SU‐E‐T‐634: Accuracy of Patient Dose Calculation for Lung VMAT Plans: Comparison of Multiple TPS
Bibliographic record
Abstract
Purpose: To evaluate the discrepancy in patient dose calculation for lung VMAT SBRT plans calculated either using the same type treatment planning systems (TPS) commissioned in different hospitals or the different type TPS commissioned in the same hospital.Methods: 10 lung SBRT cases have been calculated using four different TPS. TPS A, B, and C are Pinnacle3 (version 9) and each one is commissioned in a different hospital for 6 MV photon beams (Varian Trilogy, 120 MLC). Therefore, beam data and parameters used in these TPS are not exactly the same. The TPS D is Eclipse (version 10) and uses the same machine data as the TPS A. The variations of PDD (10)/PDD (20) ratios for three 6 MV beams are within 0.5%. MU are rescaled for TPS A, B, and C to get a normalized dose in a reference condition. TPS A and D use the same MU. The Dmean for IGTV and PTV, D50, D99, D2 for PTV, D2cm, cord Dams, Dmean, V20 and V10 for lung are used for comparison. Cases with large discrepancy are also recalled in typically used lung QA phantoms (ROTG and Quasar). Results: The doses calculated using different Pinnacle TPS have no clinical significantly discrepancy. The largest discrepancy in PTV mean dose is 3%. In most cases the dose discrepancy between the Eclipse and Pinnacle is also small but the largest discrepancy is up to 7% in PTV mean dose. Dose calculation in lung phantoms cannot predict the corresponding discrepancy in patients. Conclusion: The same type PTS even commissioned in different hospitals will have minimum calculated discrepancy. Using different type TPS have largest discrepancy especially for a tumour surrounded unusual lower density lung volume. Centralized modeling with comprehensive QA will improve dose calculation accuracy to achieve a standardized treatment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".