Computed tomography enterography radiomics and machine learning for identification of Crohn's disease.

ABSTRACT:

Background

Crohn's disease is a severe chronic and relapsing inflammatory bowel disease. Although contrast-enhanced computed tomography enterography is commonly used to evaluate crohn's disease, its imaging findings are often nonspecific and can overlap with other bowel diseases. Recent studies have explored the application of radiomics-based machine learning algorithms to aid in the diagnosis of medical images. This study aims to develop a non-invasive method for detecting bowel lesions associated with Crohn's disease using CT enterography radiomics and machine learning algorithms.

Methods

A total of 139 patients with pathologically confirmed Crohn's disease were retrospectively enrolled in this study. Radiomics features were extracted from both arterial- and venous-phase C

SUBMITTER: Shi Q 

PROVIDER: S-EPMC11542238 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature