Comparison of Volumetric Modulated Arc Therapy (VMAT) and Conventional Intensity-Modulated Radiotherapy (IMRT) for Locally Advanced Head and Neck Squamous Cell Carcinoma: A Retrospective Cohort Study
Bibliographic record
Abstract
Purpose This study examines the outcomes of locally advanced head and neck squamous cell carcinoma (HNSCC) following the adoption of conventional intensity-modulated radiotherapy (cIMRT) and volumetric-modulated arc therapy (VMAT) over a decade. The region under study has higher comorbidities associated with increased HNSCC incidence and poorer prognosis. Materials and methods A 10-year retrospective review of electronic medical records included 296 patients with stage III, IVA, and IVB HNSCC (American Joint Committee on Cancer, Seventh edition). Survival outcomes were compared between VMAT and cIMRT using Kaplan-Meier survival curves and adjusted for relevant demographic factors using Cox's proportional hazards model. Analysis was performed using R software (R Foundation, Vienna, Austria). Results The median age of the cohort was 63 years, comprising of 80% males. The oropharynx was the most common primary tumor site. 264 (89%) received 50Gy or higher dose radiation by either cIMRT (22%) or VMAT (67%). At five years, locoregional control (LC) and overall survival (OS) rates were 79.5% and 56.7%, respectively. VMAT showed a significant improvement in five-year OS (63.4% versus 43.8% for cIMRT, p=0.0023) but no significant difference in five-year LC (81% VMAT versus 74.5% cIMRT, p=0.17). Grade 3-4 acute toxicity was observed in 22% of patients. Conclusions VMAT and cIMRT demonstrated excellent LC in locally advanced HNSCC despite high comorbidity rates. Notably, VMAT was associated with significantly better OS compared to cIMRT. These outcomes surpass historical data, suggesting that VMAT technology may lead to improved patient outcomes. However, larger randomized controlled trials and dosimetric studies are needed to confirm these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".