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

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 anatomical-functional 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 obt

SUBMITTER: Palsson S 

PROVIDER: S-EPMC9671967 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature