A Proposal for HPV-Associated Oropharyngeal Carcinoma in the Ninth Edition Clinical TNM Classification
Bibliographic record
Abstract
Importance: A subset of Union for International Cancer Control (UICC)/American Joint Committee on Cancer (AJCC) eighth edition TNM stage I and II human papillomavirus-positive oropharyngeal carcinoma has undesirable outcomes, which might have contributed to a lack of success in phase III deintensification trials. Refining clinical stage groups, especially in the overabundant cN1/stage I group, has become important for treatment selection. Objective: To assess the prognostic importance of pretreatment lymph node (LN) characteristics to optimize case distribution and outcome homogeneity within the N classification system. Design, Setting, and Participants: This is an international multi-institutional retrospective prognostic cohort study. Analysis of human papillomavirus-positive oropharyngeal carcinoma treated curatively from 4 institutions (International Collaboration of Oropharyngeal Cancer Network for N-Classification [ICON-N] dataset) provided a refined clinical staging proposal; an independent dataset (Centre Hospitalier de l'Université de Montréal [CHUM] dataset) validated the proposal. Neuroradiologists reviewed pretreatment computed tomography and/or magnetic resonance imaging for nodal features, including presence or absence of abnormal LN(s), retropharyngeal LN, laterality, number of abnormal LN, and imaging-detected extranodal extension (iENE). Data were collected from February to May 2023, and data were analyzed from June to July 2023. Exposures: Definitive chemoradiotherapy/radiotherapy or definitive surgery with or without postoperative chemoradiotherapy/radiotherapy. Main Outcomes and Measures: The primary end point was overall survival. A Cox proportional hazards multivariable model was used to estimate adjusted hazard ratios (AHRs) and to derive an optimal clinical TNM stage classification (AHR-stage schema) incorporating the strongest prognostic nodal features within the UICC/AJCC eighth edition TNM framework after confirming the prognostication of iENE status. The performance (according to overall normalized scores and ranking) of the AHR-stage schema against the current UICC/AJCC eighth edition TNM staging system was evaluated for hazard consistency, hazard discrimination, prognostic importance, and sample size balance. Validation was performed in the CHUM dataset. Results: The ICON-N dataset comprised 2053 patients, including 1898 (92.5%) with cN-positive disease and 155 (7.5%) with cN0 disease; a total of 298 (14.5%) were female, and the mean (SD) age was 60.6 (9.3) years. iENE-positive disease was identified in 710 of 1898 patients with cN-positive disease (37.4%). The median (range) follow-up was 5.1 (0.1-14.7) years. iENE was the strongest prognostic nodal feature in multivariable analysis; the AHR for iENE-positive vs iENE-positive disease was 2.43 (95% CI, 1.96-3.03) in the ICON-N dataset and 2.04 (95% CI, 1.28-3.23) in the CHUM dataset (n = 451). Reclassifying iENE-positive cases 1 stratum higher for N categorization without altering iENE-negative cases yielded an AHR-stage schema that outperformed the current TNM staging system in disease-free and overall survival with a lower (ie, better) overall normalized score (2 vs 3). Conclusions and Relevance: In this study, reclassifying each N category 1 stratum higher for iENE-positive disease resulted in better disease-free and overall survival. The proposed new classification outperformed the currently TNM staging system in risk stratification and may facilitate future clinical trial design, outcomes research, and patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".