Modelling the variation in rectal dose due to inter-fraction rectal wall deformation in external beam prostate treatments
Bibliographic record
Abstract
Prostate radiotherapy inevitably deposits radiation dose in the rectal wall, and the dose delivered to prostate is limited by the expected rectal complications. Accurate evaluation of the rectal dose is non-trivial due to a number of factors. One of these is variation of the shape and position of the rectal wall (with respect to the clinical target volume (CTV)), which may differ daily from that taken during planning CT acquisition. This study uses data currently available in the literature on rectal wall motion to provide estimates of mean population rectal wall dose. The rectal wall geometry is characterized by a population mean radius of the rectum as well as inter-patient and inter-fraction standard deviations in rectum radius. The model is used to evaluate the range of inter-fraction and inter-patient rectal dose variations. The simulation of individual patients with full and empty rectum in the planning CT scan showed that large variations in rectal dose (>15 Gy) are possible. Mean calculated dose accounting for treatment and planning uncertainties in the rectal wall surface was calculated as well as the map of planning dose over/underpredictions. It was found that accuracy of planning dose is dependent on the CTV-PTV margin size with larger margins producing more accurate estimates. Over a patient population, the variation in rectal dose is reduced by increasing the number of pre-treatment CT scans.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".