Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery

Background: To develop machine-learning based models to predict the progression-free survival (PFS) and overall survival (OS) in patients with gliomas and explore the effect of different feature selection methods on the prediction. Methods: We included 505 patients (training cohort, n = 354; validat...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Bin, Yan, Jing, Chen, Weiqi, Dong, Yuhao, Zhang, Lu, Mo, Xiaokai, Chen, Qiuying, Cheng, Jingliang, Liu, Xianzhi, Wang, Weiwei, Zhang, Zhenyu, Zhang, Shuixing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2021
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890310/
https://www.ncbi.nlm.nih.gov/pubmed/33613747
http://dx.doi.org/10.7150/jca.52183

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890310/
https://www.ncbi.nlm.nih.gov/pubmed/33613747
http://dx.doi.org/10.7150/jca.52183

Ejemplares similares