Poster — Thur Eve — 33: The Influence of a Modeled Treatment Couch on Dose Distributions During IMRT and RapidArc Treatment Delivery
Bibliographic record
Abstract
Treatment couches have been known to perturb dose delivery in patients. This effect is most pronounced in techniques such as IMRT and RapidArc. Although modern treatment planning systems (TPS) include data for a “default” treatment couch, actual couches are not manufactured identically. Thus, variations in their Hounsfield Unit (HU) values may exist. This study demonstrates a practical and simple method of acquiring reliable HU data for any treatment couch. We also investigate the effects of both the default and modeled treatment couches on absorbed dose. Experimental verifications show that by neglecting to incorporate the treatment couch in the TPS, dose differences of up to 9.5% and 7.3% were present for 4 MV and 10 MV photon beams, respectively. Furthermore, a clinical study based on a cohort of 20 RapidArc and IMRT (brain, pelvis and abdominal) cases is performed. 2D dose distributions show that without the couch in the planning phase, differences ≤ 4.6% and 5.9% for RapidArc and IMRT cases are present for the same cases that the default couch was added to. Additionally, in comparison to the default couch, employing the modeled couch in the calculation process influences dose distributions by ≤ 2.7% and 8% for RapidArc and IMRT cases, respectively. This result was found to be site specific; where an accurate couch proves to be preferable for IMRT brain plans. As such, adding the couch during dose calculation decreases dose calculation errors, and a precisely modeled treatment couch offers higher dose delivery accuracy for brain treatment using IMRT.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".