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...

Descripció completa

Guardat en:
Dades bibliogràfiques
Publicat a:Neuro Oncol
Autors principals: 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
Idioma:Inglês
Publicat: Oxford University Press 2019
Matèries:
Neuro-Imaging
Accés en línia:https://ncbi.nlm.nih.gov/pmc/articles/PMC6847463/
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/neuonc/noz175.730
Etiquetes: Afegir etiqueta
Sense etiquetes, Sigues el primer a etiquetar aquest registre!

Ítems similars