SU‐E‐T‐378: Evaluation of An Analytical Model for the Inter‐Seed Attenuation Effect in 103‐Pd Multi‐Seed Implant Brachytherapy
Bibliographic record
Abstract
Purpose: Brachytherapy treatment planning systems based on TG‐43 protocol calculate the dose in water and neglects the heterogeneity effect of seeds in multi‐seed implant brachytherapy. In this research, the accuracy of a novel analytical model that we propose for the inter‐seed attenuation effect (ISA) for 103‐Pd seed model is evaluated. Methods: In the analytical model, dose perturbation due to the ISA effect for each seed in an LDR multi‐seed implant for 103‐Pd is calculated by assuming that the seed of interest is active and the other surrounding seeds are inactive. The cumulative dosimetric effect of all seeds is then summed using the superposition principle. The model is based on pre Monte Carlo (MC) simulated 3D kernels of the dose perturbations caused by the ISA effect. The cumulative ISA effect due to multiple surrounding seeds is obtained by a simple multiplication of the individual ISA effect by each seed, the effect of which is determined by the distance from the seed of interest. This novel algorithm is then compared with full MC water‐based simulations (FMCW). Results: The results show that the dose perturbation model we propose is in excellent agreement with the FMCW values for a case with three seeds separated by 1 cm. The average difference of the model and the FMCW simulations was less than 8%±2%. Conclusion: Using the proposed novel analytical ISA effect model, one could expedite the corrections due to the ISA dose perturbation effects during permanent seed 103‐Pd brachytherapy planning with minimal increase in time since the model is based on multiplications and superposition. This model can be applied, in principle, to any other brachytherapy seeds. Further work is necessary to validate this model on a more complicated geometry as well.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".