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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Bibliografische gegevens
Gepubliceerd in:Neuro Oncol
Hoofdauteurs: 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
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Oxford University Press 2019
Onderwerpen:
Neuro-Imaging
Online toegang: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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