Poster - Thurs Eve-14: Linking IGRT data with dose calculation for prostate IMRT planning
Bibliographic record
Abstract
Internal organ motion was studied for 20 prostate patients who were treated with IGRT using MV EPI with three gold seeds implanted in the prostate. Prostate motion was determined from the gold seed displacement relative to bony anatomy between the EPI and the DRR fraction-to-fraction before any correction was applied. The patients were planned with a tight 2mm PTV margin for seven-beam IMRT with prescribed dose of 82 Gy. Treatment planning incorporating organ motion was done manually by convolving the static dose distribution with patient-specific PDF. A Gaussian PDF is reasonable for modeling geometric uncertainties. In the anterior and superior directions, dose decreased more than 5% on the edge of PTV for 5% of the patients. While in inferior direction the dose decreased more than 5% on the edge of PTV for 15% of the patients. The PTV dose is lower than 95% prescription dose for 10% of the patient incorporating individual IGRT data. While for applying group PDF, the dose satisfied the minimum 95% of PTV dose, so group PDF should not be used for accurate treatment planning evaluation for individual patients. Static dose distribution is insufficient to assess PTV coverage. The inclusion of organ motion on dose distribution is required for close agreement between planned and delivered dose. The Gaussian PDF is patient specific and group PDF should not be used for accurate treatment planning evaluation for individual patients. Patient-specific PDF data should be used for re-planning to assess accuracy of delivered dose.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".