Effectiveness of adjuvant chemoradiotherapy for oral cavity squamous cell carcinoma with minor and major extranodal extension: A multi-institutional consortium study.
Bibliographic record
Abstract
6010 Background: Extranodal extension (ENE) in oral cavity squamous cell carcinoma (OSCC) is a poor prognostic feature and an indication for adjuvant chemoradiotherapy. Recent pathology reporting guidelines recommend stratifying ENE into minor (≤2mm) or major (>2mm) extent. Prior studies have suggested that the addition of chemotherapy to adjuvant radiation may not improve oncologic outcomes in minor ENE. We evaluated this through a large multi-institutional cohort study. Methods: Surgically resected primary T1-4,N1-N3,M0 OSCC with pathologic nodal disease treated between 2005-2018 from four institutions in three countries were included. Extent of ENE was re-classified by pathologists on archived tissue. Adjuvant radiotherapy or chemoradiotherapy was recommended as per standard guidelines, unless contraindicated. Uni- (UVA) and Multivariable analysis (MVA) assessed the effect of chemotherapy on survival and disease control in minor and major ENE subgroups. Outcomes were also assessed in propensity score matched cohorts for each subgroup. Results: A total of 764 patients were included, of whom 126 (16%) had minor ENE and 242 (32%) had major ENE. Adjuvant chemoradiotherapy was given in 51 (40.5%) with minor ENE and 115 (47.5%) with major ENE. On MVA, chemotherapy was not associated with improved overall survival (OS) (HR 0.97, 95% CI 0.55-1.73, p=0.92) for patients with minor ENE, however, there was significant OS benefit for patients with major ENE (HR 0.61, 95% CI 0.38-0.98, p=0.041) after adjusting for age, T-category, N-category, margin status, adjuvant radiation, LN ratio, LVI, PNI, and ECOG status. Patients with major ENE receiving adjuvant chemoradiotherapy had improved locoregional control (LRC) (HR 0.67, 95% CI 0.42-1.09, p=0.1) although this did not reach statistical significance. Propensity score matched analysis found that patients with minor ENE who did and did not receive chemotherapy had no difference in OS (52% vs. 52%, p=0.85), but those with major ENE did (44% vs 13%, p=0.008). Conclusions: In OSCC, the addition of chemotherapy to adjuvant treatment is beneficial in major ENE, but our group failed to demonstrate a benefit for minor ENE. The benefit of chemotherapy in major ENE may result from improved LRC. Minor ENE is a clinically relevant subgroup in OSCC that warrants distinctive adjuvant treatment considerations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".