MétaCan
Menu
Back to cohort
Record W2547989472 · doi:10.1097/pgp.0000000000000345

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

2016· review· en· W2547989472 on OpenAlexaff
Sandra Lee, Marianne S Rose, Vikrant V. Sahasrabuddhe, Rachel Zhao, Máire A. Duggan

Bibliographic record

VenueInternational Journal of Gynecological Pathology · 2016
Typereview
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsCalgary Laboratory ServicesUniversity of CalgaryAlberta Health Services
FundersNational Institutes of Health
KeywordsEndocervixImmunohistochemistryMedicinePathologyCervixAdenocarcinomaBiomarkerMeta-analysisCarcinomaCancerInternal medicineUterine cervixBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.512
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.109
GPT teacher head0.443
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations23
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Gynecological PathologySame topicCervical Cancer and HPV ResearchFrench-language works237,207