Computer-extracted MR imaging features are associated with survival in glioblastoma patients.

Journal Article (Journal Article;Multicenter Study)

Automatic survival prognosis in glioblastoma (GBM) could result in improved treatment planning for the patient. The purpose of this research is to investigate the association of survival in GBM patients with tumor features in pre-operative magnetic resonance (MR) images assessed using a fully automatic computer algorithm. MR imaging data for 68 patients from two US institutions were used in this study. The images were obtained from the Cancer Imaging Archive. A fully automatic computer vision algorithm was applied to segment the images and extract eight imaging features from the MRI studies. The features included tumor side, proportion of enhancing tumor, proportion of necrosis, T1/FLAIR ratio, major axis length, minor axis length, tumor volume, and thickness of enhancing margin. We constructed a multivariate Cox proportional hazards regression model and used a likelihood ratio test to establish whether the imaging features are prognostic of survival. We also evaluated the individual prognostic value of each feature through multivariate analysis using the multivariate Cox model and univariate analysis using univariate Cox models for each feature. We found that the automatically extracted imaging features were predictive of survival (p = 0.031). Multivariate analysis of individual features showed that two individual features were predictive of survival: proportion of enhancing tumor (p = 0.013), and major axis length (p = 0.026). Univariate analysis indicated the same two features as significant (p = 0.021, and p = 0.017 respectively). We conclude that computer-extracted MR imaging features can be used for survival prognosis in GBM patients.

Full Text

Duke Authors

Cited Authors

  • Mazurowski, MA; Zhang, J; Peters, KB; Hobbs, H

Published Date

  • December 2014

Published In

Volume / Issue

  • 120 / 3

Start / End Page

  • 483 - 488

PubMed ID

  • 25151504

Electronic International Standard Serial Number (EISSN)

  • 1573-7373

Digital Object Identifier (DOI)

  • 10.1007/s11060-014-1580-5


  • eng

Conference Location

  • United States