The contribution from transit dose for<sup>192</sup>Ir HDR brachytherapy treatments
Bibliographic record
Abstract
Brachytherapy treatment planning systems that use model-based dose calculation algorithms employ a more accurate approach that replaces the TG43-U1 water dose formalism and adopt the TG-186 recommendations regarding composition and geometry of patients and other relevant effects. However, no recommendations were provided on the transit dose due to the source traveling inside the patient. This study describes a methodology to calculate the transit dose using information from the treatment planning system (TPS) and considering the source's instantaneous and average speed for two prostate and two gynecological cases. The trajectory of the (192)Ir HDR source was defined by importing applicator contour points and dwell positions from the TPS. The transit dose distribution was calculated using the maximum speed, the average speed and uniform accelerations obtained from the literature to obtain an approximate continuous source distribution simulated with a Monte Carlo code. The transit component can be negligible or significant depending on the speed profile adopted, which is not clearly reported in the literature. The significance of the transit dose can also be due to the treatment modality; in our study interstitial treatments exhibited the largest effects. Considering the worst case scenario the transit dose can reach 3% of the prescribed dose in a gynecological case with four catheters and up to 11.1% when comparing the average prostate dose for a case with 16 catheters. The transit dose component increases by increasing the number of catheters used for HDR brachytherapy, reducing the total dwell time per catheter or increasing the number of dwell positions with low dwell times. This contribution may become significant (>5%) if it is not corrected appropriately. The transit dose cannot be completely compensated using simple dwell time corrections since it may have a non-uniform distribution. An accurate measurement of the source acceleration and maximum speed should be incorporated in clinical practice or provided by the manufacturer to determine the transit dose component with high accuracy.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".