Tumour dose enhancement using modified megavoltage photon beams and contrast media
Bibliographic record
Abstract
This study examines the magnitude of tumour dose enhancement achieved by injection of gadolinium or iodine contrast media (CM) and treatment using modified x-ray photon spectra from linear accelerators. Monte Carlo modelling of the linear accelerator and patient geometry was used to explore the effect of removing the flattening filter for various beam qualities and the resultant effect on dose enhancement. In addition, ionization measurements were conducted to observe dose enhancement within a phantom containing CM. Simulation results indicate that for flattened 6-24 MV photon beams and realistic CM tumour concentrations, the dose enhancement remains below 5%. However, if the flattening filter is removed, dose enhancement is increased significantly. For a 30 mg ml(-1) gadolinium CM tumour concentration, for example, 8.4%, 10.8%, 13.7% and 23.1% dose enhancements are achieved for 18 MV, 6 MV, 4 MV and 2 MV unflattened beams, respectively. In contrast to the phototherapy technique, which uses the orthovoltage beam from a modified CT scanner to achieve dose enhancement, all unflattened spectra preserve the dose build-up at the surface, and thus the skin and bone are spared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".