Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT.

ABSTRACT:

Purpose

This study aims to explore the potential of non-contrast abdominal CT radiomics and deep learning models in accurately diagnosing fatty liver.

Materials and methods

The study retrospectively enrolled 840 individuals who underwent non-contrast abdominal CT and quantitative CT (QCT) examinations at the First Affiliated Hospital of Zhengzhou University from July 2022 to May 2023. Subsequently, these participants were divided into a training set (n = 539) and a testing set (n = 301) in a 9:5 ratio. The liver fat content measured by experienced radiologists using QCT technology served as the reference standard. The liver images from the non-contrast abdominal CT scans were then segmented as regions of interest (ROI) from which radiomics features were extracted. Two-dimens

SUBMITTER: Zhang H 

PROVIDER: S-EPMC11825062 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature