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...
Guardado en:
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: | |
Acceso en línea: | https://doaj.org/article/29a1d68a17774a75aedf2ebb89547a04 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis
por: Lee Curtin, et al.
Publicado: (2021) -
Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients
por: Carolina de la Pinta, et al.
Publicado: (2021) -
Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer
por: Ming Fan, et al.
Publicado: (2020) -
MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies
por: Mario Zanfardino, et al.
Publicado: (2021) -
Integrative Radiogenomics Approach for Risk Assessment of Postoperative and Adjuvant Chemotherapy Benefits for Gastric Cancer Patients
por: Yin Jin, et al.
Publicado: (2021)