CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study.

ABSTRACT:

Objectives

This study aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing of malignancy and benignity specifically in endometrial cancer (EC) patients.

Methods

A total of 83 EC patients from two centers, including 46 with malignant and 37 with benign conditions, were included, with data split into a training set (n = 59) and a testing set (n = 24). The regions of interest (ROIs) were manually segmented from pre-surgical CT scans, and 1132 radiomic features were extracted from the pre-surgical CT scans using Pyradiomics. Six explainable machine learning (ML) modeling algorithms were implemented, respectively, for determining the optimal radiomics pipeline. The diagnostic performance of the radiomic model was evaluated b

SUBMITTER: Zhang T 

PROVIDER: S-EPMC12584457 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature