Poster — Thur Eve — 59: Improving Treatment Delivery Efficiency in Lung SBRT with a VMAT Approach
Bibliographic record
Abstract
Lung SBRT has demonstrated excellent local control and low toxicity for patients with medically inoperable, early stage non‐small‐cell lung cancer (NSCLC). The promising clinical lung SBRT results have been achieved using a variety of hypofractionated dose regimens and localization/immobilization techniques; however treatment delivery times have been consistently long. Volumetric modulated arc therapy (VMAT) is a variable dose rate delivery method which has the potential for increased treatment delivery efficiency, as it allows continuous irradiation of the target with the gantry rotating around the patient. A retrospective planning study (n = 10) demonstrates VMAT as a promising technique to produce dose distributions similar or, in most cases, superior to those achieved with the 3D conformai SBRT plans currently used in our clinic for early stage NSCLC treatments. Single arc and non‐coplanar 2 arc VMAT approaches lead to improved conformity of both the high and low dose around the target and lower V20 and V5 values for the healthy lung. In spite of the significantly increased number of monitor units for the VMAT plans, this study shows treatment delivery time improvements (n = 2) of 45% for the single arc configurations compared with 3D conformai static delivery. A second non‐coplanar partial arc can be used to further increase conformity of the high dose surrounding the target and better avoid organs at risk, with a delivery efficiency improvement of 25% for the 2‐arc configurations compared with static delivery. Finally, treatment delivery accuracy with VMAT was similar to that of conformal static 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".