NIMG-74. RADIOMICS OF TUMOR INVASION 2.0: COMBINING MECHANISTIC TUMOR INVASION MODELS WITH MACHINE LEARNING MODELS TO ACCURATELY PREDICT TUMOR INVASION IN HUMAN GLIOBLASTOMA PATIENTS

In glioblastoma (GBM), contrast enhanced (CE)-MRI delineates bulk tumor with contrast-enhancement but poorly characterizes invasive tumor in the nonenhancing T2W abnormality. There is extensive literature in both machine-learning (ML) and mechanistic mathematical oncology seeking to accurately predi...

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Gepubliceerd in:Neuro Oncol
Hoofdauteurs: Swanson, Kristin R, Gaw, Nathan, Hawkins-Daarud, Andrea, Jackson, Pamela R, Singleton, Kyle W, DeGirolamo, Lauren, Eschbacher, Jennifer, Baxter, Leslie, Smith, Kris, Nakaji, Peter, McGee, Samuel, Clark-Swanson, Kamala, Bendok, Bernard, Dueck, Amylou, Wu, Teresa, Li, Jing, Hu, Leland
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Oxford University Press 2017
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Abstracts
Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC5692897/
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/neuonc/nox168.646
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