Poster — Thur Eve — 59: Dosimetric evaluation on the variation of PTV coverage due to patient size reduction using the prostate dose‐volume factor in prostate radiotherapy
Bibliographic record
Abstract
We proposed to use the prostate dose‐volume factor (PDVF), derived from the dose‐volume dataset of planning target volume (PTV) in prostate radiotherapy to evaluate treatment plans of prostate volumetric modulated arc therapy (VMAT) and intensity modulated radiotherapy (IMRT). To demonstrate plan evaluation using PDVF, VMAT and 7‐beam IMRT plans were created in three patients with prostate volumes equal to 32, 48.4 and 86.5 cm3. Dose variation of PTV was made by reducing the body contour of the patients with reduced depth equal to 0.5 – 2 cm, mimicking a patient size reduction in the treatment. The Gaussian error function was used to model the cumulative dose‐volume histogram of the PTV, and PDVF was calculated as per the parameters of the error function. PDVF = 1 reflects an ideal PTV coverage (i.e. 100% prescribed dose in 100% target volume). We found that for PDVF ranged 0.98 – 1 in prostate VMAT and IMRT without patient size change, reduced depth led to PDVF decreasing 0.03 ± 4.7 × 10−4 (VMAT) and 0.04 ± 9.7 × 10−3 (IMRT) per cm for the patients. The variation of PTV coverage on the prostate volume due to the reduced depth was less significant in VMAT plans than IMRT. It is concluded that PDVF was successfully used to evaluate the variation of PTV coverage due to the weight loss of patient in prostate VMAT and IMRT. Degradation of PTV coverage in prostate VMAT regarding patient size reduction is less significant than that in 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".