SU‐E‐T‐345: Effect of DLG and MLC Transmission Value Set in the Treatment Planning System (TPS) On Dosimetric Accuracy of True Beam Hypofractionated SRT/SBRT and 2Gy/fx Prostate Rapid Arc Plans
Bibliographic record
Abstract
Purpose: Due to limited commissioning time, we previously only released our True beam non‐FFF mode for prostate treatment. Clinical demand now pushes us to release the non‐FFF mode for SRT/SBRT treatment. When re‐planning on True beam previously treated SRT/SBRT cases on iX machine we found the patient specific QA pass rate was worse than iX's, though the 2Gy/fx prostate Result had been as good. We hypothesize that in TPS the True beam DLG and MLC transmission values, of those measured during commissioning could not yet provide accurate SRS/SBRT dosimetry. Hence this work is to investigate how the TPS DLG and transmission value affects Rapid Arc plans’ dosimetric accuracy. Methods: We increased DLG and transmission value of True beam in TPS such that their percentage differences against the measured matched those of iX's. We re‐calculated 2 SRT, 1 SBRT and 2 prostate plans, performed patient specific QA on these new plans and compared the results to the previous. Results: With DLG and transmission value set respectively 40 and 8% higher than the measured, the patient specific QA pass rate (at 3%/3mm) improved from 95.0 to 97.6% vs previous iX's 97.8% in the case of SRT. In the case of SBRT, the pass rate improved from 75.2 to 93.9% vs previous iX's 92.5%. In the case of prostate, the pass rate improved from 99.3 to 100%. The maximum dose difference in plans before and after adjusting DLG and transmission was approximately 1% of the prescription dose among all plans. Conclusion: The impact of adjusting DLG and transmission value on dosimetry might be the same among all Rapid Arc plans regardless hypofractionated or not. The large variation observed in patient specific QA pass rate might be due to the data analysis method in the QA software being more sensitive to hypofractionated plans.
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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 | 0.003 |
| 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".