Adenocarcinoma of the Uterine Cervix: Immunohistochemical Biomarker Expression and Diagnostic Performance
Bibliographic record
Abstract
Immunohistochemistry (IHC) improves the diagnosis of cervical adenocarcinoma but is not adequately studied. The performance of 16 antibodies previously reported as potentially discriminating between some histotypes was investigated in 184 tumors comprised of 12 histotype groups collapsed into 3 categories [47 adenocarcinomas in situ (AIS), 121 probable human papillomavirus-dependent adenocarcinomas (HPVD), and 16 of probable independence (HPVI)]. IHC sections from 5 tissue microarrays were scanned, and 3 pathologists independently reviewed images to assess staining percentages and intensities. Biomarker expression was based on published positive and negative cutoffs and agreement between any 2 pathologists. Differences between the 3 categories in the hierarchical ranking of biomarker positivity were analyzed by Random Forest classification, and between select groups by Unsupervised Hierarchical Clustering. Important category discriminants were combined in logistic regression models and the area under the curve (AUC) computed. Potential group discriminants were terminal cluster biomarkers with a 50% or more difference in positivity. Strong associations occurred between the lower expression of carcinoembryonic antigen and stromal actin in AIS compared with HPVD [AUC=0.70, 95% confidence interval (CI), 0.59-0.80] and in the higher expression of p16 and estrogen receptor in comparison to HPVI (AUC=0.86, 95% CI, 0.73-0.98), and between the higher expression of p16, carcinoembryonic antigen and estrogen receptor in HPVD compared with HPVI (AUC=0.88, 95% CI, 0.77-0.99). Between select groups, 9 biomarkers emerged as potential discriminants. Select IHC biomarkers can discriminate AIS from invasive adenocarcinomas, and invasive adenocarcinomas stratified by human papillomavirus dependency from each other. Independent replication in larger studies is needed, and to confirm discriminants of histotype groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".