Poster — Thur Eve — 28: Optimization of a prostate cancer IGRT protocol
Bibliographic record
Abstract
Our image-guided radiation therapy (IGRT) protocol for post-prostatectomy patients involves acquiring a kV cone beam computed tomography (CBCT) dataset at each fraction and shifting the treatment couch to align the surgical clips. This IGRT strategy requires significant resources, and delivers non-negligible dose to normal tissues. The objective of this work is to evaluate this IGRT protocol against two alternative strategies in terms of the dose-volume statistics for target and organ at risk regions. Our method involves deforming the planning CT to the CBCT dataset acquired at each fraction, computing dose on the deformed dataset, and inversely transforming the dose back onto the original planning CT dataset. The treatments of six patients were evaluated assuming three IGRT scenarios: no IGRT, daily IGRT using the clinically employed couch shifts, and alternating day IGRT. The doses delivered to the clinical target volumes are within approximately 3.2, 1.3, and 2.1% of the plan for the non-IGRT, daily, and alternating day IGRT protocols, respectively. Doses to relevant portions of the organs at risk deviate from the plan by up to 10.5, 13.1 and 10.7% for non-IGRT, daily IGRT, and alternating day IGRT protocols, respectively. Some cases do not differ significantly between IGRT and non-IGRT protocols in terms of cumulative DVHs, highlighting the difficult task of correcting prostate bed deformations via the treatment couch translations. In general, the alternating day IGRT protocol was found to result in a clinically insignificant deviation in delivered dose while providing a significant reduction in resource use and patient imaging 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".