Template-Based Inverse PlanningSimulated Annealing for CT-Based High-Dose-Rate Brachytherapy of Cervical Cancer: Feasibility Study
Bibliographic record
Abstract
Purpose:To investigate the feasibility of using an inverse planning technique for CT-based ring and tandem high-dose rate brachytherapy of cervical cancer. Methods and Materials:Two patients previously treated with high-dose-rate brachytherapy for cervical cancer were retrospectively identified for this study. Each patient had five intracavitary insertions using CT/MR-compatible tandem and ring applicators. The 6Gy isodose lines from the original clinical plans were converted into a structure set (S6) using MIMvista. Inverse plans were then generated in Oncentra using the inverse planning simulated annealing (IPSA) with S6 as the optimization target. The dose to 0.1cm3, 1cm3, 5cm3 of bladder (DB0.1, DB1, and DB5) and rectum (DR0.1, DR1, DR5) were determined from the dose volume histogram (DVH). Percentage of physician drawn clinical target volume (CTV) and S6 coverage (V100CTV, V100S6) were also recorded. Results:The mean V100%CTV of the original clinical plans and the inverse plans were 88.14% and 87.57%. The mean V100%S6 of the original clinical plans and the inverse plans was 98.68% and 97.00%. The mean dose reduction for DB0.1, DB1 and DB5 were 5.4%, 5.4%, and 4.7%, respectively. The mean dose reduction for DR0.1, DR1 and DR5 were 6.4%, 5.5%, and 4.8%. Conclusions:This work demonstrated the feasibility of this structure-based inverse planning. It can achieve comparable CTV coverage while reducing dose to critical structures. Once template structure set is constructed, this procedure can not only reduce planning time, but improve quality assurance by standardizing the procedure. This approach can be directly extended to other applicator-based brachytherapy procedures.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".