ArticleNPJ precision oncology2026
AI-assisted histomorphological stratification of endometrial cancer: real-world validation of foundation models for molecular subtyping.
Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.International journal of molecular sciences · 2026Review
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Authors and funding
12 authors.
Funding
Abstract
Endometrial cancer (EC) is classified into four molecular subtypes with distinct prognosis and treatment implications. Despite this well-established molecular classification, morpho-molecular correlations remain understudied. Artificial intelligence (AI) enables biomarker prediction from H&E-stained whole-slide images (WSIs). However, real-world validation for EC molecular subtyping is lacking. In this study, we evaluated image-based molecular subtyping in a real-world cohort derived from routine diagnostic cases with heterogeneous image quality. We benchmarked the feature extraction with the CTransPath and UNI foundation models, demonstrating robust results across different scanner hardware and quantified performance variation by additional stain normalization. UNI-based features achieved a mean AUROC of 0.646 for POLEmut (n = 16), 0.700 for MMRd_MSI (n = 79), 0.684 for NSMP (n = 176), and 0.844 for p53abn (n = 18) on external real-world data. We provided human interpretations of subtype-specific morphological features. Our findings may thus lay the foundations of a more reliable framework for personalized treatment stratification based on morphologically informed molecular subtyping.
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Registered trials
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