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Front. Comput. Neurosci. | doi: 10.3389/fncom.2019.00073

Robustness of Radiomics for Survival Prediction of Brain Tumor Patients depending on Resection Status

 Leon Weninger1*, Christoph Haarburger1 and Dorit Merhof1
  • 1RWTH Aachen University, Germany

Prediction of overall survival based on multimodal MRI of brain tumor patients is a difficult problem.
Although survival also depends on factors that cannot be assessed via preoperative MRI such as surgical outcome, encouraging results for MRI-based survival analysis have been published for different datasets.
We assess if and how established radiomic approaches as well as novel methods can predict overall survival of brain tumor patients on the BraTS challenge dataset.
This dataset consists of multimodal preoperative images of 211 glioblastoma patients from several institutions with reported resection status and known survival.
In the official challenge setting, only patients with a reported gross total resection are taken into account.

We therefore evaluated previously published methods as well as different machine learning approaches on the BraTS dataset.
For different types of resection status, these approaches are compared to a baseline, a linear regression on patient age only.
This naive approach won the 3rd place out of 26 participants in the BraTS survival prediction challenge 2018.

Previously published radiomic signatures show significant correlations and predictiveness to patient survival for patients with a reported subtotal resection.
However, for patients with reported gross total resection, none of the evaluated approaches was able to outperform the age-only baseline in a cross-validation setting, explaining the poor performance of approaches based on radiomics in the BraTS challenge 2018.

Keywords: BRATS, Survival Prediction, Radiomics, machine learning, brain tumor, feature selection

Received: 25 Apr 2019; Accepted: 09 Oct 2019.

Copyright: © 2019 Weninger, Haarburger and Merhof. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Leon Weninger, RWTH Aachen University, Aachen, Germany, leon.weninger@lfb.rwth-aachen.de