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View ResearchDiscover how multiparametric MRI combined with machine learning informs on the molecular underpinnings, prognosis, and treatment response in pediatric low-grade gliomas (pLGGs). This comprehensive radiogenomic analysis identifies three immunological clusters, with one 'immune-hot' group linked to poorer prognosis. A radiomic signature predicts immunological profiles with high accuracy, while a clinicoradiomic model predicts progression-free survival and treatment response, enhancing precision medicine in pLGGs.
MRI and Machine Learning in Pediatric Glioma
Using multiparametric MRI and machine learning to understand pediatric low-grade glioma prognosis and treatment response.
Radiogenomic Clustering and Prognosis
Identifying immunological clusters in pediatric glioma with radiogenomics for improved prognosis and treatment insights.
Predicting Glioma Survival with Clinicoradiomics
Clinicoradiomic model predicts progression-free survival and treatment response in pediatric low-grade glioma.
Immune Microenvironment and Glioma Clusters
Study links immune cell profiles in glioma clusters to patient outcomes and potential immunotherapy benefits.
Molecular Pathways in Pediatric Glioma
Investigating germline variants and transcriptomic pathways related to clinicoradiomic risk in pediatric glioma.
Advancing Precision Therapy in Glioma
Combining imaging and molecular data to enhance precision therapy and risk stratification in pediatric low-grade glioma.