Refining nodal category in TNM staging with extent of extranodal extension for oral cavity squamous cell carcinoma.
Bibliographic record
Abstract
6015 Background: The TNM 8th edition N classification (TNM-8-N) has limitations based on evidence that: (1) extent of extranodal extension (ENE), classified as minor( min ) (≤ 2mm) or major ( maj )(> 2mm), is prognostic, and (2) N3a category (i.e. lymph node (LN)s > 6 cm, no ENE) is rare and redundant. Refining N classification may improve staging performance. Methods: Patients with surgically resected T1-4,N0-3 OCSCC at four centers between 2005 - 2018 were included. Pathologists were asked to measure extent of ENE on archived slides for all cases. Thresholds for stratification of adverse LN features were determined using the Contal O’Quigley method. Multivariable analyses for overall survival (OS) were performed using Cox proportional hazard models. Two new N classification proposals were created based on LN features using (1) adjusted hazard ratios (aHR) and (2) recursive partitioning analysis (RPA). These were ranked against two published proposals and TNM-8-N with the following criteria: hazard consistency, hazard discrimination, explained variance, likelihood difference, sample size balance. Results: In total, 1460 patients were included, 764 (52%) were LN positive, and 135 (18%) had contralateral LNs. The following TNM-8-N subgroups were rare and had poor prognosis: LN between 3-6 cm without ENE (N2a) [n=4] and LN(s) > 6 cm without ENE (N3a) [n=1]. All 5 either recurred or died. TNM-8-N N2b and N2c categories had disordered 5-year OS (45% vs. 67%). Thresholds for stratification of LN features, rounded to the nearest whole number, were <=1 vs >1 for number of LNs; ≤ 3 vs. > 3 cm for size of largest LN, and ≤ 2 mm vs. > 2 mm for extent of ENE. Significant predictors of OS included (1) the presence of ENE maj and no ENE vs. ENE min (HR 1.37; 95% CI 1.03-1.82 and HR=0.66, 95%CI 0.49-0.88), (2) multiple LNs vs. 1 LN (HR 1.72; 95% CI, 1.33-2.22), and (3) size of largest LN > 3 cm vs. <=3 (HR 1.86; 95% CI 1.42-2.43), but contralateral LN(s) vs. ipsilateral LN(s) was not (HR 1.08; 0.83-1.40). The aHR proposal ranked highest (table). Conclusions: A new N-classification proposal based on aHR provides improved staging information. Major changes include: (1) stratification by ENE extent (2) elimination of N2c (3) stratification of multiple LNs without ENE by size, and (4) elimination of the 6 cm threshold. New TNM iterations may incorporate these changes. TNM-8-N aHR RPA Liao et al. Ho et al. N1 1 LN, ≤ 3cm, no ENE same Same same 1 LN, no ENE N2a 1 LN, ≤ 3 cm, with ENEOR1 LN, 3-6 cm, no ENE 1 LN, ≤ 3 cm, with ENE 1 LN, any size, with ENE same 1 LN, with ENEOR2 LNs N2b >1 LN, ≤ 6 cm, no ENE > 1 LNs,≤ 3 cm, no ENE >1 LN, ≤ 3 cm, no ENE same N2c Contralateral LN(s), ≤ 6 cm, no ENE * * same N3a LN(s) > 6 cm, no ENE LN(s) > 3 cm, no ENEOR1 LN, > 3 cm, ENE min OR> 1 LNs, ENE min LN (s) > 3 cm, no ENEOR>1 LN, ENE min ≤ 7 LNs,OR≤ 4 LNs with ENE 3-7 LNs N3b 1 LN, > 3 cm, any ENEOR>1 LN(s), any ENE 1 LN > 3 cm, ENE maj OR>1 LNs, ENE maj > 1 LN, ENE maj ≥ 8 LNsOR ≥ 5 LNs with ENE ≥ 8 LNs Ranking 5 1 2 4 3 *eliminated
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".