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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".