Cargando…
Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients
Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In...
Autores principales: | Lamichhane, Bidhan, Daniel, Andy G. S., Lee, John J., Marcus, Daniel S., Shimony, Joshua S., Leuthardt, Eric C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937731/ https://www.ncbi.nlm.nih.gov/pubmed/33692747 http://dx.doi.org/10.3389/fneur.2021.642241 |
Ejemplares similares
-
Glioblastoma induces whole-brain spectral change in resting state fMRI: Associations with clinical comorbidities and overall survival
por: Park, Ki Yun, et al.
Publicado: (2023) -
Predicting survival in glioblastoma with multimodal neuroimaging and machine learning
por: Luckett, Patrick H., et al.
Publicado: (2023) -
Structural gray matter alterations in glioblastoma and high-grade glioma—A potential biomarker of survival
por: Lamichhane, Bidhan, et al.
Publicado: (2023) -
Functional connectivity within glioblastoma impacts overall survival
por: Daniel, Andy G S, et al.
Publicado: (2020) -
Resting state network mapping in individuals using deep learning
por: Luckett, Patrick H., et al.
Publicado: (2023)