SU‐GG‐T‐517: Dose Calculation Accuracy of a Commercial Treatment Planning System for Phantom Geometries with Varied Lung Densites
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate the dose calculation accuracy of a commercial treatment planning system for various water‐lung phantom geometries; specifically, the effects of lung density, chest‐wall thickness and a 3‐field beam configuration. Method and Materials: A comparison was made between collapsed cone convolution (CCC) calculations and DOSXYZnrc Monte Carlo (MC) simulations for: (1) a homogeneous phantom (ρ = 1.00, 0.500, 0.250 and 0.125 g⋅cm−3), (2) a slab phantom with varied chest‐wall thicknesses (dchest = 1.5, 2.25 and 3.0 cm) and lung densities and (3) a 15×15×15 cm3 box of lung surrounded by a 2.25 cm layer of water. We use ρlung = 0.400, 0.150 and 0.250 g⋅cm−3 to simulate full exhalation, inhalation and mean lung density respectively. For the homogeneous and slab phantoms one 6MV 10×10 cm2 field incident on a 50×50×25 cm3 phantom at SSD = 100 cm was simulated. For the box phantom a 3‐field beam configuration was used to simulate a basic lung treatment. Results: For the homogeneous phantom at, , the CCC results were systematically 5% high. The slab phantom results showed that past d = 3.0 cm the accuracy of the CCC calculations were dependent on lung density and independent of chest‐wall thickness. The percent difference was as high as 4% for . The 3‐field box simulations revealed an increased difference with decreasing lung density. Percent differences were as high as 8%, 4%, and 2% for the ρlung = 0.150, 0.250, and 0.400 g⋅cm−3 phantoms. Conclusion: For the homogeneous phantom simulations, the percent difference increased with decreasing density. Dose accuracy was found to be invariant with respect to chest‐wall thickness. From the 3‐field box configuration, we found the total percent difference increased with the number of fields.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| 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".