Spatially fractionated radiotherapy with very high energy electron pencil beam scanning
Bibliographic record
Abstract
Abstract Objective. To evaluate spatially fractionated radiation therapy (SFRT) for very-high-energy electrons (VHEEs) delivered with pencil beam scanning. Approach. Radiochromic film was irradiated at the CERN linear electron accelerator for research using 194 MeV electrons with a step-and-shoot technique, moving films within a water tank. Peak-to-valley dose ratios (PVDRs), depths of convergence (PVDR ⩽ 1.1), peak doses, and valley doses assessed SFRT dose distribution quality. A Monte Carlo (MC) model of the pencil beams was developed using TOPAS and applied to a five-beam VHEE SFRT treatment for a canine glioma patient, compared to a clinical 6 MV VMAT plan. The plans were evaluated based on dose-volume histograms, mean dose, and maximum dose to the planning target volume (PTV) and organs at risks (OARs). Main results. Experimental PVDR values were maximized at 15.5 ± 0.1 at 12 mm depth for 5 mm spot spacing. A DOC of 76.5, 70.7, and 56.6 mm was found for 5, 4, and 3 mm beamlet spacings, respectively. MC simulations and experiments showed good agreement, with maximum relative dose differences of 2% in percentage depth dose curves and less than 3% in beam profiles. Simulated PVDR values reached 180 ± 4, potentially achievable with reduced leakage dose. VHEE SFRT plans for the canine glioma patient showed a decrease in mean dose (>16%) to OARs while increasing the PTV mean dose by up to 15%. Lowering beam energy enhanced PTV dose homogeneity and reduced OAR maximum doses. Significance. The presented work demonstrates that pencil beam scanning SFRT with VHEEs could treat deep-seated tumors such as head and neck cancer or lung lesions, though small beam size and leakage dose may limit the achievable PVDR.
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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) | 0.002 | 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".