Advanced image-identified extranodal extension of retropharyngeal lymph nodes in the refinement of N classification for nasopharyngeal carcinoma
Bibliographic record
Abstract
Advanced extranodal extension (ENE) in cervical lymph nodes (CLNs) increases the risk of distant metastasis in nasopharyngeal carcinoma (NPC). The 9th NPC staging system classifies N1/N2 patients with advanced CLN ENE as N3 due to similar outcomes. However, the prognostic impact of advanced ENE in retropharyngeal lymph nodes (RLNs) remains unclear. In this study of 4,485 patients with non-metastatic NPC, N1/N2 patients with advanced RLN ENE demonstrate better 5-year overall survival (hazard ratio [HR]: 0.60, 95% confidence interval [CI]: 0.38–0.93; HR: 0.57, 95% CI: 0.32–1.00) and failure-free survival (HR: 0.63, 95% CI: 0.44–0.92; HR: 0.52, 95% CI: 0.31–0.86) than N3 patients. Advanced RLN ENE shows a positive correlation with other RLN-related anatomical factors and is not identified as an independent prognostic factor. External validation in 3,849 patients from five centers supports these findings. Based on this evidence, upgrading advanced RLN ENE to N3 is not advised. • Anatomical factors of RLNs are positively correlated • Advanced RLN ENE is not an independent prognostic factor for NPC • N1/N2 NPC with advanced RLN ENE has a better prognosis than N3 disease Jiang et al. demonstrate that patients with N1 and N2 nasopharyngeal carcinoma (NPC) who exhibit advanced image-identified extranodal extension (ENE) in retropharyngeal lymph nodes (RLNs) should not be upgraded to N3 disease in the NPC staging system. This study supplements the 9th AJCC/UICC TNM staging system for NPC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".