The protective role of postoperative radiation therapy in low and intermediate grade major salivary gland malignancies: A study of the Canadian Head and Neck Collaborative Research Initiative
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to examine the utility of postoperative radiation for low and intermediate grade cancers of the parotid and submandibular glands. METHODS: The authors conducted a retrospective, Canadian-led, international, multi-institutional analysis of a patient cohort with low or intermediate grade salivary gland cancer of the parotid or submandibular gland who were treated from 2010 until 2020 with or without postoperative radiation therapy. A multivariable, marginal Cox proportional hazards regression analysis was performed to quantify the association between locoregional recurrence (LRR) and receipt of postoperative radiation therapy while accounting for patient-level factors and the clustering of patients by institution. RESULTS: In total, 621 patients across 14 tertiary care centers were included in the study; of these, 309 patients (49.8%) received postoperative radiation therapy. Tumor histologies included 182 (29.3%) acinic cell carcinomas, 312 (50.2%) mucoepidermoid carcinomas, and 137 (20.5%) other low or intermediate grade primary salivary gland carcinomas. Kaplan-Meier LRR-free survival at 10 years was 89.0% (95% confidence interval [CI], 84.9%-93.3%). In multivariable Cox regression analysis, postoperative radiation therapy was independently associated with a lower hazard of LRR (adjusted hazard ratio, 0.53; 95% CI, 0.29-0.97). The multivariable model estimated that the marginal probability of LRR within 10 years was 15.4% without radiation and 8.8% with radiation. The number needed to treat was 16 patients (95% CI, 14-18 patients). Radiation therapy had no benefit in patients who had early stage, low-grade salivary gland cancer without evidence of nodal disease and negative margins. CONCLUSIONS: Postoperative radiation therapy may reduce LLR in some low and intermediate grade salivary gland cancers with adverse features, but it had no benefit in patients who had early stage, low-grade salivary gland cancer with negative margins.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".