ArticleCancers2024
Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.International journal of molecular sciences · 2026Review
- Artificial intelligence as a pragmatic adjunct to molecular classification in endometrial cancer.Ewha medical journal · 2026Article
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- Precision oncology in gynecologic cancers: molecular taxonomy, biomarker-guided therapeutics, and the challenge of therapeutic resistance.Frontiers in oncology · 2026Review
- Novel transcriptomic alterations in poorly differentiated endometrial carcinomas: evidence from South African women.Frontiers in oncology · 2026Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review.Journal of clinical medicine · 2025Review
- Hybrid ResNet-ViT Framework for Endometrial Lesion Analysis: A Comparative Study of MRI and CT in Endometrial Cancer Classification.International journal of women's health · 2025Article
- Enhancing clinical decision-making in endometrial cancer through deep learning technology: A review of current research.Digital healthReview
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly for gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.