Sci‐AM1 Sat ‐ 06: Improved absorbed dose calculations incorporating internal organ motion
Bibliographic record
Abstract
The goal of radiation therapy is to deliver a highly conformal dose to a prescribed target volume and to spare surrounding healthy tissue as much as possible. Present commercial dose planning systems assume that patient's anatomy is static over the course of treatment. During treatment delivery, however, dosimetric uncertainties arising from patient repositioning and internal organ motion are unavoidable practically. The purpose of this study is to evaluate the effect of prostate motion on the physical dose distribution by model based on the Pinnacle treatment planning system. Prostate motion, within the PTV, was represented by a weighted average of seven individually shifted PTVs As already well known, internal organ motion always leads to blurred contour surfaces. The dose coverage of PTV and critical organs is less as indicated by dose at “edge” of contours and also by decreased DVH particularly at high dose region. The averaged decrease of TCP between the static planning and model is 2.9%, and the rectum is spared if motion is equally weighted and symmetric. The configurations yield a better estimate of the actual dose in the rectal wall with decreasing NTCP. The effects of different shifting weight to TCP and NTCP in L‐R, A‐P and S‐I directions were also quantitatively analyzed. The calculation of the cumulative dose incorporating internal organ motion plays an important role in pursuing adaptive radiation therapy and dose escalation for IMRT with the goal of decreasing the dose delivered to the normal critical structures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".