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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Detalhes bibliográficos
Publicado no:Neuro Oncol
Principais autores: 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
Formato: Artigo
Idioma:Inglês
Publicado em: Oxford University Press 2019
Assuntos:
Neuro-Imaging
Acesso em linha: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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