SU‐F‐J‐157: Effect of Contouring Uncertainty in Post Implant Dosimetry of Low‐Dose‐Rate Prostate Permanent Seed Brachytherapy
Bibliographic record
Abstract
Purpose: There is strong evidence relating post‐implant dosimetry for low‐dose‐rate (LDR) prostate seed brachytherapy to local control rates. The delineation of the prostate on CT images, however, represents a challenge due to the lack of soft tissue contrast in order to identify the prostate borders. This study aims at quantifying the sensitivity of clinically relevant dosimetric parameters to uncertainty in the contouring of prostate. Methods: CT images, post‐op plans and contours of a cohort of patients (n=43) (low risk=55.8%, intermediate risk=39.5%, high risk=4.7%), who had received prostate seed brachytherapy, were imported into MIM Symphony treatment planning system. The prostate contours in post‐implant CT images were expanded/contracted uniformly for margins of ±1.00 mm, ±2.00 mm, ±3.00 mm, ±4.00 mm and ±5.00 mm. The values for V100 and D90 were extracted from Dose Volume Histograms for each contour and compared. Results: Significant changes were observed in the values of D90 and V100 as well as the number of suboptimal plans for expansion or contraction margins of only few millimeters. Evaluation of coverage based on D90 was found to be less sensitive to expansion errors compared to V100. D90 led to a lower number of implants incorrectly identified with insufficient coverage for expanded contours which increases the accuracy of post‐implant QA using CT images compared to V100. Conclusion: In order to establish a successful post implant QA for LDR prostate seed brachytherapy, it is necessary to identify the low and high thresholds of important dose metrics of the target volume such as D90 and V100. Since these parameters are sensitive to target volume definition, accurate identification of prostate borders would help to improve accuracy and predictive value of the post‐implant QA process. In this respect, use of imaging modalities such as MRI where prostate is well delineated should prove useful.
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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 | 0.002 | 0.006 |
| 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) | 0.001 | 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 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".