Clinically Delivered Treatment for Glioma Patients on Hybrid Magnetic Resonance Imaging (MRI)-Linear Accelerator (MR-Linac) versus Cone Beam CT (CBCT)-Guided Linac
Bibliographic record
Abstract
Abstract Magnetic Resonance Imaging (MRI)-Linear Accelerator (MR-Linac) 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. Thirty-seven consecutive glioma patients had treatment planning completed and approved prior to radiotherapy initiation using commercial treatment planning systems (TPS): a Monte Carlo-based or convolution-based TPS for MR-Linac or Cone Beam CT (CBCT)-guided Linac, respectively. In vivo skin dose was measured using an Optically Stimulated Luminescent Dosimeter (OSLD) and correlated with TPS skin dose. We found that Monte Carlo-based MR-Linac plans and convolution-based CBCT-Linac plans had similar dosimetric parameters for target volumes and organs-at-risk. However, MR-Linac plans had 1.52 Gy higher mean dose to air cavities (p<0.0001) and 1.10 Gy higher mean dose to skin (p<0.0001). In vivo skin dose was 14.5% greater for MR-Linac (p=0.0027), and were more accurately predicted by Monte Carlo-based calculation (ρ=0.95, p<0.0001) vs. convolution-based (ρ=0.80, p=0.0096). This is the first prospective dosimetric comparison of glioma patients clinically treated on both MR-Linac and CBCT-guided Linac. Skin doses were significantly greater with MR-Linac and correlated with in vivo measurements. Future MR-Linac 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 | 0.000 | 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) | 0.001 | 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".