Integrative models of histopathological images and multi-omics data predict prognosis in endometrial carcinoma.

ABSTRACT:

Objective

This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging.

Methods

The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set.

Results

Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathol

SUBMITTER: Li Y 

PROVIDER: S-EPMC10424667 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature