Machine Learning Optimization for Prostate Brachytherapy Treatment Planning
Bibliographic record
Abstract
Prostate Low-Dose-Rate brachytherapy (LDR) is one of the most effective treatments for localized prostate cancer. Machine Learning (ML), the application of statistics to complex computational problem solving, was applied to prostate LDR brachytherapy treatment planning. Planning time, pre-implant dosimetry, and various measures of clinical implant quality for ML plans were compared against plans created by expert brachytherapists. The average planning time to create an ML plan was 0.84 _ 0.57 min compared to over 17.88 _ 8.76 min for an experienced brachytherapists. Dosimetry was not significantly different for ML and expert brachytherapist plans. Clinical implant quality for the ML plans were ranked as nearly equivalent to the brachytherapist treatment plans in all qualitative categories evaluated. The results of this thesis demonstrate that it is possible to generate high quality prostate brachytherapy treatment plans with comparable quality to those of a human expert using a custom ML algorithm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".