Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice.
Journal:
Journal of translational medicine
PMID:
40165301
Abstract
BACKGROUND: High-grade gliomas are among the most aggressive and deadly brain tumors, highlighting the critical need for improved prognostic markers and predictive models. Recent studies have identified the expression of IL7R as a significant risk factor that affects the prognosis of patients diagnosed with high-grade gliomas (HGG). This research focuses on investigating the prognostic significance of Interleukin 7 Receptor (IL7R) expression and aims to develop a noninvasive predictive model based on radiomics for HGG.
Authors
Keywords
Adult
Aged
Algorithms
Brain Neoplasms
Female
Glioma
Humans
Interleukin-7 Receptor alpha Subunit
Kaplan-Meier Estimate
Machine Learning
Magnetic Resonance Imaging
Male
Middle Aged
Multivariate Analysis
Neoplasm Grading
Prognosis
Proportional Hazards Models
Radiomics
Receptors, Interleukin-7
ROC Curve
Support Vector Machine