NIMG-61. USING MACHINE LEARNING TO BUILD RADIOMICS MODELS THAT DISTINGUISH REGIONS OF GLIOBLASTOMA RECURRENCE VS TUMOR PROGRESSION ON MRI

Recurrent glioblastoma is challenging to distinguish from so called “treatment effect” on routine clinical imaging. Further, within tumor heterogeneity reveals that some regions can be histologically dominated by tumor progression whilst others can be dominated by secondary effects of treatment resp...

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Bibliografiske detaljer
Udgivet i:Neuro Oncol
Main Authors: Yoon, Hyunsoo, Hawkins-Daarud, Andrea, Save, Akshay, Singleton, Kyle, Clark-Swanson, Kamala, Wang, Lujia, Bendok, Bernard, Mrugala, Maciej, Wu, Teresa, Bruce, Jeffrey, Hu, Leland, Li, Jing, Canoll, Peter D, Swanson, Kristin
Format: Artigo
Sprog:Inglês
Udgivet: Oxford University Press 2019
Fag:
Neuro-Imaging
Online adgang:https://ncbi.nlm.nih.gov/pmc/articles/PMC6847463/
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/neuonc/noz175.730
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