A fully automatic extraction of magnetic resonance image features in glioblastoma patients.

Journal Article (Journal Article)

PURPOSE: Glioblastoma is the most common malignant brain tumor. It is characterized by low median survival time and high survival variability. Survival prognosis for glioblastoma is very important for optimized treatment planning. Imaging features observed in magnetic resonance (MR) images were shown to be a good predictor of survival. However, manual assessment of MR features is time-consuming and can be associated with a high inter-reader variability as well as inaccuracies in the assessment. In response to this limitation, the authors proposed and evaluated a computer algorithm that extracts important MR image features in a fully automatic manner. METHODS: The algorithm first automatically segmented the available volumes into a background region and four tumor regions. Then, it extracted ten features from the segmented MR imaging volumes, some of which were previously indicated as predictive of clinical outcomes. To evaluate the algorithm, the authors compared the extracted features for 73 glioblastoma patients to the reference standard established by manual segmentation of the tumors. RESULTS: The experiments showed that their algorithm was able to extract most of the image features with moderate to high accuracy. High correlation coefficients between the automatically extracted value and reference standard were observed for the tumor location, minor and major axis length as well as tumor volume. Moderately high correlation coefficients were also observed for proportion of enhancing tumor, proportion of necrosis, and thickness of enhancing margin. The correlation coefficients for all these features were statistically significant (p < 0.0001). CONCLUSIONS: The authors proposed and evaluated an algorithm that, given a set of MR volumes of a glioblastoma patient, is able to extract MR image features that correlate well with their reference standard. Future studies will evaluate how well the computer-extracted features predict survival.

Full Text

Duke Authors

Cited Authors

  • Zhang, J; Barboriak, DP; Hobbs, H; Mazurowski, MA

Published Date

  • April 2014

Published In

Volume / Issue

  • 41 / 4

Start / End Page

  • 042301 -

PubMed ID

  • 24694151

Electronic International Standard Serial Number (EISSN)

  • 2473-4209

Digital Object Identifier (DOI)

  • 10.1118/1.4866218


  • eng

Conference Location

  • United States