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

Anahita Fathi Kazerooni (), Adam Kraya, Komal S. Rathi, Meen Chul Kim, Arastoo Vossough, Nastaran Khalili, Ariana M. Familiar, Deep Gandhi, Neda Khalili, Varun Kesherwani, Debanjan Haldar, Hannah Anderson, Run Jin, Aria Mahtabfar, Sina Bagheri, Yiran Guo, Qi Li, Xiaoyan Huang, Yuankun Zhu, Alex Sickler, Matthew R. Lueder, Saksham Phul, Mateusz Koptyra, Phillip B. Storm, Jeffrey B. Ware, Yuanquan Song, Christos Davatzikos, Jessica B. Foster, Sabine Mueller, Michael J. Fisher, Adam C. Resnick and Ali Nabavizadeh ()
Additional contact information
Anahita Fathi Kazerooni: The Children’s Hospital of Philadelphia
Adam Kraya: The Children’s Hospital of Philadelphia
Komal S. Rathi: The Children’s Hospital of Philadelphia
Meen Chul Kim: The Children’s Hospital of Philadelphia
Arastoo Vossough: The Children’s Hospital of Philadelphia
Nastaran Khalili: The Children’s Hospital of Philadelphia
Ariana M. Familiar: The Children’s Hospital of Philadelphia
Deep Gandhi: The Children’s Hospital of Philadelphia
Neda Khalili: The Children’s Hospital of Philadelphia
Varun Kesherwani: The Children’s Hospital of Philadelphia
Debanjan Haldar: The Children’s Hospital of Philadelphia
Hannah Anderson: The Children’s Hospital of Philadelphia
Run Jin: The Children’s Hospital of Philadelphia
Aria Mahtabfar: Thomas Jefferson University
Sina Bagheri: The Children’s Hospital of Philadelphia
Yiran Guo: The Children’s Hospital of Philadelphia
Qi Li: The Children’s Hospital of Philadelphia
Xiaoyan Huang: The Children’s Hospital of Philadelphia
Yuankun Zhu: The Children’s Hospital of Philadelphia
Alex Sickler: The Children’s Hospital of Philadelphia
Matthew R. Lueder: The Children’s Hospital of Philadelphia
Saksham Phul: The Children’s Hospital of Philadelphia
Mateusz Koptyra: The Children’s Hospital of Philadelphia
Phillip B. Storm: The Children’s Hospital of Philadelphia
Jeffrey B. Ware: University of Pennsylvania
Yuanquan Song: The Children’s Hospital of Philadelphia
Christos Davatzikos: University of Pennsylvania
Jessica B. Foster: The Children’s Hospital of Philadelphia
Sabine Mueller: University of California San Francisco
Michael J. Fisher: The Children’s Hospital of Philadelphia
Adam C. Resnick: The Children’s Hospital of Philadelphia
Ali Nabavizadeh: The Children’s Hospital of Philadelphia

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-024-55659-z

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