Dosimetric and microdosimetric study of contrast-enhanced radiotherapy with kilovolt x-rays
Bibliographic record
Abstract
Kilovolt x-rays are clearly suboptimal compared to MV photon beams for radiotherapy of deep-seated tumours because of the increased attenuation in tissue, causing a rapid dose fall-off. This picture could change drastically when tumours can be labelled with contrast medium, containing high atomic number elements. This causes a significant dose enhancement to the tumour by exploiting the high cross sections for the photo-electric effect for kV x-rays. In this work, we have investigated the dosimetric and microdosimetric characteristics of kV contrast-enhanced radiation therapy (CERT) for different photon energies, contrast-medium concentrations and types (I and Gd). Two idealized patient treatment plans (head and lung) for irradiation with CT-arc beams were calculated. It is shown that the dose enhancement in tumours can be highly significant (up to about sixfold for realistic 80-120 kVp x-ray spectra and an iodine concentration of 50 mg ml-1) but that dose homogeneity in the tumour depends on photon energy, contrast-medium concentration and type, and irradiation scheme. An attempt to optimize the irradiation scheme is discussed. The microdosimetric study of the dose mean lineal energy shows that radiation quality changes in the contrast-medium-labelled region compared to homogeneous tissue are fairly small and limited to 10%. It is concluded that kV-CERT is a promising radiotherapy technique, provided contrast medium can be delivered reliably to tumours.
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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 | 0.001 |
| 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".