A systematic study of imaging uncertainties and their impact on prostate brachytherapy dose evaluation
Bibliographic record
Abstract
In order to calculate the dose distribution delivered by a prostate brachytherapy implant, the seed positions and prostate volume are normally identified on post-implant CT images. We have systematically considered the impact of uncertainties in contouring the prostate, seed localization, and visualization of all the seeds on the calculated dose distributions, dose-volume histograms, and predicted radiobiological outcome. This study was done for a collection of 27 clinical 125I prostate brachytherapy implants, performed at the London Regional Cancer Centre during our early adoption of this technique. For these clinical dose distributions, the median D90 was 76% of the prescription dose, or 110 Gy, and the median V90 was 80%. We calculated the changes in these dosimetric indices (D90 and V90) and radiobiological outcome (SF2 TCP) as a function of contouring uncertainty, seed localization uncertainty, inability to localize all of the seeds, and binary combinations of these three. The results are presented for a range of uncertainties, which allows the possible application of these results to a variety of imaging modalities that have differing spatial resolutions. We found that both contouring uncertainties and seed localization uncertainties had a large impact on the predicted radiobiological outcome, but that seed localization uncertainties of 6 mm had the largest impact on the dosimetric indices. We also found that the variability in both the predicted radiobiological and dosimetric outcome was largest for contouring uncertainties of 4-8 mm. We conclude that accounting for contouring uncertainties is crucial in accurately deducing the DVHs for post-implant prostate brachytherapy, and hence enabling valid correlation with ultimate clinical outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".