SU‐E‐T‐321: Dynamic Modulated Brachytherapy
Bibliographic record
Abstract
PURPOSE: We introduced the concept of Dynamic Modulated Brachytherapy (DMBT) for rectal cancer, last year. To continue our work, we studied different shield designs and investigated the system's tolerance against systematic setup errors. METHODS: As previously presented, our system uses a cylindrical tungsten shield to create a directional radiation profile, which is modulated through translation and rotation using a specialized robotic arm. We used Monte Carlo simulations and an in-house gradient projection optimization algorithm to look at key design parameters. First, we used ideal phantoms to study treatment quality from shield radii ranging 0.5-1.5 cm in 0.25 cm increments. Second, using 36 patient plans, the dependence on radial source position within the shield was studied. We also analyzed the tolerance of the system to systematic setup errors by simulating dose distributions from possible inaccuracies. These included translational and rotational errors as well as possible Ir-192 source misplacements by the afterloading system. RESULTS: Changes in shield radius followed steady patterns. Increasing the radius showed a consistent increase in dose conformality to the tumor volume and better sparing to surrounding tissues. However, there was also a linear increase in total dwell time. There was a trade off to changing the radial position of the source. As the source is brought away from the center, there is a decrease in conformality to the tumor volume, but sparing to healthy tissues was increased and there is a decrease in total dwell time. We found that any potential setup errors for our system, within anticipated margins, had negligible effects on the dose distributions (< 3% deviation). CONCLUSION: Various parameters for shield designs must be balanced for an effective DMBT application. It was found that the system is highly robust against systematic setup uncertainties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".