Lack of influence of intravenous contrast on head and neck IMRT dose distributions
Bibliographic record
Abstract
PURPOSE: Intravenous (i.v.) contrast at the time of CT-Simulation facilitates radiotherapy contouring, but may introduce a discrepancy between planned and delivered dose due to density variation in blood vessels. Here, the effect of physiologic and non-physiologic extremes of i.v. contrast densities on intensity modulated radiotherapy (IMRT) plans for patients with head and neck cancer was investigated. METHODS AND MATERIALS: This planning study was conducted using i.v. contrast CT scans of ten patients with squamous cell cancer of the head and neck treated with IMRT. The target volumes and normal tissues, including the blood vessels of the head and neck, were contoured and IMRT plans were created according to RTOG Protocol 0022. The density within the blood vessels was then virtually altered to mimic non-contrast and extreme (bone and air) densities. The dose was then recalculated using the same IMRT plan. Plans obtained with and without density overrides were then compared. RESULTS: The change in planning target volume (PTV) coverage for plans with and without i.v. contrast was minimal. The volume of the PTVs covered by the 93% and 100% isodoses changed on average by 0.57%. The minimum dose to PTVs varied by a maximum of 0.17 Gy. The maximum point dose to critical organs changed by a maximum of 0.12 Gy (brainstem). Non-physiologic extremes of density within blood vessels also resulted in minimal changes in tumor or normal tissue dosimetry. CONCLUSION: The use of i.v. contrast at time of CT-simulation does not significantly affect dose calculation in head and neck IMRT plans.
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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 | 0.004 |
| 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 | 0.001 | 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".