Evaluation of the trade-offs encountered in planning and treating locally advanced head and neck cancer: intensity-modulated radiation therapy<i>vs</i>dual-arc volumetric-modulated arc therapy
Bibliographic record
Abstract
OBJECTIVE: The primary purpose of this study was to assess the practical trade-offs between intensity-modulated radiation therapy (IMRT) and dual-arc volumetric-modulated arc therapy (DA-VMAT) for locally advanced head and neck cancer (HNC). METHODS: For 15 locally advanced HNC data sets, nine-field step-and-shoot IMRT plans and two full-rotation DA-VMAT treatment plans were created in the Pinnacle(3) v. 9.0 (Philips Medical Systems, Fitchburg, WI) treatment planning environment and then delivered on a Clinac iX (Varian Medical Systems, Palo Alto, CA) to a cylindrical detector array. The treatment planning goals were organised into four groups based on their importance: (1) spinal cord, brainstem, optical structures; (2) planning target volumes; (3) parotids, mandible, larynx and brachial plexus; and (4) normal tissues. RESULTS: Compared with IMRT, DA-VMAT plans were of equal plan quality (p>0.05 for each group), able to be delivered in a shorter time (3.1 min vs 8.3 min, p<0.0001), delivered fewer monitor units (on average 28% fewer, p<0.0001) and produced similar delivery accuracy (p>0.05 at γ(2%/2mm) and γ(3%/3mm)). However, the VMAT plans took more planning time (28.9 min vs 7.7 min per cycle, p<0.0001) and required more data for a three-dimensional dose (20 times more, p<0.0001). CONCLUSIONS: Nine-field step-and-shoot IMRT and DA-VMAT are both capable of meeting the majority of planning goals for locally advanced HNC. The main trade-offs between the techniques are shorter treatment time for DA-VMAT but longer planning time and the additional resources required for implementation of a new technology. Based on this study, our clinic has incorporated DA-VMAT for locally advanced HNC. ADVANCES IN KNOWLEDGE: DA-VMAT is a suitable alternative to IMRT for locally advanced HNC.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".