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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,052 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,015 | 0,031 |
| Bibliométrie | 0,010 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».