Recognition of nonkeratinizing morphology in oropharyngeal squamous cell carcinoma – a prospective cohort and interobserver variability study*
Bibliographic record
Abstract
Lewis J S Jr, Khan R A, Masand R P, Chernock R D, Zhang Q, Al‐Naief N S, Muller S, McHugh J B, Prasad M L, Brandwein‐Gensler M, Perez‐Ordonez B & El‐Mofty S K (2012) Histopathology 60, 427–436 Recognition of nonkeratinizing morphology in oropharyngeal squamous cell carcinoma – a prospective cohort and interobserver variability study Aims: Nonkeratinizing morphology in oropharyngeal squamous cell carcinoma (NKSCC) strongly correlates with human papillomavirus and p16 status, but as a unique diagnostic entity is not widely recognized by pathologists. We sought to prospectively examine the performance of a new histological typing system during 1 year of routine clinical practice (Aim 1) and also its reproducibility amongst six head and neck pathologists using a 40 case test set (Aim 2). Methods and Results: The three histological types were: Type 1 (keratinizing), Type 2 (nonkeratinizing with maturation) and Type 3 (nonkeratinizing). For Aim 1, there were 85 cases. p16 immunohistochemistry was positive in five of the 18 (27.8%) cases classified as Type 1, 18 of the 19 (94.7%) as Type 2, and 47 of the 48 (97.9%) as Type 3. For Aim 2, agreement among pathologists on the test cases was best for types 1 and 3 (kappa values 0.62 and 0.56; P < 0.0001) and lowest for type 2 (kappa 0.35; P < 0.0001). All 21 cases classified as NK SCC (type 3) by any of the reviewers was p16 positive. Conclusions: Pathologists can recognize NK SCC with good agreement, and when a pathologist classifies a tumour as NK SCC, this reliably predicts p16 positivity.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".