MO‐E‐T‐618‐05: Monte Carlo Study of the Effect of the Tissue Composition On the Dosimetric Data Used for Low Energy Photons
Bibliographic record
Abstract
Purpose: Low energy photon isotopes like Iodine‐125 or Palladium‐103 are widely used for brachytherapy applications, as in the treatment of prostate, eye or very recently breast. The mean energy is approximately 30 keV for Iodine‐125 and 20 keV for Palladium‐103. In the vast majority of brachytherapy treatment planning the patient is still considered as an infinite water phantom. The real compositions of the tissues, the presence of heterogeneities or the real shape of the body are not taken into account. In this work we used Monte Carlo techniques to estimate the impact of those approximations on the calculations of the dose distributions in the patient. Method and Materials: The radial dose functions were calculated for IBt seeds using MCNP4C in different situations and in different materials including different body tissues whose compositions were taken from ICRU441. For these calculations, MCNP4C default cross section library was modified to match EPDL972. Results: The differences between radial dose functions calculated in muscle and in water can be as high as 15% at 5 cm for and 10% at 5 cm for . For breast tissue the comparison with water shows an underestimation of the radial function of up to 50% at 5 cm from a seed. The same calculations in lens tissue show on the contrary a good agreement with the radial dose function calculated in water. Moreover, due to photoelectric absorption, we show that the presence of even very limited amount of high Z elements as heterogeneities or as component of the tissue has an effect on the radial dose function and so on the dose distribution around the seeds. Conclusion: The real composition of the body tissues should not be neglected because of the large influence of high Z elements due to the high photoelectric cross section for low energy photons.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".