Po‐Thur Eve General‐31: Dosimetric Impact of Planning a Boost within a Boost in Permanent Seed Implants
Bibliographic record
Abstract
Purpose: To investigate the dosimetric impact on the target volume, boost volume (BV) and urethra of performing limited volume boosts in seed implants. Method and Materials: 35 localized prostate cancer patients with detailed biopsy results were treated with permanent seed implants Personalized plans in which BV corresponding to regions of positive biopsy were generated intra‐operatively based on a simulated annealing inverse planning algorithm. The dose objectives are 144 Gy to the prostate with a 3–4 mm margin, 2/3 (1/3) of the prostate covered by 150% (200%) of the prescription dose (PD), urethra V150 less than 10% and D5 should below 220 Gy. Finally, 100% of BV should receive 150% PD (or 216 Gy). Results: Comparing plans without and with BV show no significant change in the number of seeds, needles, prostate V100 and V200. Prostate V150 (D90) show significant differences increasing from 65% (187Gy) to 70% (191Gy). By forcing the coverage of the BV, the 150% isodose lines (which extend outside the prostate) shift inside of the prostate. Small increase in prostate V150 leads to significant increases of the BV V150 from 79% to 94%. BV do not translate to higher urethra doses. Thus multiple seed reconfigurations inside the prostate are achievable to cover BV. Conclusion: Specific areas within the prostate can be boost to receive 150% of the PD with no impact on the urethra. The plans lead essentially to a rearrangement of the seed positions. Potential radiobiological advantages could be obtained at no cost (procedure or time).
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".