Abstract
Gliomas are the most prevalent primary malignant brain tumors in adults. Until now an accurate and reliable method to predict patient survival time based on medical imaging and meta-information has not been developed [3]. Therefore, the survival time prediction task was introduced to the Multimodal Brain Tumor Segmentation Challenge (BraTS) to facilitate research in survival time prediction.
Here we present our submissions to the BraTS survival challenge based on classical statistical models to which we feed the provided metadata as features. We intentionally ignore the available image information to explore how patient survival can be predicted purely by metadata. We achieve our best accuracy on the validation set using a simple median regression model taking only patient age into account. We suggest using our model as a baseline to benchmark the added predictive value of sophisticated features for survival time prediction.
B. Wiestler and B.H. Menze—Contributed equally as senior authors.
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Acknowledgments
Bjoern Menze, Benedikt Wiestler and Florian Kofler are supported through the SFB 824, subproject B12.
Supported by Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE), GSC 81.
With the support of the Technical University of Munich – Institute for Advanced Study, funded by the German Excellence Initiative.
Johannes C. Paetzold is supported by the Graduate School of Bioengineering, Technical University of Munich.
Daniel Krahulec is supported by MR R&D Clinical Science, Philips Healthcare.
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Kofler, F. et al. (2020). A Baseline for Predicting Glioblastoma Patient Survival Time with Classical Statistical Models and Primitive Features Ignoring Image Information. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_24
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