Comparison of dose calculation algorithms for colorectal cancer brachytherapy treatment with a shielded applicator
Bibliographic record
Abstract
Colorectal cancer patients are treated at our hospital with 192Ir high dose rate (HDR) brachytherapy using an applicator that allows the introduction of a lead or tungsten shielding rod to reduce the dose to healthy tissue. The clinical dose planning calculations are, however, currently performed without taking the shielding into account. To study the dose distributions in shielded cases, three techniques were employed. The first technique was to adapt a shielding algorithm which is part of the Nucletron PLATO HDR treatment planning system. The isodose pattern exhibited unexpected features but was found to be a reasonable approximation. The second technique employed a ray tracing algorithm that assigns a constant dose ratio with/without shielding behind the shielding along a radial line originating from the source. The dose calculation results were similar to the results from the first technique but with improved accuracy. The third and most accurate technique used a dose-matrix-superposition algorithm, based on Monte Carlo calculations. The results from the latter technique showed quantitatively that the dose to healthy tissue is reduced significantly in the presence of shielding. However, it was also found that the dose to the tumor may be affected by the presence of shielding; for about a quarter of the patients treated the volume covered by the 100% isodose lines was reduced by more than 5%, leading to potential tumor cold spots. Use of any of the three shielding algorithms results in improved dose estimates to healthy tissue and the tumor.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".