Inverse planning anatomy‐based dose optimization for HDR‐brachytherapy of the prostate using fast simulated annealing algorithm and dedicated objective function
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Bibliographic record
Abstract
An anatomy-based dose optimization algorithm is developed to automatically and rapidly produce a highly conformal dose coverage of the target volume while minimizing urethra, bladder, and rectal doses in the delivery of an high dose-rate (HDR) brachytherapy boost for the treatment of prostate cancer. The dwell times are optimized using an inverse planning simulated annealing algorithm (IPSA) governed entirely from the anatomy extracted from a CT and by a dedicated objective function (cost function) reflecting clinical prescription and constraints. With this inverse planning approach, the focus is on the physician's prescription and constraint instead of on the technical limitations. Consequently, the physician's control on the treatment is improved. The capacity of this algorithm to represent the physician's prescription is presented for a clinical prostate case. The computation time (CPU) for IPSA optimization is less than 1 min (41 s for 142915 iterations) for a typical clinical case, allowing fast and practical dose optimization. The achievement of highly conformal dose coverage to the target volume opens the possibility to deliver a higher dose to the prostate without inducing overdosage of urethra and normal tissues surrounding the prostate. Moreover, using the same concept, it will be possible to deliver a boost dose to a delimited tumor volume within the prostate. Finally, this method can be easily extended to other anatomical sites.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it