Rhabdomyosarcoma in Pediatric Patients Under 2 Years: Clinical Features and Outcome
Bibliographic record
Abstract
BACKGROUND: Rhabdomyosarcoma (RMS) typically responds well to a combination of treatments with favorable prognosis in children 1 to 9 years old. However, infants may fare worse due to receiving less aggressive local therapy for concerns about long-term effects of surgery/radiation. This study investigates the clinical characteristics, treatment approach, and survival outcomes of RMS in children under 2. METHODS: We reviewed retrospectively children younger than 2 years with newly diagnosed RMS treated from January 2002 until December 2022 at King Hussein Cancer Center. Demographics, clinical characteristics, and outcomes were analyzed. Statistical analysis included descriptive statistics and survival analysis using Kaplan-Meier methods. All cases were reviewed in a multidisciplinary clinic comprising experienced radiotherapists and surgeons. RESULTS: We identified 34 cases of RMS in patients younger than 2 years at diagnosis. The median age was 13 months, with 70.6% males. The most common tumor site was bladder/prostate (N=13, 38%), followed by orbit (N=5, 14.7%), the predominant subtype was embryonal (N=30). Risk-stratification categorized 17.6% as low-risk and 79.4% as intermediate-risk. Twenty-five patients had tumors >5 cm, with metastasis in 6 (17.6%). All patients received neo-adjuvant chemotherapy, local control was by radiotherapy only (n=12, 35.3%), combined surgery and radiotherapy (n=11, 32.4%), or surgery alone (n=3, 8.8%). The 5-year event-free and overall survival rates were 55.1% and 57.5%, respectively. Fourteen patients experienced relapse/progression, with local relapse the most common pattern. TNM stage, clinical group, metastasis at diagnosis, and radiotherapy use significantly impacted survival. CONCLUSIONS: Children under 2 years of age with RMS face significant challenges, with high local recurrence rates and suboptimal survival outcomes compared with older pediatric patients. Our findings highlight the need for tailored treatment approaches that balance effective local control with minimizing long-term toxicity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".