Sci‐YIS Fri ‐ 04: Clinical impact of seed density and prostate elemental composition on permanent seed implant dosimetry
Bibliographic record
Abstract
The purpose is to evaluate the impact of inter‐seed attenuation and prostate elemental composition in clinical prostate treatment plans with 6711 125I permanent seed implants using the Monte Carlo (MC) method. The effect of seed density (number of seeds per prostate unit volume) is specifically investigated. The MC toolkit Geant4 is used to perform the simulations. The study focuses on treatment plans that were generated for clinical cases. For each plan, four different dose calculation techniques are compared: TG43‐based calculation, superposition MC (SMC), full MC with water prostate (MCW), and full MC with realistic prostate tissue (MCP). The SMC method is a technique for which a shifted one‐source MC distribution is added to the total dose distribution for each source position. It was found that seed density has a definite influence on inter‐seed attenuation. A typical low seed density (42 0.6 mCi seeds in a 26 cc prostate) yields a 0.7% drop in the CTV D90 value when comparing SMC to MCW while a drop of 2.5% is calculated for a higher seed density (75 0.3 mCi seeds, same prostate). The influence of the prostate composition is similar for all plans. When comparing MCW to MCP, the difference in total dose deposited in the CTV is 2.9 +/− 0.2%, while it is 3.6 +/− 0.3% for the D90 parameter. Overall, the effect on D90 is ranging from 4.3% to 9.3% when comparing TG43 to MCP. This effect is clinically significant.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".