Comparison of Prospectively Generated Glioma Treatment Plans Clinically Delivered on Magnetic Resonance Imaging (MRI)-Linear Accelerator (MR-Linac) Versus Conventional Linac: Predicted and Measured Skin Dose
Bibliographic record
Abstract
Introduction: Magnetic resonance imaging-linear accelerator radiotherapy is an innovative technology that requires special consideration for secondary electron interactions within the magnetic field, which can alter dose deposition at air–tissue interfaces. As part of ongoing quality assurance and quality improvement of new radiotherapy technologies, the purpose of this study was to evaluate skin dose modelled from the treatment planning systems of a magnetic resonance imaging-linear accelerator and a conventional linear accelerator, and then correlate with in vivo measurements of delivered skin dose from each linear accelerator. Methods: In this prospective cohort study, 37 consecutive glioma patients had treatment planning completed and approved prior to radiotherapy initiation using commercial treatment planning systems: a Monte Carlo-based algorithm for magnetic resonance imaging-linear accelerator or a convolution-based algorithm for conventional linear accelerator. In vivo skin dose was measured using an optically stimulated luminescent dosimeter. Results: Monte Carlo-based magnetic resonance imaging-linear accelerator plans and convolution-based conventional linear accelerator plans had similar dosimetric parameters for target volumes and organs-at-risk. However, magnetic resonance imaging-linear accelerator plans had 1.52 Gy higher mean dose to air cavities ( P < .0001) and 1.10 Gy higher mean dose to skin ( P < .0001). In vivo skin dose was 14.5% greater for magnetic resonance imaging-linear accelerator treatments ( P = .0027), and was more accurately predicted by Monte Carlo-based calculation ( ρ = 0.95, P < .0001) versus convolution-based ( ρ = 0.80, P = .0096). Conclusion: This is the first prospective dosimetric comparison of glioma patients clinically treated on both magnetic resonance imaging-linear accelerator and conventional linear accelerator. Our findings suggest that skin doses were significantly greater with magnetic resonance imaging-linear accelerator plans but correlated better with in vivo measurements of actual skin dose from delivered treatments. Future magnetic resonance imaging-linear accelerator planning processes are being designed to account for skin dosimetry and treatment delivery.
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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 | 0.003 |
| 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) | 0.001 | 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".