Stereotactic Body Radiotherapy: Volumetric Modulated Arc Therapy versus 3D Non-Coplanar Conformal Radiotherapy for the Treatment of Early Stage Lung Cancer
Bibliographic record
Abstract
Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early stage lung cancer. Treatment duration can be >30 minutes per fraction with non-coplanar 3D-conformal radiotherapy (3D-CRT). Whilst this is generally well tolerated, faster delivery techniques are desirable. Volumetric modulated arc therapy (VMAT) allows for fast delivery of radiation treatment. The purpose of this planning study was to compare SBRT with 3D-CRT and VMAT, with VMAT plans generated using both single arc and 3 non-coplanar partial arcs. Ten patients who previously underwent SBRT (48 Gy in 4 fractions) with 3D-CRT were selected. VMAT plans were generated to treat the PTV while limiting doses to organs at risk. Cumulative dose volume histogram (DVH) parameters were compared between the 3 techniques using the Wilcoxon matched pairs test. Treatment delivery time was also assessed. Both VMAT techniques covered target volumes more conformally than 3D-CRT with a mean V48/VPTV of 1.21 for 3D-CRT, 1.03 for 3 arc plans and 1.01 for single arc plans (p = 0.005). Dose constraints to organs at risk were met using all three techniques. Mean lung doses were 2.93 Gy for 3D-CRT, 2.87 Gy for single arc and 2.73 Gy for the 3 arc technique (3-arc vs. 3D-CRT: p = 0.009). Lung V20 for 3D-CRT, 1 arc and 3 arcs were 3.24%, 2.89% and 2.73%, respectively (3 arc vs. 3D-CRT: p = 0.028). Mean time to deliver a single fraction was 13 minutes for 3D-CRT, 9.2 minutes for 3 arcs and 5.5 minutes for 1 arc. VMAT resulted in improved conformality compared to 3D-CRT. The 3 arc technique appears to have the lowest dose to lung although the magnitude is unlikely to be clinically significant. The main advantage of VMAT over 3D-CRT is faster treatment delivery time. Shortened treatment times are anticipated to improve tolerability of this treatment and reduce the chance of error due to intra-fraction motion.
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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) | 0.001 | 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".