ArticleJournal of personalized medicine2022
Computer-Aided Histopathological Characterisation of Endometriosis Lesions.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Engineering the human endometrium at the intersection of development and reproduction.Nature biomedical engineering · 2026Review
- Emerging Pathways to Non-Invasive Diagnosis in Endometriosis: Integrating Machine Learning, Deep Learning and Multi-Omics Biomarkers.Diagnostics (Basel, Switzerland) · 2026Review
- Matrix Tropism Influences Endometriotic Cell Attachment Patterns.Advanced functional materials · 2025Article
- Artificial intelligence-based tissue segmentation and cell identification in multiplex-stained histological endometriosis sections.Human reproduction (Oxford, England) · 2025Article
- Real world perspectives on endometriosis disease phenotyping through surgery, omics, health data, and artificial intelligence.npj women's health · 2025Review
- Endometriosis in the era of precision medicine and impact on sexual and reproductive health across the lifespan and in diverse populations.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometriosis is a common gynaecological condition characterised by the growth of endometrial tissue outside the uterus and is associated with pain and infertility. Currently, the gold standard for endometriosis diagnosis is laparoscopic excision and histological identification of endometrial epithelial and stromal cells. There is, however, currently no known association between the histological appearance, size, morphology, or subtype of endometriosis and disease prognosis. In this study, we used histopathological software to identify and quantify the number of endometrial epithelial and stromal cells within excised endometriotic lesions and assess the relationship between the cell contents and lesion subtypes. Prior to surgery for suspected endometriosis, patients provided menstrual and abdominal pain and dyspareunia scores. Endometriotic lesions removed during laparoscopic surgery were collected and prepared for immunohistochemistry from 26 patients. Endometrial epithelial and stromal cells were identified with Cytokeratin and CD10 antibodies, respectively. Whole slide sections were digitised and the QuPath software was trained to automatically detect and count epithelial and stromal cells across the whole section. Using this classifier, we identified a significantly larger number of strongly labelled CD10 stromal cells (
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Registered trials
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