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

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 th...

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Main Authors: 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, Ali Nabavizadeh
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-024-55659-z
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https://doi.org/10.1038/s41467-024-55659-z

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