Optimization of intensity-modulated very high energy (50–250 MeV) electron therapy
Bibliographic record
Abstract
This work evaluates the potential of very high energy (50-250 MeV) electron beams for dose conformation and identifies those variables that influence optimized dose distributions for this modality. Intensity-modulated plans for a prostate cancer model were optimized as a function of the importance factors, beam energy and number of energy bins, number of beams, and the beam orientations. A trial-and-error-derived constellation of importance factors for target and sensitive structures to achieve good conformal dose distributions was 500, 50, 10 and I for the target, rectum, bladder and normal tissues respectively. Electron energies greater than 100 MeV were found to be desirable for intensity-modulated very high energy electron therapy (VHEET) of prostate cancer. Plans generated for lower energy beams had relatively poor conformal dose distributions about the target region and delivered high doses to sensitive structures. Fixed angle beam treatments utilizing a large number of fields in the range 9-21 provided acceptable plans. Using more than 21 beams at fixed gantry angles had an insignificant effect on target coverage, but resulted in an increased dose to sensitive structures and an increased normal tissue integral dose. Minor improvements in VHEET plans utilizing a 'small' number (< or =9) of beams may be achieved if, in addition to intensity modulation, energy modulation is implemented using a small number (< or =3) of beam energies separated by 50 to 100 MeV. Rotation therapy provided better target dose homogeneity but unfortunately resulted in increased rectal dose, bladder dose and normal tissue integral dose relative to the 21-field fixed angle treatment plan. Modulation of the beam energy for rotation therapy had no beneficial consequences on the optimized dose distributions. Lastly, selection of beam orientations influenced the optimized treatment plan even when a large number of beams (approximately 15) were employed.
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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 | 0.000 | 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) | 0.000 | 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".