Poster — Wed Eve—26: Dose‐Volume Histogram Analyses on the Prostate IMRT Plan for Interfraction Organ Motion Using the Gaussian Error Function
Bibliographic record
Abstract
The Gaussian error function (GEF) model was used to carry out cumulative dose‐volume histogram (cDVH) analysis on prostate IMRT plans with interfraction organ motion. cDVHs for CTVs, shifted in the anterior‐posterior directions based on 7‐beam IMRT plans for three patients (small, medium and large prostate), were calculated and modeled using the Pinnacle3 planning system and GEF. To simulate the interfraction prostate motion, the CTV was shifted 1 cm in the anterior‐posterior directions in 2 mm steps, using the dose distribution for the plan without prostate motion. As parameters in the GEF model, namely, a, b and c, were related to the shape of the cDVH curve, evaluation of cDVHs corresponding to the prostate motion becomes possible. cDVH analysis for the CTV shifting in the anterior‐posterior directions using the GEF model showed that parameters , which were related to the maximum relative volume of the cDVH, changed symmetrically when the prostate was shifted in the anterior‐posterior directions. This change was more significant for larger prostate. For parameters b related to the slope of the cDVH, changed symmetrically from the isocenter, when the CTV was within the PTV. This was different from parameters c ( related to the maximum dose of the cDVH), which did not vary significantly with the prostate motion in the anterior‐posterior directions and prostate volume. Using the patient data, this analysis validates the GEF model, and further verified the clinical application of this mathematical model on treatment plan evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".