Poster — Thur Eve — 08: Dosimetric Comparison Study of Rotational and Static IMRT Treatment Plans for the Prostate
Bibliographic record
Abstract
It has been reported that for certain clinical applications, Rotational Intensity Modulated Radiation Therapy (R‐IMRT) techniques such as Volumetric Modulated Arc Therapy are capable of improved target dose coverage and shorter delivery time when compared to static, step‐and‐shoot IMRT. Five similar early stage prostate cases were used to generate R‐IMRT and static IMRT plans. The R‐IMRT plans consisted of 72 single‐segmented 6MV beams, equally spaced with beam angle separations of 5 degrees. The static IMRT plans employed 7 multi‐segmented 6 MV beams. Both types of plans were optimized with the direct machine parameter optimization algorithm using the same set of optimization objectives. Dose volume histograms were obtained for both types of plans and were comparatively evaluated based on target coverage and dose to organs at risk. For 3 out of 5 cases, target coverage was found to be better with R‐IMRT: standard deviations were consistently lower, and V95 values were equal or better. Critical structure sparing was better for static IMRT with mean dose, V50 and V75 values for the rectum and bladder consistently lower for the majority of the 5 cases. In cases where target coverage was better for static IMRT, the critical structure sparing was better for R‐IMRT. Treatment time was approximately 3 times as long for R‐IMRT due to the lack of dynamic MLC's on the Primus linac. It was found that R‐IMRT can provide improved target coverage in certain applications when compared to static IMRT, however critical structures may receive a slightly higher dose with this technique.
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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) | 0.006 | 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".