Impact of interseed attenuation and tissue composition for permanent prostate implants
Bibliographic record
Abstract
The purpose is to evaluate the impact of interseed attenuation and prostate composition for prostate treatment plans with 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 study focuses on treatment plans that were generated for clinical cases. For each plan, four different dose calculation techniques are compared: TG-43 based calculation, superposition MC, full MC with water prostate, and full MC with realistic prostate tissue. The prostate tissue description is from the ICRP report 23 (W. S. Snyer, M. J. Cook, E. S. Nasset, L. R. Karkhausen, G. P. Howells, and I. H. Tipton, "Report of the task group on reference man," Technical Report 23, International Commission on Radiological Protection, 1974). According to the comparisons, the seed density has an influence on interseed attenuation. A plan with a typical low seed density (42 0.6 mCi seeds in a 26 cm3 prostate) suffers a 1.2% drop in the CTV D90 value due to interseed attenuation. A drop of 3.0% is calculated for a higher seed density (75 0.3 mCi seeds, same prostate). The influence of the prostate composition is similar for all seed densities and prostate sizes. The difference between MC simulations in water and MC simulations in prostate tissue is between 4.4% and 4.8% for the D90 parameter. Overall, the effect on D90 is ranging from 5.8% to 12.8% when comparing clinically approved TG-43 and MC simulations in prostate tissue. The impact varies from one patient to the other and depends on the prostate size and the number of seeds. This effect can reach a significant level when reporting correlations between clinical effect and deposited dose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".