Proposal for the 8th edition of the <scp>AJCC</scp>/<scp>UICC</scp> staging system for nasopharyngeal cancer in the era of intensity‐modulated radiotherapy
Bibliographic record
Abstract
BACKGROUND: An accurate staging system is crucial for cancer management. Evaluations for continual suitability and improvement are needed as staging and treatment methods evolve. METHODS: This was a retrospective study of 1609 patients with nasopharyngeal carcinoma investigated by magnetic resonance imaging, staged with the 7th edition of the American Joint Committee on Cancer (AJCC)/International Union Against Cancer (UICC) staging system, and irradiated by intensity-modulated radiotherapy at 2 centers in Hong Kong and mainland China. RESULTS: Among the patients without other T3/T4 involvement, there were no significant differences in overall survival (OS) between medial pterygoid muscle (MP) ± lateral pterygoid muscle (LP), prevertebral muscle, and parapharyngeal space involvement. Patients with extensive soft tissue involvement beyond the aforementioned structures had poor OS similar to that of patients with intracranial extension and/or cranial nerve palsy. Only 2% of the patients had lymph nodes > 6 cm above the supraclavicular fossa (SCF), and their outcomes resembled the outcomes of those with low extension. Replacing SCF with the lower neck (extension below the caudal border of the cricoid cartilage) did not affect the hazard distinction between different N categories. With the proposed T and N categories, there were no significant differences in outcome between T4N0-2 and T1-4N3 disease. CONCLUSIONS: After a review by AJCC/UICC preparatory committees, the changes recommended for the 8th edition include changing MP/LP involvement from T4 to T2, adding prevertebral muscle involvement as T2, replacing SCF with the lower neck and merging this with a maximum nodal diameter > 6 cm as N3, and merging T4 and N3 as stage IVA criteria. These changes will lead not only to a better distinction of hazards between adjacent stages/categories but also to optimal balance in clinical practicability and global applicability.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".