Treatment Planning and Delivery Evaluation of Volumetric Modulated Arc Therapy for Stereotactic Body Radiotherapy of Spinal Tumours: Impact of Arc Discretization in Planning System
Bibliographic record
Abstract
The purpose of this study was to compare single arc volumetric modulated arc therapy (VMAT) to intensity modulated radiotherapy (IMRT) for spine SBRT in terms of target coverage, organ at risk (OAR) sparing and delivery performance. VMAT plans with 91 control points (VMAT-91CP) were generated for 15 spine metastases patients previously treated with a nine-field IMRT technique. VMAT and IMRT plans were compared based on target coverage, maximum spinal cord dose, maximum plan dose and volume of normal tissue receiving 20% to 80% of the prescribed dose. Treatment delivery time and monitor units (MU) were measured to determine delivery efficiency. To assess the impact of arc discretization in the treatment planning system (TPS), the VMAT-91CP plans were modified by almost doubling the number of CPs (VMAT-181CP). Planned-to-delivered dose agreement for both techniques was assessed using two types of 3D detector arrays.VMAT-91CP target coverage was equivalent to IMRT while maintaining or improving spinal cord sparing. This was achieved without increasing the volume of normal tissue receiving low or intermediate dose levels. Planned-to-delivered dose agreement equivalent to IMRT was achieved with VMAT, but required decreasing the CP angular spacing from 4° to 2° (VMAT-181CP plans). On average, VMAT-181CP plans reduced delivery time by 53% compared to IMRT. Single-arc VMAT for spine SBRT improved delivery efficiency while maintaining target coverage and OAR sparing compared to IMRT. VMAT plans generated with a CP gantry angular spacing of 2° is recommended to avoid a discretization effect in the TPS and ensure acceptable planned-to-delivered dose agreement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".