Tissue-based Immunohistochemical Biomarker Accuracy in the Diagnosis of Malignant Glandular Lesions of the Uterine Cervix: A Systematic Review of the Literature and Meta-Analysis
Bibliographic record
Abstract
Immunohistochemistry is widely used to support a pathology diagnosis of cervical adenocarcinoma despite the absence of a systematic review and meta-analysis of the published data. This systematic review and meta-analysis was performed to investigate the sensitivity and specificity of immunohistochemistry biomarkers in the tissue-based diagnosis of cervical adenocarcinoma histotypes compared with normal endocervix and benign glandular lesions. The systematic review and meta-analysis used a PICOT framework and QUADAS-2 to evaluate the quality of included studies. The literature search spanned 40 years and ended June 30, 2015. Abstracts of identified records were independently screened by 2 of the authors who then conducted a full-text review of selected articles. Sensitivity and specificity of immunohistochemistry expression in malignant glandular lesions of the cervix classified per WHO 2003 compared with 5 benign comparators (normal/benign endocervix, and benign endocervical, endometrioid, gastric, and mesonephric lesions) were calculated. Of 902 abstracts screened, 154 articles were selected for full review. Twenty-five articles with results for 36 biomarkers were included. The only biomarker with enough studies for a meta-analysis was p16 and the definition of positive p16 staining among them was variable. Nevertheless, any positive p16 expression was sensitive, ranging from 0.94 to 0.98 with narrow confidence intervals (CIs), for adenocarcinoma in situ (AIS) and mucinous adenocarcinomas in comparison with normal/benign endocervix and benign endocervical and endometrioid lesions. Specificity for AIS and mucinous adenocarcinomas was also high with narrow CIs compared with benign endocervical lesions. The specificity was high for AIS, 0.99 (0.24, 1.0), and mucinous adenocarcinoma, 0.95 (0.52, 1.0), compared with normal/benign endocervix but with wider CIs, and low with very wide CIs compared with benign endometrioid lesions: 0.31 (0.00, 0.99) and 0.34 (0.00, 0.99), respectively. Results from single studies showed that p16, p16/Ki67 dual stain, ProExC, CEA, ESA, HIK1083, Claudin 18, and ER loss in perilesional stromal cells were useful with high (≥0.75) sensitivity and specificity estimates in ≥1 malignant versus benign comparisons. None of the biomarkers had highly useful sensitivity and specificity estimates for AIS, mucinous adenocarcinomas, or minimal deviation adenocarcinoma/gastric adenocarcinoma compared with benign gastric or mesonephric lesions or for mesonephric carcinoma compared with normal/benign endocervix, benign endocervical, endometrial, or mesonephric lesions. Any expression of p16 supports a diagnosis of AIS and mucinous adenocarcinomas in comparison with normal/benign endocervix and benign endocervical lesions. The majority of studies did not separate mosaic/focal p16 staining from diffuse staining as a distinct pattern of p16 overexpression and this may have contributed to the poor performance of p16 in distinguishing AIS and mucinous adenocarcinomas from benign endometrioid lesions. Single studies support further investigation of 8 additional biomarkers that have highly useful sensitivity and specificity estimates for ≥1 malignant glandular lesions compared with ≥1 of the 5 benign comparators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".