Poster — Thur Eve — 26: Cinical Implimentation of kV CBCT for Gynecological IMRT
Bibliographic record
Abstract
The aim of this work was to implement CBCT imaging for assessment of CTV coverage for IMRT treatment of gynecological malignancies. A cone beam CT imaging protocol was developed to assist with assessment of organ motion and dose volume analysis. Accurate assessment of CBCT dosimetry using Monte Carlo simulations was performed to allow selection of a CBCT imaging protocol that was deemed to not elevate the risk of second malignancy as well as providing the opportunity to incorporate CBCT doses into the treatment plan. Assessment of CTV coverage was performed by contouring structures on multiple CBCT images for each patient. Dose volume calculations were performed on the CBCT data sets for the CTV segmented into vessel and vaginal regions, bladder and rectum. Measurements of bladder and rectal volumes, intra‐organ distance and marker seed positions are reported. Data was collected for patients treated between fall 2009 and spring 2010 using a seven field sliding window IMRT technique. It is concluded that routine CBCT imaging provides a safe and effective method of assessing the impact of organ motion for patients under going gynae IMRT. The posterior‐superior margin of the vaginal CTV may be impacted by bladder and rectal filling which should be accounted either through appropriate patient preparation protocols or by PTV margin adjustment. The feasibility of using fiducial markers for daily assessment of CTV coverage is the subject of ongoing investigation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.026 | 0.004 |
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".