Pathologic staging changes in oral cavity squamous cell carcinoma: Stage migration and implications for adjuvant treatment
Bibliographic record
Abstract
BACKGROUND: The eighth edition of the AJCC Cancer Staging Manual (AJCC 8) incorporates depth of invasion (DOI) into the pathologic tumor (pT) classification and pathologic extranodal extension (pENE) into the pathologic nodal (pN) classification for oral cavity squamous cell carcinoma (OCSCC). This study evaluated the incidence and prognostic importance of stage migration as a result of these changes in the AJCC 8 staging system. METHODS: From the National Cancer Database, cohorts were identified from patients with OCSCC undergoing definitive surgery between 2004 and 2013 for pT (n = 7184), pN (n = 13,627), and pathologic stage (pStage) analysis (n = 5580). RESULTS: DOI and pENE were prognostic in all groups except for pN3 according to the seventh edition of the AJCC Cancer Staging Manual (AJCC 7). Upstaging was seen in 12.4% of patients for the pT classification, in 13.3% for the pN classification, and in 24.8% for the overall pStage grouping. Notably, upstaging led to similar or improved 5-year overall survival (OS) for every AJCC 8 pT/N classification except pStage IVB. Patients with AJCC 7 pT1 tumors that were upstaged to AJCC 8 pT3 tumors had improved OS in comparison with the remainder of the pT3 group (71.7% vs 43.7%; P < .0001). A multivariable analysis of upstaged pT3N0 patients demonstrated a reduced risk of death with the receipt of postoperative radiotherapy (PORT; hazard ratio, 0.56; 95% confidence interval, 0.33-0.95; P = .03). CONCLUSIONS: Upstaging is common in AJCC 8, and patients with upstaged tumors demonstrate improved survival; these factors should be kept in mind when one is interpreting data with the new staging system. PORT may reduce deaths among newly upstaged pT3N0 patients, and further study is needed in this area.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".