ArticleNature communications2024
AI-based histopathology image analysis reveals a distinct subset of endometrial cancers.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.International journal of molecular sciences · 2026Review
- Review
- Molecular subtypes and survival patterns of endometrial cancer in a South Indian cohort.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- High-sensitivity Raman spectroscopy for prostate cancer detection and tissue extraction guidance.Biomedical optics express · 2026Article
- Artificial intelligence as a pragmatic adjunct to molecular classification in endometrial cancer.Ewha medical journal · 2026Article
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- AI-assisted histomorphological stratification of endometrial cancer: real-world validation of foundation models for molecular subtyping.NPJ precision oncology · 2026Article
- Article
- Multiscale tumor characterization in histopathology via self-distilled transformers and topology-aware visual encoding.Scientific reports · 2025Article
- Computational pathology in precision oncology: Evolution from task-specific models to foundation models.Chinese medical journal · 2025Review
- Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors.Orphanet journal of rare diseases · 2025Review
- Integrating Multi-Omics in Endometrial Cancer: From Molecular Insights to Clinical Applications.Cells · 2025Review
- Review
- Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model.Abdominal radiology (New York) · 2025Article
- Prediction of molecular subtypes for endometrial cancer based on hierarchical foundation model.Bioinformatics (Oxford, England) · 2025Article
- AI-powered advances in type II endometrial cancer: global trends and African contexts.Frontiers in oncology · 2025Review
- Research Progress of DNA Methylation Markers for Endometrial Carcinoma Diagnosis.Journal of Cancer · 2025Review
- GJFocuser: a Gaussian difference and joint learning-based autofocus method for whole slide imaging.Biomedical optics express · 2025Article
- From organs to algorithms: Redefining cancer classification in the age of artificial intelligence.Clinical and translational science · 2024Review
- Learning generalizable AI models for multi-center histopathology image classification.NPJ precision oncology · 2024Article
Corrections and comments
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Authors and funding
20 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed 'p53abn-like NSMP'), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the 'p53abn-like NSMP' group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study's findings are applicable exclusively to females.
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