Development and validation of a novel TNM staging N‐classification of oral cavity squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: For oral cavity squamous cell carcinoma (OSCC), extent of extranodal extension (ENE) (minor, ≤2 mm; major, >2 mm) is differentially prognostic, whereas limitations exist with the 8th edition of American Joint Committee on Cancer/International Union Against Cancer TNM N-classification (TNM-8-N). METHODS: Resected OSCC patients at four centers were included and extent of ENE was recorded. Thresholds for optimal overall survival (OS) discrimination of lymph node (LN) features were established. After dividing into training and validation sets, two new N-classifications were created using 1) recursive partitioning analysis (RPA), and 2) adjusted hazard ratios (aHRs) and were ranked against TNM-8-N and two published proposals. RESULTS: A total of 1460 patients were included (pN0: 696; pN+: 764). Of the pN+ cases, 135 (18%) had bilateral/contralateral LNs; 126 (17%) and 244 (32%) had minor and major ENE, and two (0.3%) had LN(s) >6 cm without ENE (N3a). LN number (1 and >1 vs. 0: aHRs, 1.92 [95% confidence interval (CI), 1.44-2.55] and 3.21 [95% CI, 2.44-4.22]), size (>3 vs. ≤3 cm: aHR, 1.88 [95% CI, 1.44-2.45]), and ENE extent (major vs. minor: aHR, 1.40 [95% CI, 1.05-1.87]) were associated with OS, whereas presence of contralateral LNs was not (aHR, 1.05 [95% CI, 0.81-1.36]). The aHR proposal provided optimal performance with these changes to TNM-8-N: 1) stratification of ENE extent, 2) elimination of N2c and 6-cm threshold, and 3) stratification of N2b by 3 cm threshold. CONCLUSION: A new N-classification improved staging performance compared to TNM-8-N, by stratifying by ENE extent, eliminating the old N2c category and the 6 cm threshold, and by stratifying multiple nodes by size.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".