Poster - Thurs Eve-38: CT Imaging in high dose rate brachytherapy for treatment of cervical cancer: Estimation of dose to bowel
Bibliographic record
Abstract
Treatment of cervical cancer often involves intracavitary high dose rate (HDR) brachytherapy. Dose delivered to the bladder and rectum are typically estimated using the ICRU reference points. Dose to the sigmoid and small bowel are not estimated, yet these organs typically exhibit significant complication rates. The objective of this study was to estimate dose to the small bowel and sigmoid using CT images. Bladder and rectum dose estimates obtained from the reference point method were also compared to those obtained from CT images. Eighteen CT scans taken before or after treatment of women treated with HDR using ring and tandem applicators were included in this study. The small bowel, sigmoid, bladder and rectum were contoured and the ICRU points were digitized. The minimum dose to 2cc (D2cc) of each organ was calculated and normalized to % prescribed to Point A. Average D2cc bowel dose was 70%. D2cc bowel dose was significantly higher than both D2cc rectal (27%) and D2cc sigmoid (31%) doses. The average D2cc bladder and rectal doses were 68% (p=NS) and 27% (p<0.001) of prescribed Point A dose. D2cc bowel dose, although significantly higher than rectum and sigmoid, is within an acceptable limit. D2cc bladder and rectum values are either not significantly different from or are significantly lower than ICRU reference values. The results of this study suggest that CT imaging is not necessary for determination of dose to organs at risk. However, image guidance is of value for identifying perforations prior to commencing treatment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".