Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.

ABSTRACT: Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and suitability for immunotherapy has the potential to improve clinical management and outcomes. Here, we present a radiogenomic analysis of pLGGs, integrating MRI and RNA sequencing data. We identify three immunologically distinct clusters, with one group characterized by increased immune activity and poorer prognosis, indicating potential benefit from immunotherapies. We develop a radiomic signature that predicts these immune profiles with over 80% accuracy. Furthermore, our clinicoradiomic model predicts progression-free survival and correl

SUBMITTER: Fathi Kazerooni A 

PROVIDER: S-EPMC11697432 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature