The addition of pretreatment plasma Epstein–Barr virus DNA into the eighth edition of nasopharyngeal cancer TNM stage classification
Bibliographic record
Abstract
The eighth edition of the American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) stage classification (TNM) for nasopharyngeal carcinoma (NPC) was launched. It remains unknown if incorporation of nonanatomic factors into the stage classification would better predict survival. We prospectively recruited 518 patients with nonmetastatic NPC treated with radical intensity-modulated radiation therapy ± chemotherapy based on the eighth edition TNM. Recursive partitioning analysis (RPA) incorporating pretreatment plasma Epstein-Barr virus (EBV) DNA derived new stage groups. Multivariable analyses to calculate adjusted hazard ratios (AHRs) derived another set of stage groups. Five-year progression-free survival (PFS), overall survival (OS) and cancer-specific survival (CSS) were: Stage I (PFS 100%, OS 90%, CSS 100%), II (PFS 88%, OS 84%, CSS 95%), III (PFS 84%, OS 84%, CSS 90%) and IVA (PFS 71%, OS 75%, CSS 80%) (p < 0.001, p = 0.066 and p = 0.002, respectively). RPA derived four new stages: RPA-I (T1-T4 N0-N2 & EBV DNA <500 copies per mL; PFS 94%, OS 89%, CSS 96%), RPA-II (T1-T4 N0-N2 & EBV DNA ≥500 copies per mL; PFS 80%, OS 83%, CSS 89%), RPA-III (T1-T2 N3; PFS 64%, OS 83%, CSS 83%) and RPA-IVA (T3-T4 N3; PFS 63%, OS 60% and CSS 68%) (all with p < 0.001). AHR using covariate adjustment also yielded a valid classification (I: T1-T2 N0-N2; II: T3-T4 N0-N2 or T1-T2 N3 and III: T3-T4 N3) (all with p < 0.001). However, RPA stages better predicted survival for PS and CSS after bootstrapping replications. Our RPA-based stage groups revealed better survival prediction compared to the eighth edition TNM and the AHR stage groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".