A simplified analytical dose calculation algorithm accounting for tissue heterogeneity for low-energy brachytherapy sources
Bibliographic record
Abstract
The American Association of Physicists in Medicine Task Group No. 43 (AAPM TG-43) formalism is the standard for seeds brachytherapy dose calculation. But for breast seed implants, Monte Carlo simulations reveal large errors due to tissue heterogeneity. Since TG-43 includes several factors to account for source geometry, anisotropy and strength, we propose an additional correction factor, called the inhomogeneity correction factor (ICF), accounting for tissue heterogeneity for Pd-103 brachytherapy. This correction factor is calculated as a function of the media linear attenuation coefficient and mass energy absorption coefficient, and it is independent of the source internal structure. Ultimately the dose in heterogeneous media can be calculated as a product of dose in water as calculated by TG-43 protocol times the ICF. To validate the ICF methodology, dose absorbed in spherical phantoms with large tissue heterogeneities was compared using the TG-43 formalism corrected for heterogeneity versus Monte Carlo simulations. The agreement between Monte Carlo simulations and the ICF method remained within 5% in soft tissues up to several centimeters from a Pd-103 source. Compared to Monte Carlo, the ICF methods can easily be integrated into a clinical treatment planning system and it does not require the detailed internal structure of the source or the photon phase-space.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".