The potential role of MR-guided adaptive radiotherapy in pediatric oncology: Results from a SIOPE-COG survey
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Magnetic resonance guided radiotherapy (MRgRT) has been successfully implemented for several routine clinical applications in adult patients. The purpose of this study is to map the potential benefit of MRgRT on toxicity reduction and outcome in pediatric patients treated with curative intent for primary and metastatic sites. MATERIALS AND METHODS: Between May and August 2020, a survey was distributed among SIOPE- and COG-affiliated radiotherapy departments, treating at least 25 pediatrics patients annually and being (candidate) users of a MRgRT system. The survey consisted of a table with 45 rows (clinical scenarios for primary (n = 28) and metastatic (n = 17) tumors) and 7 columns (toxicity reduction, outcome improvement, PTV margin reduction, target volume daily adaptation, online re-planning, intrafraction motion compensation and on-board functional imaging) and the option to answer by 'yes/no' . Afterwards, the Dutch national radiotherapy cohort was used to estimate the percentage of pediatric treatments that may benefit from MRgRT. RESULTS: The survey was completed by 12/17 (71% response rate) institutions meeting the survey inclusion criteria. Responders indicated an 'expected benefit' from MRgRT for toxicity/outcome in 7% (for thoracic lymphomas and abdominal rhabdomyosarcomas)/0% and 18% (for mediastinal lymph nodes, lymph nodes located in the liver/splenic hilum, and liver metastases)/0% of the considered scenarios for the primary and metastatic tumor sites, respectively, and a 'possible benefit' was estimated in 64%/46% and 47%/59% of the scenarios. When translating the survey outcome into a clinical perspective a toxicity/outcome benefit, either expected or possible, was anticipated for 55%/24% of primary sites and 62%/38% of the metastatic sites. CONCLUSION: Although the benefit of MRgRT in pediatric radiation oncology is estimated to be modest, the potential role for reducing toxicity and improving clinical outcomes warrants further investigation. This fits best within the context of prospective studies or registration trials.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| 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".