Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma

Abstract Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical d...

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Autores principales:	Leland S. Hu, Lujia Wang, Andrea Hawkins-Daarud, Jennifer M. Eschbacher, Kyle W. Singleton, Pamela R. Jackson, Kamala Clark-Swanson, Christopher P. Sereduk, Sen Peng, Panwen Wang, Junwen Wang, Leslie C. Baxter, Kris A. Smith, Gina L. Mazza, Ashley M. Stokes, Bernard R. Bendok, Richard S. Zimmerman, Chandan Krishna, Alyx B. Porter, Maciej M. Mrugala, Joseph M. Hoxworth, Teresa Wu, Nhan L. Tran, Kristin R. Swanson, Jing Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/29a1d68a17774a75aedf2ebb89547a04
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Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma

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