Poster — Thur Eve — 66: A planning comparison between RapidArc and intensity modulated radiotherapy for head and neck cancer
Bibliographic record
Abstract
Volumetric modulated arc therapy (VMAT) has recently been used to improve the dose distribution and efficiency of treatment delivery over the standard intensity-modulated radiotherapy (IMRT) technique. This study compares the dosimetry between RapidArc plan and standard IMRT plan for head and neck cancer. Three head and neck patients treated clinically with sliding window intensity-modulated radiotherapy (IMRT) technique at Grand River Regional Cancer Center were selected randomly and re-planned using RapidArc technique with 6 MV photon beams generated by a Varian 21EX linac with 120-leaf multileaf collimator. Three dose prescriptions were used to deliver 70 Gy, 63 Gy and 58.1 Gy to the regions of the primary tumors, intermediate-risk nodes and low-risk nodal level, respectively, in 35 fractions. Dosimetric comparison based on the dose-volume histogram, target coverage, organ at risk (OAR) dose sparing were studied between the RapidArc plan and IMRT plan. RapidArc technique from Varian Medical Systems showed superior target coverage, better OAR sparing, fewer monitor units per fraction with less treatment time over IMRT technique for head and neck cancers. The average homogeneity index, defined as the difference between the percentage dose covering 5% and 95% of the PTV, is 9.5 for RapidArc plan and 10.5 for IMRT plan. All RapidArc plans met the dose objectives for the primary OAR: spinal cord, brainstem, brain etc. Both parotid mean dose and D50% are lower for RapidArc plan than those of the IMRT plan. The technique is currently being used clinically at our cancer center.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".