Stray radiation dose and second cancer risk for a pediatric patient receiving craniospinal irradiation with proton beams
Bibliographic record
Abstract
Proton beam radiotherapy unavoidably exposes healthy tissue to stray radiation emanating from the treatment unit and secondary radiation produced within the patient. These exposures provide no known benefit and may increase a patient's risk of developing a radiogenic cancer. The aims of this study were to calculate doses to major organs and tissues and to estimate second cancer risk from stray radiation following craniospinal irradiation (CSI) with proton therapy. This was accomplished using detailed Monte Carlo simulations of a passive-scattering proton treatment unit and a voxelized phantom to represent the patient. Equivalent doses, effective dose and corresponding risk for developing a fatal second cancer were calculated for a 10-year-old boy who received proton therapy. The proton treatment comprised CSI at 30.6 Gy plus a boost of 23.4 Gy to the clinical target volume. The predicted effective dose from stray radiation was 418 mSv, of which 344 mSv was from neutrons originating outside the patient; the remaining 74 mSv was caused by neutrons originating within the patient. This effective dose corresponds to an attributable lifetime risk of a fatal second cancer of 3.4%. The equivalent doses that predominated the effective dose from stray radiation were in the lungs, stomach and colon. These results establish a baseline estimate of the stray radiation dose and corresponding risk for a pediatric patient undergoing proton CSI and support the suitability of passively-scattered proton beams for the treatment of central nervous system tumors in pediatric patients.
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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 | 0.001 |
| 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) | 0.001 | 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".