Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma

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 decision m...

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Detalles Bibliográficos
Autores principales: Hu, Leland S., Wang, Lujia, Hawkins-Daarud, Andrea, Eschbacher, Jennifer M., Singleton, Kyle W., Jackson, Pamela R., Clark-Swanson, Kamala, Sereduk, Christopher P., Peng, Sen, Wang, Panwen, Wang, Junwen, Baxter, Leslie C., Smith, Kris A., Mazza, Gina L., Stokes, Ashley M., Bendok, Bernard R., Zimmerman, Richard S., Krishna, Chandan, Porter, Alyx B., Mrugala, Maciej M., Hoxworth, Joseph M., Wu, Teresa, Tran, Nhan L., Swanson, Kristin R., Li, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886858/
https://www.ncbi.nlm.nih.gov/pubmed/33594116
http://dx.doi.org/10.1038/s41598-021-83141-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886858/
https://www.ncbi.nlm.nih.gov/pubmed/33594116
http://dx.doi.org/10.1038/s41598-021-83141-z

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