A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistic (Cox Proportional Hazards) for Predicting Progression in High-Grade Glioma after Proton and Carbon Ion Radiotherapy

BACKGROUND: Machine learning (ML) algorithms are increasingly explored in glioma prognostication. Random survival forest (RSF) is a common ML approach in analyzing time-to-event survival data. However, it is controversial which method between RSF and traditional cornerstone method Cox proportional h...

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Detalles Bibliográficos
Autores principales: Qiu, Xianxin, Gao, Jing, Yang, Jing, Hu, Jiyi, Hu, Weixu, Kong, Lin, Lu, Jiade J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662123/
https://www.ncbi.nlm.nih.gov/pubmed/33194609
http://dx.doi.org/10.3389/fonc.2020.551420

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662123/
https://www.ncbi.nlm.nih.gov/pubmed/33194609
http://dx.doi.org/10.3389/fonc.2020.551420

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