Prognostic value and cost benefit of HPV testing for oropharyngeal cancer patients
Bibliographic record
Abstract
OBJECTIVES: High-risk human papillomavirus (HR-HPV) can cause oropharyngeal squamous cell carcinoma (OpSCC). The revised 8th edition of the AJCC Staging Manual now stages OpSCC by incorporating p16 immunohistochemistry (IHC), the surrogate marker for HPV status. This study assessed the prognostic values of p16 and HPV markers. METHODS: We identified 244 OpSCC patients diagnosed between 2000 and 2008 from the British Columbia Cancer Registry with enough tissue to conduct experiments. Formalin-fixed, paraffin-embedded tissue sections were stained for p16 IHC, RNA in situ hybridization (ISH) HPV 16 and 18, and DNA ISH HR-HPV. Electronic charts were reviewed to collect clinical and outcome data. Combined positive RNA and/or DNA ISH was used to denote HPV status. RESULTS: Human papillomavirus was positive among 77.9% of samples. Using HPV as the benchmark, p16 IHC had high sensitivity (90.5%), but low specificity (68.5%). Distinct subgroups of patients were identified by sequential separation of p16 then HPV status. Among both p16-positive and p16-negative groups, HPV-positive patients were younger, more males, and had better clinical outcomes, especially 5-year overall survival. We further evaluated the technical costs associated with HPV testing. CONCLUSION: Human papillomavirus is more prognostic than p16 for OpSCC. Clinical laboratories can adopt HPV RNA ISH for routine analysis.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".