Cargando…
Predicting survival in glioblastoma with multimodal neuroimaging and machine learning
PURPOSE: Glioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care. METHODS: GB...
Autores principales: | Luckett, Patrick H., Olufawo, Michael, Lamichhane, Bidhan, Park, Ki Yun, Dierker, Donna, Verastegui, Gabriel Trevino, Yang, Peter, Kim, Albert H., Chheda, Milan G., Snyder, Abraham Z., Shimony, Joshua S., Leuthardt, Eric C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522528/ https://www.ncbi.nlm.nih.gov/pubmed/37668941 http://dx.doi.org/10.1007/s11060-023-04439-8 |
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) -
Structural gray matter alterations in glioblastoma and high-grade glioma—A potential biomarker of survival
por: Lamichhane, Bidhan, et al.
Publicado: (2023) -
Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients
por: Lamichhane, Bidhan, et al.
Publicado: (2021) -
Functional connectivity within glioblastoma impacts overall survival
por: Daniel, Andy G S, et al.
Publicado: (2020) -
Mapping of the Language Network With Deep Learning
por: Luckett, Patrick, et al.
Publicado: (2020)