Prediction of molecular subtypes for endometrial cancer based on hierarchical foundation model.

ABSTRACT:

Motivation

Endometrial cancer is a prevalent gynecological malignancy that requires accurate identification of its molecular subtypes for effective diagnosis and treatment. Four molecular subtypes with different clinical outcomes have been identified: POLE mutation, mismatch repair deficient, p53 abnormal, and no specific molecular profile. However, determining these subtypes typically relies on expensive gene sequencing. To overcome this limitation, we propose a novel method that utilizes hematoxylin and eosin-stained whole slide images to predict endometrial cancer molecular subtypes.

Results

Our approach leverages a hierarchical foundation model as a backbone, fine-tuned from the UNI computational pathology foundation model, to extract tissue embedding from different scal

SUBMITTER: Cui H 

PROVIDER: S-EPMC11878776 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature