Dosimetric impact of positional uncertainties and a robust optimization approach for rectal intensity‐modulated brachytherapy
Bibliographic record
Abstract
Abstract Background Intensity‐modulated brachytherapy (IMBT) employs rotating high‐Z shields during treatment to decrease radiation in certain directions and conform the dose distribution to the target volume. Prototypes for dynamic IMBT have been proposed for prostate, cervical, and rectal cancer. Purpose We considered two shielded applicators for IMBT rectal cancer treatment and investigated how rotational uncertainties in the shield angle and translational uncertainties in the source position affect plan evaluation criteria. Methods The effect of rotational errors of , and , and translational errors of 1, 2 and 3 mm on evaluation criteria were investigated for shields with and emission windows. Further, a robust optimization approach based on quadratic penalties that includes scenarios with errors was proposed. The extent to which dosimetric effects of positional errors can be mitigated with this model was evaluated compared to a quadratic penalty model without scenarios with errors. A retrospective rectal cancer data set of ten patients was included in this study. Treatment planning was performed using the Monte Carlo‐based treatment planning system, RapidBrachyMCTPS. Results For the largest investigated rotational error of , the clinical target volume remained, on average, within of the result without error, while the contralateral healthy rectal wall experienced an increase in the mean , , and of , , and for the shield and of 32%, 9%, and 2% for the shield. For translational errors of mm, there were increases in dosimetric indices for both the superior (sup) and inferior (inf) dose spill regions. Specifically, for the shield, the , , and increased by , , and , respectively, for the sup region, and by , , and , respectively, for the inf region. Similar results were obtained with the shield. Overall, the robust and traditional models had similar results. However, the number of active dwell positions obtained with the robust model was larger, and the longest dwell time was shorter. Conclusions We have quantified the effect of rotational shield and translational source errors of various magnitudes on evaluation criteria for rectal IMBT. The robust optimization approach was generally not able to mitigate positional errors. However, it resulted in more homogeneous dwell times, which can be beneficial in conventional high‐dose‐rate brachytherapy to avoid hot spots around specific dwell positions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".