October 30, 2020Open Access

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

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Abstract

In a relatively small dataset with HGG patients treated with PBRT, CPH outperformed RSF for predicting tumor progression. A comprehensive criterion with accuracy, precision, and interpretability is recommended in evaluating ML prognostication approaches for clinical deployment.

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Qiu et al. (Fri,) studied this question.

synapsesocial.com/papers/69d80ac0fc5937d393ae2c48 https://doi.org/https://doi.org/10.3389/fonc.2020.551420

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