Preoperative Clinical and Tumor Factors Associated With Adjuvant Therapy for Oral Cavity Cancer
Bibliographic record
Abstract
Importance: The standard of care for patients with oral cavity squamous cell carcinoma (OCSCC) is generally primary surgical resection with or without adjuvant therapy (AT), based on pathological factors. Identifying preoperative factors that are associated with the receipt of AT may enhance treatment planning. Objective: To identify preoperative patient and tumor factors associated with receiving AT, either radiation therapy (RT) or chemoradiation therapy (CRT), in patients with OCSCC. Design, Setting, and Participants: This cohort study, spanning January 2005 to December 2019 at 9 academic centers in Canada, was conducted as part of the Canadian Head & Neck Collaborative Research Initiative, a national network of head and neck surgical oncologists. Participants included patients with oral cavity cancer who underwent surgery. The data analysis was performed in March 2024. Exposures: Preoperative variables, including demographics (age, sex, smoking history, and Charlson Comorbidity Index [CCI]) and tumor characteristics (clinical T and N stage, biopsy grade, tumor size). Main Outcomes and Measures: The main outcomes were the receipt of AT vs surgery alone; the type of AT, either RT or CRT; and the presence of a strong pathologic indicator for AT. Results: Of the 3980 patients, 2438 underwent surgery alone (61%) and 1542 received AT (39%). Of these, 1907 (48%) had a strong pathologic indicator for AT. The mean (SD) age was 63 (13) years, and 1498 participants (38%) were female. On multivariable analysis, factors independently associated with AT included being older than 65 years (odds ratio [OR], 0.50 [95% CI, 0.38-0.64]), CCI of 4 or higher (OR, 1.83 [95% CI, 1.26-2.65]), previous head and neck cancer (OR, 0.40 [95% CI, 0.26-0.62]), maxillary alveolus (OR, 2.16 [95% CI, 1.11-4.22]) and retromolar trigone (OR, 1.85 [95% CI, 1.04-3.29) subsites, tumor dimension (OR, 1.35 [95% CI, 1.22-1.50] per cm), increasing clinical T and N stages, and worse grade on biopsy (poorly differentiated: OR, 1.89 [95% CI, 1.25-2.84]). Among those receiving AT, poorly differentiated grade (OR, 2.40 [95% CI, 1.34-4.30]) and advanced N stage were associated with CRT rather than RT. Among patients with strong pathologic indicators for AT, factors associated with not receiving AT included age, CCI, grade, stage, and tumor dimension. The prediction model showed good discriminatory power (area under the receiver operating characteristic curve, 0.84 [95% CI, 0.82-0.86]). Conclusions and Relevance: The results of this cohort study suggest that preoperative variables can help to identify patients with OCSCC who are more likely to receive AT, despite many factors not being predictable until the postoperative period. Early identification of patients at high risk may improve treatment planning and reduce delays in initiating AT, potentially enhancing patient outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".