Sci-Thur AM: Planning - 12: Comparative study of SBRT lung dose calculation using Eclipse and Monte Carlo
Bibliographic record
Abstract
Stereotactic Body Radiation Therapy (SBRT) is an option for early stage non-small cell lung cancer treatment. In SBRT treatment, high biological effective dose is delivered to the patient within a small number of fractions. High level of confidence in accuracy is required in the entire treatment procedure, from patient setup, tumour delineation, treatment simulation and planning, to the final dose delivery. SBRT lung treatment utilizes small fields that are incident on large tissue inhomogeneities within the patient. It is difficult for commercially available treatment planning systems (TPS) to model the lack of charged particle equilibrium and the dose near tissue-lung interfaces accurately. The Monte Carlo (MC) technique calculates the dose distribution from the first principles thereby providing a feasible tool for verifying the dose distribution computed from TPS. In this study, we compared the SBRT dose distribution between Eclipse 8.9 and BEAMnrc/DOSXYZnrc for both conformal and RapidArc plans. Calculation results for five clinical SBRT conformal lung plans were compared. Eclipse and MC results for each plan showed good agreement in dose received by organs at risk. MC simulation predicted uniformly hotter or similar PTV coverage for three cases with tumor either small or attached to the chest wall. When tumor is inside lung and at relatively medium to larger size for SBRT, MC predicted lower PTV coverage. The variation in dose coverage may depend on the tumour size and its position within the lung. Dose comparison for RapidArc plans shows similar dependence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".