Poster — Thur Eve — 22: Monte Carlo Inverse Planning and Site‐Specific Integration in HDR Brachytherapy
Bibliographic record
Abstract
Purpose: In HDR brachytherapy, the presences of air gaps, bones and applicator are neglected in the TG43 dose calculation algorithm and therefore no commercial treatment planning system (TPS) can take into account these factors. This approximation could cause major dosimetric divergences. This work demonstrates the combine use of Monte Carlo (MC) dose calculations with inverse planning based on simulated annealing in order to incorporate heterogeneities at the optimization stage in brachytherapy. Materials and Method: Each DK consists in an independent simulation with a full representation of the setup: CT‐based reconstruction of the patient anatomy and applicator model in Geant4. Two different HDR source model were used for the study: Nucletron's 192Ir microSelectron and the axxent electronic source (Xoft inc.), for 50 kVp. This method is tested with two different anatomies. The first one is an interstitial breast treatment and the second case is a rectum applicator boost. A research version of the inverse planning algorithm IPSA was chosen as the optimization method. IPSA reads and analyzes the DKs, replacing the TG43 formula for the cost function evaluation. Results: The impact of the water approximation is found to be energy dependent, with a greater effect for the x‐ray source compared to Iridium. For the breast case, an underdosage of 5.4 % versus 2% on the CTV V100 is found. These deviations are corrected using the MC approach. Conclusion: This novel technique is shown to improve the dosimetry and the planning in HDR brachytherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".