TH‐C‐213AB‐12: On the Importance of Heterogeneous Calculation in Brachytherapy: A Radiobiological Point of View
Bibliographic record
Abstract
Purpose: The purpose of this work is to investigate the importance of heterogeneity corrections for dosimetry in brachytherapy. In particular, we are interested to see how the estimations of the a/b parameter for prostate cancer may change if accurate dose calculations are implemented in brachytherapy. Methods: A Monte Carlo dose engine called ALGEBRA (Based on GEANT4) is used to accurately calculate dose distributions for 30 prostate cancer patients treated with brachytherapy at our institution. The equivalent uniform BED (EUBED) is used to take the high spatial dose heterogeneity of BT into account for estimating the biological efficiency of treatments. For the same level of clinical outcome, the EUBED of BT can be assumed is‐effective with BED of external beam radiotherapy to extract the a/b value for prostate cancer. Results: When the heterogeneities are neglected, a/b value equals 3.1 Gy as reported in the literature. When heterogeneities are considered in BT, an a/b value of 5.4 Gy is predicted for prostate cancer. Conclusions: The importance of a precise dosimetry method in plan evaluation has been recognized but its importance on the radiobiological evaluations is usually neglected. This study shows that by using an accurate dosimetry method in BT, the estimation of a/b can change considerably with regard to the reported value of 3.1 Gy in the literature.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".