Poster — Thur Eve — 22: Bone heterogeneity in kV x‐ray radiotherapy
Bibliographic record
Abstract
This study evaluated the dosimetric impact of bone heterogeneity on the surface dose and dose prescription, when dose is assumed to be prescribed on a homogeneous medium in kV x-ray radiotherapy. A heterogeneous phantom containing a thin water layer (thickness = 1-5 mm) over a bone (thickness =1 cm) was used to mimic treatment sites of forehead, knee and chest wall. The phantom was irradiated by a 220 kVp photon beam with field size of 5 cm diameter. Percentage depth dose, surface dose and photon energy spectrum with different thicknesses of water were determined using Monte Carlo simulations (the EGSnrc code) with experimental verifications using parallel-plate ionization chamber and radiochormatic film. Our results (treatment cone of 5 cm diameter) showed that the surface dose increased in a range of 2.5-3.7% when the water layer above the bone was increased from 1 to 5 mm. However, the surface dose did not increase linearly with the increase of water thickness, and the maximum increase of surface dose was found at a water thickness of 3 mm. Results of the percentage depth dose showed that the maximum bone dose was about 210% higher than the surface dose in the heterogeneous phantom. It is concluded that in treatment sites having a thin layer of tissue over a bone in kV x-ray radiotherapy, if increased surface dose due to bone heterogeneity is not considered, this will result in an additional dosimetric uncertainty narrowing down the total error margin (5%).
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".