SU‐C‐16A‐03: Direction Modulated Brachytherapy for HDR Treatment of Cervical Cancer
Bibliographic record
Abstract
Purpose: To investigate a new Directional Modulated Brachytherapy (DMBT) intra‐uterine tandem using various 192‐Ir after‐loaders. Methods: Dose distributions from the 192‐Ir sources were modulated using a 6.3mm diameter tungsten shield (18.0g/cm3). The source moved along 6 longitudinal grooves, each 1.3mm in diameter, evenly spaced along periphery of the shield, The tungsten rod was enclosqed by 0.5mm thick Delrin (1.41g/cc). Monte Carlo N particle (MCNPX) was used to calculate dose distributions. 51million particles were calculated on 504 cores of a supercomputer. Fifteen different patients originally treated with a traditional tandem‐and‐ovoid applicator, with 5 fractions each, (15 patients X 5 fxs = 75 plans) were re‐planned with the DMBT applicator combined with traditional ovoids, on an in‐house developed HDR brachytherapy planning platform, which used intensity modulated planning capabilities using a constrained gradient optimization algorithm. For all plans the prescription dose was 6 Gy and they were normalized to match the clinical treated V100. Results: Generally, the DMBT plan quality was a remarkable improvement from conventional T&O plans because of the anisotropic dose distribution of DMBT. The largest difference was to the bladder which had a 0.59±0.87 Gy (8.5±28.7%) reduction in dose. This was because of the the horseshoe shape (U‐shape) of the bladder. The dose reduction to rectum and sigmoid were 0.48±0.55 Gy (21.1±27.2%) and 0.10±0.38 Gy (40.6±214.9%), respectively. The D90 to the HRCTV was 6.55±0.96 Gy (conventional T&O) and 6.59±1.06 Gy (DMBT). Conclusion: For image guided adaptive brachytherapy, greater flexibility of radiation intensity is essential and DMBT can be the solution.
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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 | 0.000 | 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) | 0.001 | 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".