Integrating radiomics with clinical data for enhanced prediction of vertebral fracture risk.

ABSTRACT:

Introduction

Osteoporotic vertebral fractures are a major cause of morbidity, disability, and mortality among the elderly. Traditional methods for fracture risk assessment, such as dual-energy X-ray absorptiometry (DXA), may not fully capture the complex factors contributing to fracture risk. This study aims to enhance vertebral fracture risk prediction by integrating radiomics features extracted from computed tomography (CT) scans with clinical data, utilizing advanced machine learning techniques.

Methods

We analyzed CT imaging data and clinical records from 124 patients, extracting a comprehensive set of radiomics features. The dataset included shape, texture, and intensity metrics from segmented vertebrae, alongside clinical variables such as age and DXA T-values. Feature

SUBMITTER: Saravi B 

PROVIDER: S-EPMC11620855 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature