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

TL;DR

This study introduces biologically interpretable, automatically computed MRI features based on brain deformation caused by glioblastoma, improving survival prediction across various imaging protocols and post-operative data.

Contribution

The paper presents novel, contrast-adaptive features that measure brain deformation for survival prediction, applicable to pre- and post-operative MR images and robust across scanners.

Findings

Features improve survival prediction accuracy.

Features are applicable to post-operative images.

Features outperform conventional non-imaging biomarkers.

Abstract

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.