Consequences of dose heterogeneity on the biological efficiency of<sup>103</sup>Pd permanent breast seed implants
Bibliographic record
Abstract
Brachytherapy is associated with highly heterogeneous spatial dose distributions. This heterogeneity is usually ignored when estimating the biological effective dose (BED). In addition, the heterogeneities of the medium including the tissue heterogeneity (TH) and the interseed attenuation (ISA) are also contributing to the heterogeneity of the dose distribution, but they are both ignored in Task Group 43 (TG43)-based protocols. This study investigates the effect of dose heterogeneity, TH and ISA on metrics that are commonly used to quantify biological efficiency in brachytherapy. The special case of 29 breast cancer patients treated with permanent (103)Pd seed implant is considered here. BED is compared to equivalent uniform BED (EUBED) capable of considering the spatial heterogeneity of the dose distribution. The effects of TH and ISA on biological efficiency of treatments are taken into account by comparing TG43 with Monte Carlo (MC) dose calculations for each patient. The effect of clonogenic repopulation is also considered. The analysis is performed for different sets of (α/β, α) ratios of (2, 0.3), (4, 0.27) and (10, 0.3) [Gy, Gy(-1)] covering the whole range of reported α/β values in the literature. BED is sometimes larger and sometimes smaller than EUBED(TG43) indicating that the effect of the dose heterogeneity is not similar among patients. The effect of the dose heterogeneity can be characterized by using the D(99) dose metric. For each set of the radiobiological parameters considered, a D(99) threshold is found over which dose heterogeneity will cause an overestimation of the biological efficiencies while the inverse happens for smaller D(99) values. EUBED(MC) is always larger than EUBED(TG43) indicating that by neglecting TH and ISA in TG43-based dosimetry algorithms, the biological efficiencies may be underestimated by about 10 Gy. Overall, by going from BED to the more accurate EUBED(MC) there is a gain of about 9.6 to 13 Gy on the biological efficiency. The efficiency gain is about 10.8 to 14 Gy when the repopulation is considered. Dose heterogeneity does not have a constant impact on the biological efficiencies and may under- or overestimate the efficacy in different patients. However, the combined effect of neglecting dose heterogeneity, TH and ISA results in underestimation of the biological efficiencies in permanent breast seed implants.
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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 | 0.003 |
| 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) | 0.001 | 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".