Distinguishing between endometrial atypical hyperplasia/endometrial intraepithelial neoplasia (EAH/EIN) and grade 1 endometrial endometrioid carcinoma (EEC) requires the evaluation of gland-to-stromal ratio, presence of stromal invasion, extent of epithelial proliferation and nuclear alterations. In small biopsies, stromal invasion may not always be sampled, so other features become more important. However, assessing of some of these features may be subjective. Digital analysis improves diagnostic uniformity, and when combined with CD10 immunostain, it can potentially become a useful objective parameter. Endometrial biopsies with a diagnosis of EAH/EIN or EEC matched were retrieved with subsequent hysterectomy for reference diagnosis. CD10 immunohistochemistry was applied to the biopsies, followed by scanning and annotation. Pixel-accurate stromal percentages were deduced from digitised whole-slide images from a test cohort. An optimal stromal percentage cut-off, thus gland-to-stroma ratio, was extrapolated from the receiver operating characteristic curve. A separate cohort was used to validate the diagnostic performance of the determined gland-to-stroma cut-off. Seventy endometrial biopsies were included in the test cohort, comprising 48 grade 1 EECs and 22 EAH/EINs. The mean stromal percentage was 15.69% for EEC and 33.65% for EAH/EIN (all endometrial tissue annotated/analysed) and 14.77% for EEC and 31.88% for EAH/EIN (only lesional tissue annotated/analysed). The corresponding gland-to-stroma ratio was 5:1 for EEC and 2:1 for EAH/EIN. The areas under curve were 0.758 (p=0.001) (all endometrial tissue) and 0.761 (p=0.001) (only lesional tissue), (p=0.001). In the validation cohort, a cut-off of 30% CD10-stained stroma (7:3 gland-to-stroma ratio) was superior in diagnostic performance than the H&E diagnosis (p=0.042). Evaluation of gland-to-stroma ratio using CD10 immunostain and digital image analysis is a robust and objective method for distinguishing between grade 1 EEC and EAH/EIN in small biopsies. A cut-off >7:3 is highly indicative of EEC.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.pathol.2024.11.012DOI Listing

Publication Analysis

Top Keywords

gland-to-stroma ratio
20
digital image
8
image analysis
8
ratio cd10
8
endometrioid carcinoma
8
stromal invasion
8
small biopsies
8
cd10 immunostain
8
endometrial biopsies
8
test cohort
8

Similar Publications

Distinguishing between endometrial atypical hyperplasia/endometrial intraepithelial neoplasia (EAH/EIN) and grade 1 endometrial endometrioid carcinoma (EEC) requires the evaluation of gland-to-stromal ratio, presence of stromal invasion, extent of epithelial proliferation and nuclear alterations. In small biopsies, stromal invasion may not always be sampled, so other features become more important. However, assessing of some of these features may be subjective.

View Article and Find Full Text PDF

Phthalates, synthetic chemicals widely utilized as plasticizers and stabilizers in various consumer products, present a significant concern due to their persistent presence in daily human life. While past research predominantly focused on individual phthalates, real-life human exposure typically encompasses complex mixtures of these compounds. The cumulative effects of prolonged exposure to phthalate mixtures on uterine health remain poorly understood.

View Article and Find Full Text PDF

Endometrial hyperplasia (EH) is a pathologic condition of the uterus with increased endometrial gland to stroma ratio compared to normal cyclic uterine proliferation. In domestic animals, EH often involves cystic distension of proliferating endometrial glands and may be concurrent with pyometra. In large captive nondomestic felids, an association between EH and pyometra is common; however, detailed species differences between the histological uterine findings in lions () and tigers () and clinical manifestations have yet to be described.

View Article and Find Full Text PDF

Diagnosis of endometrium hyperplasia and screening of endometrial intraepithelial neoplasia in histopathological images using a global-to-local multi-scale convolutional neural network.

Comput Methods Programs Biomed

June 2022

Xi'an Key Lab of Radiomics and Intelligent Perception, School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710069, China. Electronic address:

Article Synopsis
  • Endometrial hyperplasia (EH) can lead to endometrial cancer (EC), making accurate diagnosis crucial, especially differentiating atypical EH from non-atypical forms.
  • A new diagnostic method called G2LNet combines global and local image analysis to better identify endometrial lesions using advanced machine learning techniques on histological images.
  • The G2LNet demonstrated high effectiveness, achieving 97.01% accuracy in diagnosing EH and a 0.9902 AUC for screening atypical EH (EIN) based on extensive testing with various endometrial specimens.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!