Sci-Sat AM: Stereo - 08: Stereotactic Ablative Radiotherapy (SABR) for low, intermediate and high risk prostate cancer using Volumetric Modulated Arc Therapy (VMAT) with a 10x Flattening Filter Free (FFF) beam
Bibliographic record
Abstract
Purpose: To determine the feasibility of using Volumetric Modulated Arc Therapy (VMAT) with a 10x Flattening Filter Free (FFF) beam for Stereotactic Ablative Radiotherapy (SABR) for low, intermediate and high risk prostate cancer. Methods and Materials: Ten anonymized patient CT data sets were used in this planning study. For each patient CT data set, three sets of contours were generated: 1) low risk, 2) intermediate risk, and 3) high risk scenarios. For each scenario, a single-arc and a double-arc VMAT treatment plans were created. Plans were generated with the Varian Eclipse™ treatment planning system for a Varian TrueBeam™ linac equipped with Millenium 120 MLC. Plans were created using a 10x-FFF beam with a maximum dose rate of 2400 MU/min. Dose prescription was 36.25Gy/5 fractions with the planning objective of covering 99% of the Planning Target Volume with the 95% of the prescription dose. Normal tissue constraints were based on provincial prostate SABR planning guidelines, derived from national and international prostate SABR protocols. Plans were evaluated and compared in terms of: 1) dosimetric plan quality, and 2) treatment delivery efficiency. Results: Both single-arc and double-arc VMAT plans were able to meet the planning goals for low, intermediate and high risk scenarios. No significant dosimetric differences were observed between the plans. However, the treatment time was significantly lower for a single-arc VMAT plans. Conclusions: Prostate SABR treatments are feasible with 10x-FFF VMAT technique. A single-arc VMAT offers equivalent dosimetric plan quality and a superior treatment delivery efficiency, compared to a double-arc VMAT.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".