Prognostic Factors for Overall Survival in Nasopharyngeal Cancer and Implication for TNM Staging by UICC: A Systematic Review of the Literature
Bibliographic record
Abstract
This study aims to identify prognostic factors in nasopharyngeal carcinoma (NPC) to improve the current 8th edition TNM classification. A systematic review of the literature reported between 2013 and 2019 in PubMed, Embase, and Scopus was conducted. Studies were included if (1) original clinical studies, (2) ≥50 NPC patients, and (3) analyses on the association between prognostic factors and overall survival. The data elements of eligible studies were abstracted and analyzed. A level of evidence was synthesized for each suggested change to the TNM staging and prognostic factors. Of 5,595 studies screened, 108 studies (44 studies on anatomical criteria and 64 on non-anatomical factors) were selected. Proposed changes/factors with strong evidence included the upstaging paranasal sinus to T4, defining parotid lymph node as N3, upstaging N-category based on presence of lymph node necrosis, as well as the incorporation of non-TNM factors including EBV-DNA level, primary gross tumor volume (GTV), nodal GTV, neutrophil-lymphocyte ratio, lactate dehydrogenase, C-reactive protein/albumin ratio, platelet count, SUVmax of the primary tumor, and total lesion glycolysis. This systematic review provides a useful summary of suggestions and prognostic factors that potentially improve the current staging system. Further validation studies are warranted to confirm their significance.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".