Treatment planning with a 2.5 MV photon beam for radiation therapy
Bibliographic record
Abstract
PURPOSE: The shallow depth of maximum dose and higher dose fall-off gradient of a 2.5 MV beam along the central axis that is available for imaging on linear accelerators is investigated for treatment of shallow tumors and sparing the organs at risk (OARs) beyond it. In addition, the 2.5 MV beam has an energy bridging the gap between kilo-voltage (kV) and mega-voltage (MV) beams for applications of dose enhancement with high atomic number (Z) nanoparticles. METHODS: We have commissioned and utilized a MATLAB-based, open-source treatment planning software (TPS), matRad, for intensity-modulated radiation therapy (IMRT) dose calculations. Treatment plans for prostate, liver, and head and neck (H&N), nasal cavity, two orbit cases, and glioblastoma multiforme (GBM) were performed and compared to a conventional 6 MV beam. Additional Monte Carlo calculations were also used for benchmarking the central axis dose. RESULTS: Both beams had similar planning target volume (PTV) dose coverage for all cases. However, the 2.5 MV beam deposited 6%-19% less integral doses to the nasal cavity, orbit, and GBM cases than 6 MV photons. The mean dose to the heart in the liver plan was 10.5% lower for 2.5 MV beam. The difference between the doses to OARs of H&N for two beams was under 3%. Brain mean dose, brainstem, and optic chiasm max doses were, respectively, 7.5%-14.9%, 2.2%-8.1%, and 2.5%-19.0% lower for the 2.5 MV beam in the nasal cavity, orbit, and GBM plans. CONCLUSIONS: This study demonstrates that the 2.5 MV beam can produce clinically relevant treatment plans, motivating future efforts for design of single-energy LINACs. Such a machine will be capable of producing beams at this energy beneficial for low- and middle-income countries, and investigations on dose enhancement from high-Z nanoparticles.
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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 | 0.001 | 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 | 0.001 |
| 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".