  Survival prediction models can potentially be used to guide treatment of
glioblastoma patients. However, currently available MR imaging biomarkers
holding prognostic information are often challenging to interpret, have
difficulties generalizing across data acquisitions, or are only applicable to
pre-operative MR data. In this paper we aim to address these issues by
introducing novel imaging features that can be automatically computed from MR
images and fed into machine learning models to predict patient survival. The
features we propose have a direct biological interpretation: They measure the
deformation caused by the tumor on the surrounding brain structures, comparing
the shape of various structures in the patient's brain to their expected shape
in healthy individuals. To obtain the required segmentations, we use an
automatic method that is contrast-adaptive and robust to missing modalities,
making the features generalizable across scanners and imaging protocols. Since
the features we propose do not depend on characteristics of the tumor region
itself, they are also applicable to post-operative images, which have been much
less studied in the context of survival prediction. Using experiments involving
both pre- and post-operative data, we show that the proposed features carry
prognostic value in terms of overall- and progression-free survival, over and
above that of conventional non-imaging features.
