SU‐E‐T‐882: Electron Disequilibrium Pitfalls for Small Megavoltage Photon Fields Incident on Lung Tumors
Bibliographic record
Abstract
Purpose: Stereotactic body radiation therapy (SBRT) of lung uses sub‐centimeter MV x‐ray fields. Under these conditions, lateral electron disequilibrium (LED) can occur in lung tissue, which causes perturbations of the dose distribution near the tumor. This purpose of this work is to characterize the LED effect in lung for clinically relevant ranges of beam energies, field sizes, and lung densities. Methods: The MC code DOSXYZnrc (National Research Council of Canada, Ottawa, ON) was employed to simulate two 20×20×25cm3 water‐lung‐water slab phantoms. The two phantoms were identical in composition except that the second phantom also included a 3×3×3cm3 centrally located water cube to mimic a small lung tumor. To characterize LED, dose calculations were performed using combinations of beam energy (Co‐60 up to 18MV), field sizes (1×1cm2 up to 15×15cm2), and lung densities (0.001g/cm3 up to 1g/cm3) for both phantoms. Results: MC lung slab phantom simulations revealed that for each combination of beam energy and field size, a critical lung density (CLD) could be defined to establish LED. For example, a 6MV 5×5cm2 photon field was subject to LED for lung densities of 0.2g/cm3 or lower. On the contrary, employing an 18MV 5×5cm2 photon field increased the CLD to 0.5g/cm3. With regard to the second lung tumor phantom, the LED effect caused major reductions in the calculated dose near to the tumor. For instance, dose reductions of 24% and 16% were found within the distal and proximal tumor surfaces, respectively. Conclusion: We have fully characterized the LED effect and shown that it causes dose reductions in both lung and tumor tissues. To avoid these dose perturbations, SBRT of lung cancer patients should be optimized to select radiation therapy parameters carefully in accordance with patient lung density. Financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Canadian Institutes of Health Research (CIHR) are gratefully acknowledged.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".