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Written and prepared by:
Anahita Fathi Kazerooni, Adam Kraya, Komal S. Rathi, Meen Chul Kim, Arastoo Vossough, Nastaran Khalili, Ariana 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 Foster, Sabine Mueller, Michael J. Fisher, Adam C. Resnick, Ali Nabavizadeh
Discover 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.
Using multiparametric MRI and machine learning to understand pediatric low-grade glioma prognosis and treatment response.
Identifying immunological clusters in pediatric glioma with radiogenomics for improved prognosis and treatment insights.
Clinicoradiomic model predicts progression-free survival and treatment response in pediatric low-grade glioma.
Study links immune cell profiles in glioma clusters to patient outcomes and potential immunotherapy benefits.
Investigating germline variants and transcriptomic pathways related to clinicoradiomic risk in pediatric glioma.
Combining imaging and molecular data to enhance precision therapy and risk stratification in pediatric low-grade glioma.
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