A Monte Carlo study on the effect of seed design on the interseed attenuation in permanent prostate implants
Bibliographic record
Abstract
Standard algorithms for postimplant analysis of transperineal interstitial permanent prostate brachytherapy (TIPPB) are based on AAPM Task Group 43 formalism (TG-43), which makes use of a world entirely made of water. This entails an assignment of the prostate, surrounding organs at risk, as well as all brachytherapy seeds present in a permanent prostate implant to water. Brachytherapy seeds are generally made from high atomic number materials. Because of the simultaneous presence of many brachytherapy seeds in a TIPPB, there is a shielding effect causing an attenuation of energy of the emitted photons generally called the "interseed attenuation" (ISA). This study investigates the impact of seed designs and compositions on the interseed attenuation. For this purpose, six brachytherapy seeds covering a wide variety of seed design and composition were modeled with the GEANT4 Monte Carlo (MC) toolkit. MC has allowed calculation of the contribution of each major component (encapsulation and internal components) of a given seed model to ISA separately. The impact of ISA on real clinical implant configurations was also explored. Two clinical postimplant geometries with different brachytherapy seeds were studied with MC simulations. The change in the clinical parameter D90 was observed. This study shows that Nucletron SelectSeed (similar to the Oncura model 6711), ProstaSeed, and Best Medical model 2335 are the most attenuating designs with 4.8%, 3.9%, and 4.6% of D90 reduction, respectively. The least attenuating seed is a 103Pd seed encapsulated in a polymer shell, the IBt OptiSeed with 1.5%. Finally, based on this systematic study, a new seed design is proposed that is predicted to be the most waterlike brachytherapy seed and thus TG-43 compatible.
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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 | 0.001 |
| 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) | 0.001 | 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".