Sci-Sat AM: Stereo - 06: Dosimetric Comparison of 3D Conformai, Flattened and Flattening Filter-Free TrueBeam RapidArc Planning for Lung SBRT
Bibliographic record
Abstract
The major advantages of the VMAT SBRT plans compared to the conventional 3D conformai plan include faster delivery and improved target dose conformity. This study quantifies the dosimetric differences among 3D conformai plan; flattened beam and flattening filter-free (FFF) beam RapidArc Plans for lung SBRT. Five early stage lung cancer patients with various tumor positions and sizes previously treated with 3D non-coplanar SBRT were randomly selected. 4DCT was used for each patient to determine the internal target volume. Abdominal compression was applied to minimize respiratory motion for SBRT patients. For treatment planning, a 5 mm margin was given to the ITV to generate a planning target volume. The prescription dose was 48 Gy in 4 fractions and normalized to 95% of the PTV. Organs at risk (OAR) included spinal cord, esophagus, heart, trachea, bilateral lung, and great vessels. Optimization constraints were set to meet the criteria of the RTOG-0915 protocol. All VMAT plans were optimized with the RapidArc technique using two full arcs in Eclipse treatment planning system. The RapidArc SBRT plans with flattened 6MV beam and 6MV FFF beam were generated and dosimetric results were compared with the previous treated 3D non-coplanar plans. RapidArc plans demonstrated better conformity to target, sharper dose fall-off in normal tissues and lower dose to normal lung and other OARs than the 3D conformai plans. RapidArc SBRT for FFF beam showed comparable target conformity, adequate tumor dose, and clinically acceptable DVHs of OARs to flattened beams and significantly reduced treatment delivery time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".