Cargando…
The role of deep learning‐based survival model in improving survival prediction of patients with glioblastoma
This retrospective study has been conducted to validate the performance of deep learning‐based survival models in glioblastoma (GBM) patients alongside the Cox proportional hazards model (CoxPH) and the random survival forest (RSF). Furthermore, the effect of hyperparameters optimization methods on...
Autores principales: | Moradmand, Hajar, Aghamiri, Seyed Mahmoud Reza, Ghaderi, Reza, Emami, Hamid |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8525162/ https://www.ncbi.nlm.nih.gov/pubmed/34453413 http://dx.doi.org/10.1002/cam4.4230 |
Ejemplares similares
-
Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma
por: Moradmand, Hajar, et al.
Publicado: (2019) -
Increased LGALS3 expression independently predicts shorter overall survival in patients with the proneural subtype of glioblastoma
por: He, Xia, et al.
Publicado: (2019) -
Improving survival prediction for melanoma
por: Artomov, Mykyta
Publicado: (2019) -
Similar overall survival with reduced vs. standard dose bevacizumab monotherapy in progressive glioblastoma
por: Gleeson, Jack Patrick, et al.
Publicado: (2019) -
Effect of marital status on survival in glioblastoma multiforme by demographics, education, economic factors, and insurance status
por: Xie, Jun‐Chao, et al.
Publicado: (2018)