Intensity Modulated and Three-Dimensional Conformal Radiation Therapy Plans for Oropharyngeal Cancer: A Comparison of Their Sensitivity to Set-Up Errors and Uncertainties
Bibliographic record
Abstract
We compared the effect of set-up error and uncertainty on two radiation therapy treatment plans for head and neck cancer: one using intensity modulated radiation therapy (IMRT) and one using conventional three-dimensional conformal radiation therapy (3D-CRT). We used a Pinnacle3 (Philips Medical Systems, Markham, Ontario) system to create the two treatment plans (7-beam IMRT and 5-beam 3D-CRT) for the same volumetric data set, based on the objectives and constraints defined in the Radiation Therapy Oncology Group H-0022 protocol. In both plans, the dose-volume constraints for the targets and the organs at risk (oars) were met as closely as the beam geometries would allow. Monte Carlo-based simulations of set-up error and uncertainty were performed in three orthogonal directions for 840 simulated "courses of treatment" for each plan. A systematic error (chosen from distributions characterized by standard deviations ranging from 0 mm to 6 mm) and random uncertainties (2 mm standard deviation) were incorporated. We used a probability approach to compare the sensitivities of the IMRT and the 3D-CRT plans to set-up error and uncertainty in terms of equivalent uniform dose (EUD) to targets and oars.Based on the EUD analysis, the targets and oars showed considerably greater sensitivity to set-up error with the IMRT plan than with the 3D-CRT plan. For the IMRT plan, target EUDS were reduced by 4%, 7.5%, and 10% for 2-mm, 4-mm, and 6-mm set-up errors respectively. However, even with set-up error, the mandible, spinal cord, and parotid EUDS always remained lower with the IMRT plan than with the 3D-CRT plan.We conclude that, when quantified by EUD, IMRT-plan doses to oars and targets are more sensitive to set-up error than are 3D-CRT-plan doses. However, as judged by the differences between target and OAR doses, IMRT retains its superiority over 3D-CRT, even in the presence of set-up error.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".