Dual staining for p16/Ki‐67 to detect high‐grade cervical lesions: Results from the Screening Triage Ascertaining Intraepithelial Neoplasia by Immunostain Testing study
Bibliographic record
Abstract
We compared clinical performance of p16/Ki-67 dual-stained cytology and human papillomavirus (HPV) genotyping, via different algorithms-alone, or in combination with cytology-to identify cervical intraepithelial neoplasia grade 2 or worse (CIN2+) and grade 3 or worse (CIN3+) in women referred to as colposcopy. We included 492 cervical specimens (134 normal, 130 CIN1, 99 CIN2, 121 CIN3, 8 cancers) randomly selected from 1158 specimens with valid conventional cytology, HPV (cobas 4800 HPV test) and biopsy results. Dual-stained cytology was retrospectively performed (CINtec PLUS assay) on PreservCyt material; slides were read by a cytologist and confirmed by two pathologists, blinded to cytology, biopsy and genotyping results. Sensitivity and specificity (95% confidence intervals in parentheses) of dual-stained cytology to detect CIN2+ and CIN3+ were compared to other screening tests available for the same women. Positivity rate for dual-stained cytology increased with histological severity: 30.6% in normal, 41.5% in CIN1, 72.7% in CIN2, 86.8% in CIN3 and 87.5% in cancer. Dual-stained cytology alone had lower sensitivity than HPV testing for CIN2+ [80.7% (75.0-85.6) vs 89.9% (85.3-93.5)] and CIN3+ [86.8% (79.7-92.1) vs 92.3% (86.2-96.2)]. However, corresponding specificity values were higher [64.0% (57.9-69.8) vs 56.1% (49.8-62.1) for CIN2+; 54.0% (48.7-59.2) vs 44.4% (39.2-49.6) for CIN3+]. Combining dual-stained cytology with an ASC-US abnormality threshold decreased specificity to 31.4% (25.9-37.4) for CIN2+ and 24.2% (19.9-29.0) for CIN3+. The corresponding values considering low squamous intraepithelial lesion threshold values were 42.8% (36.8-49.0) and 35.0% (30.1-40.1). Dual-stained cytology and HPV testing exhibited similar performance, although the former improved the specificity by 7.9% and 9.6% for CIN2+ and CIN3+, respectively.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".