Development and validation of a machine learning-based pathomics nomogram for prognostic prediction in endometrial cancer.

ABSTRACT:

Introduction

Endometrial cancer (EC) is a common gynecologic malignancy with increasing incidence and notable molecular heterogeneity. This study aimed to develop a non-invasive, cost-effective prognostic model using pathomics features extracted from histopathological images.

Methods

We retrospectively analyzed hematoxylin and eosin (H&E)-stained whole slide images (WSIs) and clinical data from 514 EC patients in the TCGA-UCEC cohort. Tumor regions were manually annotated, preprocessed, and tiled into patches. Using CellProfiler, 728 high-dimensional quantitative features were extracted per case. Feature selection was performed through univariate Cox regression followed by LASSO regression to build a prognostic risk score. A nomogram integrating pathomics and clinical variab

SUBMITTER: Han X 

PROVIDER: S-EPMC12779805 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature