 20, 69%), locoregional (n = 6, 21%), or metastatic (n = 3, 10%). Seventeen of 29 (59%) who underwent surgery achieved complete resection (R0). Other treatments included conventional chemotherapy (n = 26, 90%), molecularly targeted therapies (n = 3, 10%), and radiation (n = 5, 17%). At last follow-up (median 3 years; range 0.3-16.4), 23 (79%) were alive, disease-free and six (21%) had died of disease. All patients with IFS were alive and all those with MRT died. A cancer predisposition syndrome (CPS) was confirmed in three of 10 (30%) genetically tested patients. CONCLUSION: We recommend tumor molecular characterization in all young patients including evaluation for CPS to optimize treatment options and prognostication.
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 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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".