AI-assisted histomorphological stratification of endometrial cancer: real-world validation of foundation models for molecular subtyping

Schwarzmann L, Stöhr R, Emons J, Pöschke P, Seitz K, Rübner M, Beckmann MW, Diederich M, Unser I, Hartmann A, Matek C, Erber R (2026)

Publication Type: Journal article

Publication year: 2026

Journal

npj Precision Oncology Springer Nature

Book Volume: 10

Article Number: 205

DOI: 10.1038/s41698-026-01522-x

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.

Authors with CRIS profile

Laura Schwarzmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Robert Stöhr Institute of Pathology Mareike Diederich Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Irina Unser Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Christian Matek Institute of Pathology Ramona Erber Institute of Pathology

Involved external institutions

Bayerisches Zentrum für Krebsforschung (BZKF) DE Germany (DE)

How to cite

APA:

Schwarzmann, L., Stöhr, R., Emons, J., Pöschke, P., Seitz, K., Rübner, M.,... Erber, R. (2026). AI-assisted histomorphological stratification of endometrial cancer: real-world validation of foundation models for molecular subtyping. npj Precision Oncology, 10. https://doi.org/10.1038/s41698-026-01522-x

MLA:

Schwarzmann, Laura, et al. "AI-assisted histomorphological stratification of endometrial cancer: real-world validation of foundation models for molecular subtyping." npj Precision Oncology 10 (2026).

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