SU‐E‐T‐538: Lung SBRT Dosimetric Comparison of 3D Conformal and RapidArc Planning
Bibliographic record
Abstract
Purpose: Dose distributions of RapidArc Plan can be quite different from standard 3D conformal radiation therapy. SBRT plans can be optimized with high conformity or mimic the 3D conformal treatment planning with very high dose in the center of the tumor. This study quantifies the dosimetric differences among 3D conformal plan; flattened beam and FFF beam RapidArc Plans for lung SBRT. Methods: Five lung cancer patients treated with 3D non‐coplanar SBRT were randomly selected. All the patients were CT scanned with 4DCT to determine the internal target volume. Abdominal compression was applied to minimize respiratory motion for SBRT patients. The prescription dose was 48 Gy in 4 fractions. The PTV coverage was optimized by two groups of objective function: one with high conformity, another mimicking 3D conformal dose distribution with high dose in the center of PTV. Optimization constraints were set to meet the criteria of the RTOG‐0915 protocol. All VMAT plans were optimized with the RapidArc technique using four 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. Results: All the RapidArc plans with flattened beam and FFF beam had similar results for the PTV and OARs. For the high conformity optimization group, The DVH of PTV exhibited a steep dose fall‐off outside the PTV compared to the 3D non‐coplanar plan. However, for the group mimicking the 3D conformal target dose distribution, although the PTV is very similar to the 3D conformal plan, the ITV coverage is better than 3D conformal plan. Conclusion: Due to excellent clinical experiences of 3D conformal SBRT treatment, the Rapid Arc optimization mimicking 3D conformal planning may be suggested for clinical use.
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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 | 0.001 |
| 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) | 0.002 | 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".