Sci‐Sat AM(2): Brachy‐08: Monte Carlo calculations of high dose rate brachytherapy treatment plans using CT and cone beam CT images
Bibliographic record
Abstract
The feasibility of using cone beam computed tomography (CBCT) images for Monte Carlo (MC) brachytherapy dose calculations has been investigated. To evaluate the effects of tissue heterogeneities and finite patient dimensions for 192Ir high dose rate treatment, CT-based MC calculations for breast and head and neck cases were first performed using the PTRAN_CT photon transport code. PTRAN_CT is an accelerated MC code specifically designed for patient-specific dose calculations. Muscles and adipose tissues, which are nearly indistinguishable in CBCT images, are found to cause minimal dose perturbations at 192Ir energies compared to water. The proximity of the tumor to the skin, however, will have an observable impact on the dose up to a few percent. Therefore, for CBCT calculations, a reasonable assignment of material and density values to the patient voxel geometry, with a good delineation of the skin and bony structures, will suffice for MC dose calculations. A CBCT-based calculation for an actual treatment plan with the tumor close to the cheek was performed. The results were compared to TG43 calculations to quantify the dose differences in the target and critical structures. Since the dose delivered to the tumor is mostly primary dose, deviations are found mostly in the organs at risk where scatter contribution becomes more significant. This study shows that for HDR brachytherapy applications, CBCT-based MC calculations is a feasible option despite inferior image quality and larger uncertainties in the Hounsfield Units compared to CT images. Research supported by Nucletron BV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.029 | 0.006 |
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".