Tissue-based Immunohistochemical Biomarker Expression in Malignant Glandular Lesions of the Uterine Cervix: A Systematic Review
Bibliographic record
Abstract
Literature published between 1975 and 2015 was systematically reviewed to conduct a case-comparator study of tissue based, immunohistochemical biomarker expression among malignant glandular histotypes of the uterine cervix so as to identify differences that could have diagnostic utility. Of the 902 abstracts, 154 articles had a full review, and 52 were included. Biomarker positivity in cases of adenocarcinoma in situ (AIS) were compared with atypical lobular endocervical glandular hyperplasia and invasive histotypes grouped as mucinous, endometrioid, adenosquamous, serous clear cell, minimal deviation-gastric type, and mesonephric carcinomas (7 AIS case-comparators). The invasive histotypes were compared with each other (30 adenocarcinoma case-comparators). Biomarker positivity in all 37 case-comparators was calculated as weighted averages of histotype-specific estimates. Unsupervised hierarchical clustering examined differences in expression and were visualized via heatmaps and dendrograms. Of the 56 biomarkers tested, 1 or more of 15 showed a 50% or more difference in positive expression in 6 (86%) of the AIS and 21 (70%) of the adenocarcinoma case-comparators. There was no data on the comparison of serous clear cell to mesonephric carcinoma. AIS case-comparator biomarkers were HIK1083, alpha SMA, PAX8, VIL1, CEA, p53, p16, and CD10, and only alpha SMA had a difference of 100%. The adenocarcinoma case-comparator biomarkers were CEA, p53, Claudin18, HIK1083, p16, Calretinin, CD10, PR, Chromogranin, MUC6, Vimentin and p63, and none had a difference of 100%. Biomarker expression in the discrimination of AIS from invasive adenocarcinoma, and the invasive histotypes from each other is understudied. One or more of 15 biomarkers could have diagnostic utility.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".