The dosimetric impact of gadolinium-based contrast media in GBM brain patient plans for a MRI-Linac
Bibliographic record
Abstract
Abstract Dosimetric effects of gadolinium based contrast media (Gadovist) were evaluated for the Elekta MRI linear accelerator using the research version of the Monaco treatment planning system (TPS). In order to represent a gadolinium uptake, the contrast was manually assigned to a phantom as well as to the gross tumour volume (GTV) of 6 glioblastoma multiforme (GBM) patients. A preliminary estimate of the dose enhancement, due to gadolinium, was performed using the phantom irradiated with a single beam. A more complicated assessment was performed for the GBM patients using a 7 field IMRT technique. The material table in Monaco was modified in order to identify the presence of a non-biological material. The dose distribution was modelled using GPUMCD (MC algorithm in Monaco) for an unmodified (or default) material table (DMT) as well as for a modified (or custom) material table (CMT) for both the phantom and patients. Various concentrations ranging between 8 and 157 mg ml −1 were used to represent the gadolinium uptake in the patient’s GTV. It was assumed that the gadolinium concentration remained the same for the entire course of radiation treatment. Results showed that at the tissue-Gadovist interface, inside the phantom, dose scored using the DMT was 7% lower compared to that using the CMT for 157 mg ml −1 concentration of gadolinium. Dosimetric differences in the case of the patient study were measured using the DVH parameters. D 50% was higher by 6% when the DMT was used compared to the CMT for dose modelling for a gadolinium concentration of 157 mg ml −1 . This difference decreased gradually with decreasing concentration of gadolinium. It was concluded that dosimetric differences can be quantified in Monaco if the tumour-gadolinium concentration is more than 23 mg ml −1 . If the gadolinium concentration is lower than 23 mg ml −1 , then a correction for the presence of gadolinium may not be necessary in the TPS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".