The effects of dose calculation resolution on dose accuracy for radiation therapy treatments of the lung. Part II. A comparison of dose distributions from an explicit lung model to dose distributions derived from a CT representation
Bibliographic record
Abstract
PURPOSE: Due to limitations in computer memory and computation time, typical radiation therapy treatments are calculated with a voxel dimension on the order of several millimeters. The anatomy below this practical resolution is approximated as a homogeneous region uniform in atomic composition and density. The purpose of this article is to examine whether the exclusion of anatomic structure below the practical dose calculation resolution produces deviations in the resulting dose distributions. METHODS: EGSnrc calculated dose distributions from the BRANCH lung model of Part I are compared and contrasted to dose distributions from a CT representation of the same BRANCH model for three different phases of the respiration cycle. RESULTS: The exclusion of branching structures below a CT resolution of 1 x 1 x 2 mm3 resulted in a deviation in dose. The deviation in dose was as high as 14% but was localized around the branching structures. There was no significant variation in the dose deviation as a function of either field size or lung density. CONCLUSIONS: The exclusion of explicit branching structures of the lung in a CT representation creates localized deviations in dose. To ensure accurate dose calculations, CT resolution must be increased
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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 | 0.003 | 0.024 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".