From whole gland to hemigland to ultra-focal high-dose-rate prostate brachytherapy: A dosimetric analysis
Bibliographic record
Abstract
PURPOSE: To assess the magnitude of dosimetric reductions of a focal and ultra-focal high-dose-rate (HDR) prostate brachytherapy treatment strategy relative to standard whole gland (WG) treatment. METHODS AND MATERIALS: HDR brachytherapy plans for five patients treated with WG HDR monotherapy were optimized to assess different treatment strategies. Plans were generated to treat the hemigland (HG), one-third gland (1/3G), and one-sixth gland (1/6G), as well as to assess treating the WG with a boost to one of those sub-volumes (WG + HG, WG + 1/3G, WG + 1/6G). Dosimetric parameters analyzed included Target D90%, V100%, V150%, Bladder (B), Rectal (R), Urethral (U) D0.1, 1 and 2cc, Urethral V75%, and the V50% to the contralateral HG. Two-tailed t tests were used for comparison of means, and p-values less than 0.05 were considered statistically significant. RESULTS: Target objectives (D90 > 100% and V100 > 97%) were met in all cases. Significant organs at risk dose reductions were achieved for all approaches compared with WG plans. 1/6G vs WG plans resulted in the greatest reduction in dose with a mean bladder D2cc 24.7 vs 64.8%, rectal D2cc 32.8 vs 65.3%, urethral D1cc 52.1 vs 103.8%, and V75 14.5 vs 75% (p < 0.05 for all comparisons). CONCLUSION: Significant dose reductions to organs at risk can be achieved using HDR focal brachytherapy. The magnitude of the reductions achievable with treating progressively smaller sub-volumes suggests the potential to reduce morbidity, but the clinical impact on morbidity and tumor control remain to be investigated.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".