Improvement of radiological penumbra using intermediate energy photons (IEP) for stereotactic radiosurgery
Bibliographic record
Abstract
Using efficient immobilization and dedicated beam collimation devices, stereotactic radiosurgery ensures highly conformal treatment of small tumours with limited microscopic extension. One contribution to normal tissue irradiation remains the radiological penumbra. This work aims at demonstrating that intermediate energy photons (IEP), above orthovoltage but below megavoltage, improve dose distribution for stereotactic radiosurgery for small irradiation field sizes due to a dramatic reduction of radiological penumbra. Two different simulation systems were used: (i) Monte Carlo simulation to investigate the dose distribution of monoenergetic IEP between 100 keV and 1 MeV in water phantom; (ii) the Pinnacle3 TPS including a virtual IEP unit to investigate the dosimetry benefit of treating with 11 non-coplanar beams a 2 cm tumour in the middle of a brain adjacent to a 1 mm critical structure. Radiological penumbrae below 300 microm are generated for field size below 2 x 2 cm2 using monoenergetic IEP beams between 200 and 400 keV. An 800 kV beam generated in a 0.5 mm tungsten target maximizes the photon intensity in this range. Pinnacle3 confirms the dramatic reduction in penumbra size. DVHs show for a constant dose distribution conformality, improved dose distribution homogeneity and better sparing of critical structures using a 800 kV beam compared to a 6 MV beam.
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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) | 0.002 | 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".