Cargando…
Optimization of deep learning methods for visualization of tumor heterogeneity and brain tumor grading through digital pathology
BACKGROUND: Variations in prognosis and treatment options for gliomas are dependent on tumor grading. When tissue is available for analysis, grade is established based on histological criteria. However, histopathological diagnosis is not always reliable or straight-forward due to tumor heterogeneity...
Autores principales: | Truong, An Hoai, Sharmanska, Viktoriia, Limbӓck-Stanic, Clara, Grech-Sollars, Matthew |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7648592/ https://www.ncbi.nlm.nih.gov/pubmed/33196039 http://dx.doi.org/10.1093/noajnl/vdaa110 |
Ejemplares similares
-
Droplet digital PCR-based detection of circulating tumor DNA from pediatric high grade and diffuse midline glioma patients
por: Izquierdo, Elisa, et al.
Publicado: (2021) -
Parents’ experiences of postmortem tumor donation for high-grade gliomas: benefits and suggested improvements
por: Robertson, Eden G, et al.
Publicado: (2021) -
Tumor mutational burden predicts survival in patients with low-grade gliomas expressing mutated IDH1
por: Alghamri, Mahmoud S, et al.
Publicado: (2020) -
Transitioning to molecular diagnostics in pediatric high-grade glioma: experiences with the 2016 WHO classification of CNS tumors
por: Baugh, Joshua N, et al.
Publicado: (2021) -
Tumor invasion after treatment of glioblastoma with bevacizumab: radiographic and pathologic correlation in humans and mice
por: de Groot, John F., et al.
Publicado: (2010)