Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma.

ABSTRACT:

Background

Accurately predicting the prognosis of patients with high-grade glioma (HGG) is potentially important for treatment. However, the predictive value of images of various magnetic resonance imaging (MRI) sequences for prognosis at different time points is unknown. We established predictive machine learning models of HGG disease progression and recurrence using MRI radiomics and explored the factors influencing prediction accuracy.

Methods

Radiomics features were extracted from T1-weighted (T1WI), contrast-enhanced T1-weighted (CE-T1WI), T2-weighted (T2WI), and fluid-attenuated inversion recovery (FLAIR) images (postoperative radiotherapy planning MRI images) obtained from 162 patients with HGG. The Mann-Whitney U test and least absolute shrinkage and selection operat

SUBMITTER: Li Z 

PROVIDER: S-EPMC9816734 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature