ArticleCell reports. Medicine2025
MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images.
Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.International journal of molecular sciences · 2026Review
- Article
- Review
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Artificial intelligence-driven integration of multi-biofluid omics and clinical phenotype enables stratification of endometrial cancer.Cell reports. Medicine · 2026Article
- Real-world benchmarking and validation of foundation model transformers for endometrial cancer subtyping from histopathology.NPJ precision oncology · 2026Article
- Histopathology Images-Based Deep Learning Prediction of Histological Types in Endometrial Cancer.Cancer medicine · 2026Article
- Real-World Benchmarking and Validation of Foundation Model Transformers for Endometrial Cancer Subtyping from Histopathology.Research square · 2025Article
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review.Journal of clinical medicine · 2025Review
- Real-World Benchmarking and Validation of Foundation Model Transformers for Endometrial Cancer Subtyping from Histopathology.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
23 authors.
Funding
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Abstract
Combining molecular classification with clinicopathologic methods improves risk assessment and chooses therapies for endometrial cancer (EC). Detecting mismatch repair (MMR) deficiencies in EC is crucial for screening Lynch syndrome and identifying immunotherapy candidates. An affordable and accessible tool is urgently needed to determine MMR status in EC patients. We introduce MMRNet, a deep convolutional neural network designed to predict MMR-deficient EC from whole-slide images stained with hematoxylin and eosin. MMRNet demonstrates strong performance, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.897, with a sensitivity of 0.628 and a specificity of 0.949 in internal cross-validation. External validation using three additional datasets results in AUROCs of 0.790, 0.807, and 0.863. Employing a human-machine fusion approach notably improves diagnostic accuracy. MMRNet presents an effective method for identifying EC cases for confirmatory MMR testing and may assist in selecting candidates for immunotherapy.
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