Evaluation of human papillomavirus (HPV) prediction using the International Endocervical Adenocarcinoma Criteria and Classification system, compared to p16 immunohistochemistry and HPV RNA in-situ hybridization
Bibliographic record
Abstract
BACKGROUND: The International Endocervical Adenocarcinoma Criteria and Classification (IECC) separated endocervical adenocarcinomas into human papillomavirus (HPV) associated (HPVA) and non-HPV-associated (NHPVA) categories by morphology alone. Our primary objective was to assess the accuracy of HPV prediction by the IECC system compared to p16 immunohistochemistry and HPV RNA in-situ hybridization (RISH). Our secondary goal was to directly compare p16 and HPV RISH concordance. METHODS: Cases were classified by IECC and stained for p16 and HPV RISH on tissue microarray, with discordant p16/HPV RISH cases re-stained on whole tissue sections. Remaining discordant cases (p16/HPV, IECC/p16, IECC/HPV discordances) were re-reviewed by the original pathologists (n = 3) and external expert pathologists (n = 2) blinded to the p16 and HPV RISH results. Final IECC diagnosis was assigned upon independent agreement between all reviewers. RESULTS: One hundred and eleven endocervical adenocarcinomas were classified originally into 94 HPVA and 17 NHPVA cases. p16 and HPV RISH was concordant in 108/111 cases (97%) independent of the IECC. HPV RISH and p16 was concordant with IECC in 103/111 (93%) and 106/111 (95%), respectively. After expert review, concordance improved to 107/111 (96%) for HPV RISH. After review of the eight discordant cases, one remained as HPVA, four were reclassified to NHPVA from HPVA, two were unclassifiable, and one possibly represented a mixed usual and gastric-type adenocarcinoma. CONCLUSIONS: p16 and HPV RISH have excellent concordance in endocervical adenocarcinomas, and IECC can predict HPV status in most cases. Focal apical mitoses and apoptotic debris on original review led to the misclassification of several NHPVA as HPVA.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".