Impact of Geometric Uncertainties on Dose Distribution During Intensity Modulated Radiotherapy of Head-and-neck Cancer: The Need for a Planning Target Volume and A Planning Organ-at-Risk Volume
Bibliographic record
Abstract
We assessed the effect of geometric uncertainties on target coverage and on dose to the organs at risk (OARS) during intensity-modulated radiotherapy (IMRT) for head-and-neck cancer, and we estimated the required margins for the planning target volume (PTV) and the planning organ-at-risk volume (PRV). For eight headand- neck cancer patients, we generated IMRT plans with localization uncertainty margins of 0 mm, 2.5 mm, and 5.0 mm. The beam intensities were then applied on repeat computed tomography (CT) scans obtained weekly during treatment, and dose distributions were recalculated. The dose–volume histogram analysis for the repeat CT scans showed that target coverage was adequate (V100 ≥ 95%) for only 12.5% of the gross tumour volumes, 54.3% of the upper-neck clinical target volumes (CTVS), and 27.4% of the lower-neck CTVS when no margins were added for PTV. The use of 2.5-mm and 5.0-mm margins significantly improved target coverage, but the mean dose to the contralateral parotid increased from 25.9 Gy to 29.2 Gy. Maximum dose to the spinal cord was above limit in 57.7%, 34.6%, and 15.4% of cases when 0-mm, 2.5-mm, and 5.0-mm margins (respectively) were used for PRV. Significant deviations from the prescribed dose can occur during IMRT treatment delivery for headand- neck cancer. The use of 2.5-mm to 5.0-mm margins for PTV and PRV greatly reduces the risk of underdosing targets and of overdosing the spinal cord.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".