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Survival prediction models can potentially be used to guide treatment ofglioblastoma patients. However, currently available MR imaging biomarkersholding prognostic information are often challenging to interpret, havedifficulties generalizing across data acquisitions, or are only applicable topre-operative MR data. In this paper we aim to address these issues byintroducing novel imaging features that can be automatically computed from MRimages and fed into machine learning models to predict patient survival. Thefeatures we propose have a direct biological interpretation: They measure thedeformation caused by the tumor on the surrounding brain structures, comparingthe shape of various structures in the patient's brain to their expected shapein healthy individuals. To obtain the required segmentations, we use anautomatic method that is contrast-adaptive and robust to missing modalities,making the features generalizable across scanners and imaging protocols. Sincethe features we propose do not depend on characteristics of the tumor regionitself, they are also applicable to post-operative images, which have been muchless studied in the context of survival prediction. Using experiments involvingboth pre- and post-operative data, we show that the proposed features carryprognostic value in terms of overall- and progression-free survival, over andabove that of conventional non-imaging features.

Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images