Document Type
Article
Publication Date
1-2-2025
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 correlates with treatment response. We also identify genetic variants and transcriptomic pathways associated with progression risk, highlighting links to tumor growth and immune response. This radiogenomic study in pLGGs provides a framework for the identification of high-risk patients who may benefit from targeted therapies.
Recommended Citation
Fathi Kazerooni, Anahita; Kraya, Adam; Rathi, Komal; Kim, Meen Chul; Vossough, Arastoo; Khalili, Nastaran; Familiar, Ariana; Gandhi, Deep; Khalili, Neda; Kesherwani, Varun; Haldar, Debanjan; Anderson, Hannah; Jin, Run; Mahtabfar, Aria; Bagheri, Sina; Guo, Yiran; Li, Qi; Huang, Xiaoyan; Zhu, Yuankun; Sickler, Alex; Lueder, Matthew R; Phul, Saksham; Koptyra, Mateusz; Storm, Phillip; Ware, Jeffrey; Song, Yuanquan; Davatzikos, Christos; Foster, Jessica; Mueller, Sabine; Fisher, Michael J; Resnick, Adam; and Nabavizadeh, Ali, "Multiparametric MRI Along With Machine Learning Predicts Prognosis and Treatment Response in Pediatric Low-Grade Glioma" (2025). Department of Neurosurgery Faculty Papers. Paper 245.
https://jdc.jefferson.edu/neurosurgeryfp/245
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Peer Review File.pdf (3563 kB)
Description of Additional Supplementary Files.pdf (83 kB)
Supplementary Data 1.xlsx (12 kB)
Supplementary Data 2.xlsx (29 kB)
Reporting Summary.pdf (3333 kB)
PubMed ID
39747214
Language
English
Included in
Artificial Intelligence and Robotics Commons, Diagnosis Commons, Neoplasms Commons, Nervous System Diseases Commons, Neurosurgery Commons
Comments
This article is the author's final published version in Nature Communications, Volume 16, Issue 1, 2025, Article number 340.
The published version is available at https://doi.org/10.1038/s41467-024-55659-z.
Copyright © The Author(s) 2025