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

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

BackgroundMachine 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 haz...

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Bibliographic Details
Main Authors: Xianxin Qiu, Jing Gao, Jing Yang, Jiyi Hu, Weixu Hu, Lin Kong, Jiade J. Lu
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-10-01
Series:Frontiers in Oncology
Subjects:
high-grade glioma
random survival forest
machine learning
particle beam radiotherapy
predictive analytics
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2020.551420/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2020.551420/full

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