Abstract #2888

Comparison of radiomics-based machine learning survival models in predicting prognosis of glioblastoma

Jixin Luan¹, Chuanchen Zhang², Bin Liu¹, Aocai Yang¹, Kuan Lv³, Pianpian Hu³, and Guolin Ma¹

¹China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, ²Department of Radiology, Liaocheng People's Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Liaocheng, China, ³China-Japan Friendship Hospital, Beijing, China

Synopsis

Keywords: Radiomics, Cancer

In this study, we aimed to compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients. The Cox proportional-hazards model (Cox-PH) and SurvivalTree, Random survival forest (RSF), DeepHit, DeepSurv four machine learning models were constructed, and the performance of the models was evaluated using C-index. We found that deep learning algorithms based on radiomics in predicting the overall survival of GBM patients, and the DeepSurv model showed the best predictive ability.

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